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WO1998024065A1 - Construction d'images tridimensionnelles a partir de balayages bidimensionnels - Google Patents

Construction d'images tridimensionnelles a partir de balayages bidimensionnels Download PDF

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Publication number
WO1998024065A1
WO1998024065A1 PCT/GB1997/003250 GB9703250W WO9824065A1 WO 1998024065 A1 WO1998024065 A1 WO 1998024065A1 GB 9703250 W GB9703250 W GB 9703250W WO 9824065 A1 WO9824065 A1 WO 9824065A1
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WO
WIPO (PCT)
Prior art keywords
image data
data slices
image
grid
scanning
Prior art date
Application number
PCT/GB1997/003250
Other languages
English (en)
Inventor
Christopher Paul Allott
Christopher David Barry
Philip Anthony Arundel
Nigel William John
Paul Maxwell Mellor
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Zeneca Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zeneca Limited filed Critical Zeneca Limited
Priority to EP97945956A priority Critical patent/EP1008111A1/fr
Priority to JP52443198A priority patent/JP2001504378A/ja
Priority to IL13005697A priority patent/IL130056A0/xx
Priority to CA002269323A priority patent/CA2269323A1/fr
Priority to AU51279/98A priority patent/AU5127998A/en
Publication of WO1998024065A1 publication Critical patent/WO1998024065A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/52017—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 particularly adapted to short-range imaging
    • G01S7/52085—Details related to the ultrasound signal acquisition, e.g. scan sequences
    • G01S7/52087—Details related to the ultrasound signal acquisition, e.g. scan sequences using synchronization techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G01S15/8906—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques
    • G01S15/8993—Three dimensional imaging systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/48—Diagnostic techniques
    • A61B8/483—Diagnostic techniques involving the acquisition of a 3D volume of data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G01S15/8906—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques
    • G01S15/8995—Combining images from different aspect angles, e.g. spatial compounding

Definitions

  • the present invention relates to methods of and apparatus for 3D imaging or diagnosing based on 2D scans .
  • the transducer may be advanced with a stepping motor (Franceschi et al . 1992; Hell et al . 1995; Moskalik et al . 1995; Vogel et al . 1995), by a freehand sweep (Geiser et al . 1982; Gardener et al . 1991; Kelly et al . 1994; King et al . 1990; Moritz et al.1980; Nelson et al . 1996; Riccabona, et al 1995 ), or m intravascular studies, by timed pull-back of the catheter ( von Birgelen et al . 1995; Mmtz et al .
  • the image positioning can be obtained from simultaneous recording of the position and orientation of the transducer using mechanical arm, acoustic spark gap or electromagnetic sensor techniques (Detmer et al . 1994; Geiser et al . 1982; Gardener et al . 1991; Hernandez et al . 1996; Kelly et al . 1994; King et al . 1990; Moritz et al . 1980; Moskalik et al . 1995; Nelson et al . 1996; Riccabona et al.1995).
  • Insonation angle for data acquisition is often fixed in the 3D methods, while 2D ultrasonography routinely allows interrogation from a variety of angles to optimise structure boundary definition.
  • the angle dependency of ultrasound reflection and backscatter intensities when investigating tissue composition was reported in 1985 (Picano et al.1985).
  • An ability to "compound" these multiple angles of insonation into a single data set would significantly improve signal to noise and thus speckle contrast and produce the most coherent object for segmentation and reconstruction (Hernandez et al . 1996; Hughes et al . 1996; Moskalik et al . 1995; Nelson et al.1996; Shattuck and von Ramra 1982) .
  • An object of the present invention is to provide a system based preferably, but not essentially, on freehand 2D ultrasound scanning, capable of delivering precise and rapid 3D reconstruction and leading to successive grey-style segmentation and volumetric analysis .
  • Embodiments of the invention may be capable of fitting into the biomedical and clinical research environments to allow 3D Ultrasound to take its place alongside the other mainstream imaging modalities of Magnetic Resonance Imaging (MRI) and Computerised Tomography (CT) that can routinely exploit the advantages of 3D imaging and analysis.
  • MRI Magnetic Resonance Imaging
  • CT Computerised Tomography
  • a method for reconstructing in 3D an image of an object scanned in 2D a plurality of times to produce a plurality of 2D image data slices at different angles of inclination said plurality of 2D image data slices being recorded and stored on a recording medium whereon said 2D image data slices are recorded in succession together with at least one datum which identifies said 2D image data slices as corresponding to at least one changing physical parameter which varies in time as the 2D scanning takes place, said 3D image of the object being reconstructed from the recorded 2D image data slices in dependence upon said recorded at least one changing physical parameter.
  • an apparatus for use in reconstructing in 3D an image of an object scanned in 2D a plurality of times to produce a plurality of 2D image data slices comprising scanning means operable to scan an object to produce said 2D image data slices at different angles of inclination, recording means coupled to said scanning means and operable to record the output thereof onto a recording medium whereon said 2D image data slices will be recorded in succession, said recording means also being operable to record onto said recording medium, together with said 2D image data slices, at least one datum which identifies said 2D image data slices as corresponding to at least one changing physical parameter which varies in time as the 2D scanning takes place, and processing means coupled to said recording means and operable to reconstruct said 3D image of the object from said recording medium, in dependence upon said recorded at least one changing physical parameter.
  • a method for reconstructing in 3D an image of an at least part of an object scanned in 2D a plurality of times wherein a plurality of 2D image data slices produced as a result of said scanning at different angles of inclination are processed to create a 3D grid of points containing data values, the said plurality of 2D image data slices being associated with at least one datum which identifies the various positionings of said 2D image data slices relative to said object, said 3D grid being constructed based on said at least part of an object being scanned, and image data values being inserted at said grid points as a result of processing of said 2D image data slices in dependence upon said least one datum.
  • a method of calibrating a scanning and position detecting device having a position detecting transmitter defining a registration frame, a position detecting receiver cooperable with said position detecting transmitter and having its own coordinate frame, and a scanning transducer mechanically connected to said position detecting receiver and having a coordinate frame associated with the image it produces, wherein the transformation from said image coordinate frame to said position detecting receiver coordinate frame is determined by scanning a point or volume in space from different transducer angles and positions, and employing an iterative mathematical process on the resultant data, thereby to calculate said transformation.
  • a method for the non-invasive determination of a condition inside a mammalian body comprising reconstructing in 3D an image of at least part of said body scanned in 2D a plurality of times to produce a plurality of 2D image data slices at different angles of inclination, said plurality of 2D image data slices being recorded and stored on a recording medium whereon said 2D image data slices are recorded in succession together with at least one datum which identifies said 2D image data slices as corresponding to at least one changing physical parameter which varies in time as the 2D scanning takes place, said 3D image of said body being reconstructed from the recorded 2D image data slices in dependence upon said at least one changing parameter.
  • a method for the non-invasive determination of a condition inside a mammalian body comprising reconstructing in 3D an image of an at least part of an object scanned in 2D a plurality of times, wherein a plurality of 2D image data slices produced as a result of said scanning at different angles of inclination are processed to create a 3D grid of points containing data values, the said plurality of 2D image data slices being associated with at least one datum which identifies the various positionings of said 2D image data slices relative to said object, said 3D grid being constructed based on said at least part of an object being scanned, and image data values being inserted at said grid points as a result of processing of said 2D image data slices in dependence upon said least one datum.
  • a system that rapidly produces a regular 3D data block suitable for processing by conventional 3D analysis and volume measurement software .
  • the system uses electromagnetic spatial location of freehand-scanned Ultrasound B-Mode image frames or slices, signal conditioning hardware and UNIX based computer processing.
  • An efficient algorithm has been developed that populates a Cartesian grid with data extracted from the 2D image frames, acquired with a variety of interrogation angles.
  • the utilisation of data from multiple angles of insonation reduces the angle- dependency of reflection intensity from each interface.
  • Such "compounding" was found to significantly reduce speckle contrast, improve structure coherence within the 3D greyscale image and enhance the ability to detect, segment and measure volumes on the basis of structure boundaries .
  • volume measurement based on automated greyscale segmentation of a series of water filled latex and cylindrical foam rubber phantoms with volumes in the range 0.9 to 8.0 ml . show that a high degree of accuracy, precision and reproducibility can be obtained.
  • the 3D reconstruction and automatic greyscale segmentation of water filled latex phantoms gave volumes for the enclosed water with rms accuracy of 1.1%, while the volumes of the foam rubber phantoms showed rms coefficients of variation of 1.4% (test- retest) and 1.3 (inter-observer).
  • the disclosure here shows that two-dimensional (2D) images acquired with conventional, freehand, scanning techniques can be reconstructed to provide a three-dimensional (3D) map of echo-intensities that allows reliable and accurate volume measurement of structures of interest following grey-scale segmentation. Extension of the technique to handle in vivo data sets by allowing physiological criteria to be taken into account in selecting the images used for reconstruction is also illustrated.
  • Satisfactory 3D reconstruction from freehand 2D ultrasound images requires precise spatial registration of the ultrasound image in a common reference frame. Compounding of the data places a particular emphasis on the precision of this registration and requires compensation for systematic errors associated with any position sensing device (Detmer et al . 1994; Moskalik et al . 1995) .
  • a 3D data block can then be generated and the intensity data from each image extracted into its appropriate position.
  • a precise method of compensating for, or gating to, physiological motion such as respiration or the cardiac pressure cycles in blood vessels using electro-cardiographic (ECG) recording, is often preferred to ensure that such motion does not disrupt or deform the integrity of the structure to be reconstructed.
  • ECG electro-cardiographic
  • the output from the process must be available within a short time of completing the scan and be in a form that can exploit the highly efficient image processing and analysis tools developed for other medical imaging modalities.
  • computer model fitting to generate volume measurement could be used, this may not deal adequately with pathology.
  • a sufficiently high degree of confidence and reliability in the results of automated segmentation on the basis of reconstructed, grey-scale, echo intensities and any subsequent volume measurements and analysis require the generated 3D ultrasound image quality to be improved beyond that represented by a typical 2D ultrasound frame.
  • a preferred embodiment of our optimal 3D ultrasound system may have the features that:
  • the ultrasound scanning equipment not be irretrievably modified.
  • the intensity values in the 2D ultrasound images be preserved to allow reconstruction, volume measurement and greyscale analysis of the acquired data.
  • a 3D data block be generated, directly compatible with 3D image analysis products.
  • EPOS electromagnetic position and orientation sensor
  • the invention is described as applied to a biological sample or living tissue, it could also be applied to an inanimate object for the detecting of flaws or cracks, for example in a manufactured metallic obj ect .
  • Figure 1 is a diagram of system components of a preferred embodiment of the present invention
  • Figure la shows circuitry details of Figure 1;
  • Figure 2a shows an ultrasound transducer combined with a Polhemus receiver;
  • Figure 2b shows transforms relating to the apparatus of Figure 2a
  • Figure 2c shows processed signals used for frame selection
  • Figure 3 is volumetric reconstruction of a foam phantom
  • Figure 4 shows images of a phantom produced by the system of Figure 1
  • the remaining tables and figures relate to the description herein of the diagnostic scans carried out in the Watanabe rabbit, and the scans of human carotid artery bifurcations, as discussed hereinafter.
  • a Polhemus transmitter 1 is fixed adjacent to an object to be scanned, and emits electromagnetic waves.
  • the transmitter 1 is coupled to an EPOS Polhemus 2 which, in return, receives signals from a Polhemus receiver 2a rigidly coupled to a Toshiba scanner 3 and a probe 4.
  • Synchronisation and processing hardware 5 coupled to the EPOS Polhemus 2, scanner 3 and probe 4 is itself coupled to a video recorder 6 to provide image data to a video channel thereof, with positioning (EPOS) and physiological (ECG) information to two respective audio channels.
  • EPOS positioning
  • ECG physiological
  • the output of video recorder 6 can be fed to a processor 7 with storage, which is itself coupled to a frame select circuit 8.
  • the output of the processor storage is fed to a further processor 9 for the reconstruction of a 3D grid map, whilst a PC 10 is arranged for reconstructing 3D images and measurements from the 3D grid map.
  • a PC 10 is arranged for reconstructing 3D images and measurements from the 3D grid map.
  • Figure 2a shows the physical arrangement of the Polhemus receiver 2a and scanner 3 and probe 4 of Figure 1, with a perspex strip connecting the receiver 2a to the other parts.
  • Figure 2b indicates the transformations required in order to transform between a point in a scanned image data slice having coordinates qrs within the slice, and the coordinates XYZ of the reference frame of the transmitter 1. The significance of these transforms will become apparent later.
  • the system implementation falls into three separate phases (figure 1) .
  • Phase 1 comprises the complete and continuous recording of the ultrasound images, their encoded positional information and any ECG wave form.
  • the three inputs were captured on the video and two associated stereo audio channels of an S-VHS video recorder 6 (Panasonic AG 7350, Matsushita Electric Industrial Co., Ltd. Osaka , Japan).
  • the ultrasound image frames were generated on a Toshiba SSH 140a ultrasound scanner 3 (Toshiba Medical Systems U.K. Crawley. England) fitted with a 7.5MHz linear array transducer 4.
  • the positional information for registration was obtained from an electromagnetic position and orientation sensor [EPOS] Polhemus '3
  • Modular signal processing hardware 5 allows the recording of positional and any ECG information on to the two hi-fi audio channels of the video recorder.
  • Ultrasound transducer positional information was obtained from an electromagnetic position and orientation sensor (EPOS), Polhemus '3 Space Isotrak II' 2, 2a attached to the ultrasound scanning transducer 3 as shown in fig 2a.
  • EPOS electromagnetic position and orientation sensor
  • Polhemus '3 Space Isotrak II' 2, 2a attached to the ultrasound scanning transducer 3 as shown in fig 2a.
  • the first process module generated the required command line for the EPOS by a programmable controller 11 (PIC16C54) , following a single manual contact closure.
  • the controller was clocked by a 8MHz crystal oscillator, communicating with the EPOS via an RS-232 line driver (MAX233) which operates at 9600 baud.
  • the command line consisted of thirteen characters defining the output list as the x,y,z linear co- ordinates, plus angles for roll, pitch and yaw, interspersed with "carriage return" and "line-feed” commands.
  • the EPOS On receipt of a synchronising pulse (see below) , the EPOS transmitted the output list corresponding to the sensor position at that instant, and therefore corresponding to the current video frame.
  • This output binary data stream was returned to the module and scaled, using a line driver 12 (MAX483) and potential divider 13, to provide a 0.4V amplitude unbalanced signal suitable for the audio input channel of the S-VHS video tape recorder.
  • synchronisation was necessary between the scanner and EPOS.
  • the latter was run in "non-continuous" mode, awaiting a pulse to start the data stream.
  • the composite video output from the scanner was taken to a purpose-designed "synchronisation " module.
  • the composite signal was fed to a sync separator circuit 14 (TDA8128) which provided an output pulse for each video field.
  • a bi-stable circuit 15 (4013) was used as a divide-by-two to give a single output pulse per frame.
  • This monophasic pulse activated a differential line driver 16 (MAX483) to provide the external synchronisation signal for the EPOS.
  • a simple diode- pump circuit 17 also integrated field pulses to light an indicator as a visual confirmation of synchronisation pulse presence.
  • the above processing was designed to ensure that the positional information was sent to the tape concurrently with the corresponding video frame. In practice, there was a slight latency of around 18ms between the instant of frame starting and the positional data stream. This was accurately measured and compensated for during subsequent editing in the digital frame store medium.
  • the ECG conditioning module can be considered in two sections.
  • the electrically-isolated front end consisted of a conventional differential instrumentation pre-amplifier (Burr-Brown Ltd. Livingston, West Lothian.) with a voltage gain of 50, configured together with an external operational amplifier to provide active drive of the indifferent electrode.
  • This front end was interfaced with the second section of the circuit via an isolation amplifier (Burr-Brown ISO 107) which also provided isolated power supplies for the pre-amplifier and driver. There was no ground connection to the subject, the resultant isolation (2500 volts a.c. rms., 3500 volts d.c.) allowing safe clinical use.
  • the isolator output was connected, via a variable attenuator, to the second, non-isolated, section of the circuit.
  • An output socket at this point provided amplified ECG for display on the ultrasound scanner screen by connection to the high level d.c, non- isolated, patient input socket.
  • a second output of this ECG stage was routed within the module to a differentiator, for enhancement of the QRS wave and partial suppression of the P and T waves.
  • a fast time constant removed baseline shifts.
  • Fig. 2c shows ECG and Polhemus signals recorded on tape by the video tape recorder.
  • a respiratory signal detected by conventional means could be employed.
  • Phase 2 starts with the transfer of a continuous sequence of image frames, each with its associated EPOS and ECG data, from the video tape to the computer. This is followed by the selection of a subset of frames on the basis of their image content and/or timing within the cardiac cycle, and finally the reconstruction process .
  • Data from the videotape were digitised on a 100MHz Silicon Graphics Indy R4600PC workstation 7 configured with 96Mbytes of memory, a Video option card, and a Cosmo Compress motion JPEG (Joint Photographic Experts Group) video compression card.
  • Four external SCSI disks were connected to the Indy and striped to provide a fast 8 Gbyte disk store.
  • the JPEG quality factor on the Cosmo card was set to
  • the audio hi-fi channels containing the ECG and EPOS data were sampled using the Indy analogue audio input at 48 kHz.
  • the user specifies a time or percentage based window in the cardiac cycle for which frames will be selected, avoiding the use of excessively long or short duration cycles. For each valid ECG cycle, the exact time for the end points of this window is calculated and mapped to the corresponding frames on the image track of the movie file to generate a "valid frames list" file.
  • FIGURE 2a Physical configuration of EPOS - ultrasound transducer mounting
  • FIGURE 2b Co-ordinate systems and transformations
  • the EPOS transmitter 1 was sited at a convenient location within a 30 cm radius of the object being scanned and remained fixed throughout the data acquisition.
  • the origin and axes of the transmitter established the fixed "registration frame" (designated XYZ in figure 2b) .
  • the EPOS readings tracked the position and orientation of the receiver 2a, and thus the receiver co-ordinate frame (designated xyz) , relative to the fixed transmitter frame.
  • the transformation relating the co-ordinates of a point in frame xyz to those of the same point in frame XYZ was designated M and obtained directly from the EPOS.
  • the EPOS receiver 2a was attached to the ultrasound transducer 3,4 by mounting on a short (15cm) Plastic strip fixed securely to the transducer to minimise electromagnetic influences (figure 2a) .
  • the co-ordinate frame associated with the ultrasound image itself (designated qrs, with q always zero), while fixed relative to the receiver frame xyz, has an offset in both position and orientation.
  • a transformation, including both translation and rotation, had to be applied to correct for the position and orientation of the EPOS receiver in relation to the 2D ultrasound image.
  • We refer to this as the Delta ( ⁇ ) transformation which has to be determined only once for each specific mounting of the EPOS on the ultrasound transducer before the ultrasound images can be properly registered.
  • the transformations M and ⁇ together relate the co-ordinates of a point in space measured using the ultrasound image axis system qrs to the coordinates of the same point measured in the registration axis system XYZ.
  • the Delta ( ⁇ ) transformation was determined by scanning a calibration phantom consisting of two crossed threads suspended in a bath of 20%w/v galactose solution using a wide range of transducer angles and positions.
  • the galactose solution provided a transmission medium for the ultrasound that more closely corresponded to the speed of sound transmission in normal tissue than does water.
  • the crossover provided a point in space whose co- ordinates were unknown but fixed in relation to the EPOS transmitter.
  • Determination of the ⁇ transformation calibrates the ultrasound transducer/EPOS configuration necessary for the accurate registration of the 2D ultrasound images in the 3D co-ordinate system established by the fixed transmitter.
  • the second critical step is Grid- mapping. This is the process which uses the data values observed on a number of oblique, non-parallel ultrasound planes to compute the echo intensities at points on a regular 3D grid in a format compatible with software for presentation, segmentation and analysis. It is essential that the algorithm used for this application is very efficient as a typical reconstruction will involve the calculation of around 2 million new positional intensity values from an input data set of some 12 million values contained on 500 registered frames.
  • the first involves sequencing through the regular array of grid positions and at each point identifying and using the sub-set of the US data that is "relevant" to the calculation of the grid value.
  • the second involves sequencing through the input data points and accumulating the contributions to the "relevant" subset of grid positions.
  • the "relevant" subsets can be specified by introducing a limiting radius "R" within which the relative weights of contributions are made a function of the distance "r" between the data point and the grid position. While the first option appears the more direct, the second is much more efficient.
  • the second approach enables effective use to be made of the limiting radius to reduce very significantly the number of grid-point, data-point pairs used in the calculation of the 3D reconstruction.
  • the saving in total computation time can approach the ratio of the volume of the grid-mapped box to the volume of a sphere of radius "R" .
  • this second approach has potential to support close to real-time implementations, utilising state of the art computing techniques.
  • Our implementation follows the second approach and uses an "inverse distance l/r" weighting scheme with two nested cycles, the outer cycle indexing through the individual 2D ultrasound frames, the inner cycle indexing through the data values and positions associated with each of the US frames.
  • the terms of the first sum are the measured echo intensities of the ultrasound data points which, when registered in 3D, fall within the limiting radius "R" of the voxel centre, scaled by a factor equal to the inverse distance from the voxel centre to the data point .
  • the terms of the second sum are the inverse distance scaling factors themselves. The ratio of the final values of these two sums provides the normalised, distance weighted average assigned to the voxel .
  • the location and orientation of the 3D reconstruction grid is defined in relation to a user selected "KEY" ultrasound frame, typically one that is centrally located and depicts a complete cross-section of the object of interest.
  • the orientation and position of the KEY ultrasound frame relative to the EPOS transmitter co-ordinate system is determined by the EPOS values and knowledge of the ⁇ transformation.
  • the origin of the 3D grid will be at the centre of the KEY frame and the grid axes will be parallel to those of this ultrasound frame.
  • the transformation relating the EPOS transmitter co-ordinate system and that of the 3D reconstruction grid is obtained from the co-ordinates of the centre of the KEY frame and the normalised axis- vectors of the oriented KEY US frame.
  • Phase 3 of the system covers the segmentation, presentation and analysis of the information inherent in the reconstructed 3D array of echo intensities.
  • TOSCA TOols for Segmentation, Correlation and Analysis
  • DX Data eXplorer
  • TOSCA implements a three-dimensional region-growing algorithm for automatic grey-scale segmentation (Elliot et al . 1996; Sivewright et al . 1994).
  • the algorithm tests adjacent voxels for inclusion in the same region-of -interest (ROI) , iterating this process until there are no more contiguous voxels consistent with the seed-point statistics.
  • ROI region-of -interest
  • a smooth contour or surface is generated bounding the ROI; the volume is determined by counting the statistically acceptable voxels rather than by estimating the volume bounded by the smoothed surface.
  • volume estimates compare well with results obtained using other methods based on contouring techniques or on edge detection algorithms provided the boundary of the ROI is reasonably continuous and uniform in intensity.
  • Accurate determination of the ⁇ transformation is the first critical step in image reconstruction. It provides the calibration of the ultrasound transducer - EPOS transmitter configuration necessary for the accurate registration of the 2D ultrasound images in the 3D coordinate system established by the fixed transmitter.
  • the next critical step is "grid-mapping" and involves mapping of the ultrasound intensity values onto a regular 3D grid suitable for input to commercial software for presentation, segmentation and analysis including volume assessment .
  • Grid-mapping involves computing ultrasound echo intensities at points on a regular 3D grid from the data values observed on a number of oblique, non-parallel 2D ultrasound planes. It is very important that the algorithm used for this application is efficient as a typical reconstruction will involve calculating 1 to 2 million values from an input data set containing some 500 frames each with 25,000 echo intensity values. The number and distribution of echo intensities will depend on the scanning pattern, frame selection and the radius examined around each 3D grid point. The single value assigned to each grid point must represent the ultrasound echo intensities observed within this radius. This was achieved through a 1/r weighting scheme with the cut-off radius ( R ) normally set to be 0.25 mm. , slightly larger than the separation between data points in the ultrasound image planes which is typically 0.2 mm. With a sufficient density of data points the result will be insensitive to the exact functional form of the weighting scheme.
  • R cut-off radius
  • the position and orientation of the 2D ultrasound frame is computed with reference to the grid co-ordinate system
  • the box bounding the grid is then mapped onto the ultrasound image plane and the indices (x and y) representing the limits of an ex- scribed rectangle containing all data points relevant to the reconstruction and with sides parallel to those of the ultrasound frame are computed. This is done by transforming the co-ordinates of the eight corners of the grid into the co-ordinate system of the data frame using its origin and orientation cosines.
  • these data points lie on a regular, square lattice the position of each point express in terms of fractional 3D grid indices can be computed incrementally using the two vectors which specify the unit displacements along the rows and columns of the ultrasound data.
  • each final grid value can be calculated from the number of individual contributions and the sums of their individual weights and weighted echo intensities.
  • the grid is represented by a 3 x N array with an appropriate mapping of the linear positional indices.
  • the three sums for each grid position are accumulating as each US data point is processed and the contributions calculated for each of the indices corresponding to the "relevant" sub-grid positions.
  • Our program code makes provision for handling the special case where the Ultrasound data point coincides exactly with a grid-map point .
  • the intensity value for each and every grid point was computed by dividing the weighted intensity sum by the sum of the weights.
  • the dimensions of the reconstruction box can be extended by an amount equal to a "fringe” parameter. This has been used to combine two adjacent reconstructions into a single block. In this case the "fringe” would be set to the same value as the limiting radius ( R ) , avoiding potential edge effects where the two boxes touch. This allowed data points outside the actual box but which were within the limiting radius of the grid points on, or close to, the box surface to be included in the calculations.
  • the system developed within Zeneca has many advantages over existing 3D systems for ultrasound imaging. Scanning is achieved freehand in real time rather than using a step acquisition of frames and gives access to normal 2D scanning procedures and measurements.
  • the storage of registered data onto videotape provides a cost effective data storage system that can be reviewed in normal 2D. Sets or portions of data sets can then be loaded into the 3D system at any time after acquisition. Throughout the 3D processing the original ultrasound intensity data is retained. This gives opportunity for interrogation of data within structures in addition to rendering and visualisation of the surface.
  • a unique system of image frame sub-set selection, from the digitised data set, allows interrogation of the data at precise points within the cardiac cycle. Reconstructions of 3D objects may be obtained at specified intervals within the cardiac cycle to identify any motion or structural changes resulting from the pressure changes during the cardiac cycle.
  • the system also features an output of a 3D datablock suitable for volume measurement utilising state of the art automatic segmentation packages designed for other imaging modalities such as MRI and CT.
  • the generated data sets may be analysed in commercially available 3D analysis software.
  • the compounded data is independent of any individual insonation angle and provides coherent data suitable for automated greyscale segmentation techniques.
  • the original data is preserved throughout the process of generating a data block and may be separately analysed to determine data acquisition density, angles of insonation explored, contributing data values and statistics.
  • Video can be reviewed for 2D data acquisition or for 3D data output. Portions of the video can be captured for interesting features requiring 3D analysis and retrospectively captured during different segments of the cardiac cycle without requiring patient re-examination. All the 3D data from individual interrogations are spatially registered so that relationships between structures are apparent even when captured from differing portions of video.
  • An electromagnetic orientation and position sensor (Polhemus, Colchester, Vermont) is attached to the scanning transducer, providing accurate positional information in 6° of freedom for each image frame generated.
  • a transformation matrix to allow accurate spatial positioning of the image frame has to be established for each sensor/transducer combination.
  • Freehand, free running (25fps) scanning of the object of interest is recorded to videotape (S-VHS) with concurrent ECG, respiration and Polhemus registration data being stored on the audio channels. (Tape bandwidth 2-20KHz) .
  • the module uses the synchronisation for video from the scanner to provide a synchronisation pulse to Polhemus, which then outputs the positional information for this image frame.
  • the binary RS232 Polhemus information is then buffered to allow this to be captured on the audio Hi-Fi channel 1 of the Video recorder.
  • Input of ECG is similarly buffered, multiplexed with the respiration signal from a Pneumotrace II (UFI California USA) and frequency modulated to preserve the slow phases of respiration to allow capture onto the Hi-Fi channel 2.
  • the video (both image and audio data) is then digitised using a real-time frame grabber (SG-COSMO utilising JPEG compression (15:1).) onto a Silicon Graphics Indy workstation, (approx 7,500 frames for a 5 minute video) .
  • the frequency modulated audio channel 2 signal being demodulated before capture.
  • the audio signals being oversampled at 48KHz on each channel.
  • a custom written Graphical user interface utilising the IRIX media enables image frames occurring at precise portions of the cardiac cycle and respiration phase to be extracted. This uses the audio file of the
  • ECG/respiration trace to recognise the intervals of the cardiac cycle and identifies the associated image frames and Polhemus data. Typically a data set of 500 frames is extracted. Further custom written software decodes the positional information, applies the transformation matrix to spatially align the image frames and extracts the greyscale information to a regular 3D data block.
  • This software provides for the multiple observations, from a variety of angles, of any single data point within the prescribed data block by scavenging data from a prescribed radius to the predetermined data point .
  • This "compounded" greyscale data provides a significantly enhanced image in that the process reduces "speckle" and provides coherent data for segmentation algorithms.
  • the system was evaluated against three criteria, the ability to provide accurate estimates of volumes in the range encountered with atherosclerotic plaques and small tumours, the ability to improve overall image quality through carefully registered spatial compounding and finally, the ability to carry out in vivo reconstructions of clinical relevance.
  • the first two studies employed phantoms scanned at room temperature using galactose solutions to match the 1540 m/s sound velocity for tissue inherent in the scanner's internal calibration of image depth.
  • the volume of distilled water contained within the balloon was obtained from the weight difference assuming a density value at room temperature (22°C) of 0.9978 gm./ml.
  • a second series of phantoms was used to introduce internal structure and texture as well as provide volumes of different shape. These phantoms were cylinders cut from a block of foam rubber by cork borers of known diameters then trimmed to length providing volumes of approximately 0.75, 1.00 and 2.50 ml. The phantoms were again scanned in a bath of 20%w/v galactose solution. Measurements of the "wet" cross sectional diameters and cylinder length were made from the 2D Ultrasound images for volume calculation.
  • %CoV percent coefficient of variation
  • a simple measure of coherence was obtained by examining the number of pixels identified in the interface boundary at a fixed level (40) and window (2) and the variation of intensity of the pixels along the boundary in the IBM DX environment.
  • the "segmentation window width" required to produce a continuous boundary at a fixed level (40) provided a similar but independent measure of the inverse of this coherence.
  • a more automatic and tool independent measure has been implemented by applying a 3x3 inverse distance filter (F) at each pixel location in the 2D image plane.
  • the filter result for each pixel in the image was then normalised by the original pixel value and the absolute value of the result expressed as a percentage.
  • These percentage values can be used to colour-code a display to reveal areas of high or low coherence within a region of interest, greater coherence again represented by the lower percentage values.
  • the average of these percentage values taken over a region offers an over-all coherence measure.
  • a "Coherence Number" for the reconstructed map or an individual plane which is equal to 100 divided by the mean of the percentage values for the map or plane.
  • the spatial resolution of the system as implemented has a theoretical isotropic limit of 0.2 mm. from the anamorphic scaling to half size of 2D ultrasound images with an in-plane resolution of 0.1 mm.
  • the out-of-plane resolution of the 2D images is controlled by the beam- thickness profile of the transducer and is of order 1 mm even at the focal depth. Without the use of the multiple insonation angle, spatial compounding technique, this much larger value would dominate the spatial resolution characteristics and render them very significantly anisotropic.
  • the degree to which the system's spatial resolution approaches the theoreticaly limit will instead depend on the extent of the compounding (number of ultrasound planes and diversity of insonation angle) and on the quality of the ⁇ matrix used in image registration.
  • the spatial resolution of the system was determined to be 0.5 mm isotropic. This figure had an associated standard deviation of 0.18 mm, and was consistent with the quality of the results and images presented throughout the paper.
  • Observer 1 demonstrated a CoV of between 5% and 2% for the model based calculation of volumes compared to an overall 1% CoV for 3D analysis.
  • Observer 2 demonstrated a 2% CoV for the model based volume measurement against a 1% CoV for 3D analysis.
  • Overall precision of 3D measurement is illustrated by an rms coefficient of variation of 1.4% (test-retest) and 1.3% (inter-observer).
  • the "compounded" grid-mapped reconstructions offer very significant improvements in interface coherence and hence the ability to segment volumes of interest based on intensity values.
  • a) the foam-rubber phantom to provide high contrast interfaces with a relative uniform boundary density and b) positioning the 3D grid using the "key" 2D plane so that direct comparison of a "raw” versus a reconstructed data plane can be made it has been possible to quantify the improvements in image quality that can be achieved with reconstructions based on compounded, free-hand scanned, high-density sampled data sets, (table 3, figures 4a and 4b) .
  • the percent coefficient of variation (CoV) of the 1/r weighted values used in the reconstructions indicated the consistency of the distribution of values averaged during the grid-mapping procedure for every pixel in the reconstruction.
  • the average CoV taken over the whole plane was 0.23%.
  • Assessment of boundary continuity for a fixed segmentation level involved determining the width of the "Window” needed to establish a continuous perimeter for the volume of interest (VOI) .
  • VOI volume of interest
  • the "Segmentation window width" then had to be set to + , 3.1 (15% of level) for the VOI in the reconstructed plane to obtain a qualitatively continuous and unbroken boundary. This compares well with a window width of 8.5 (42% of level) required for the same effect in the 2D ultrasound image .
  • a maximum pixel count of 345 for the perimeter of a VOI was obtained from a 1000 frame reconstruction, gridmapped using a limiting radius (R) of 0.5mm, at a window of ⁇ 2 (10% of level) .
  • R limiting radius
  • 300 boundary pixels were detected (87% of the maximum) in the 3D reconstructed plane compared to 121 (35% of maximum) in the original 2D ultrasound image (Table 3) .
  • the derived "Coherence Number” (see methods section) provided an abstract measure of ability to segment on greyscale intensity that is independent of the tools to be used.
  • the coherence number generated from the statistical analysis of the plane was found to correlate well with :
  • the percent coefficients of variation show the distribution of l/r weighted pixel values used in the 3D reconstructions of the reference plane to be tightly grouped, with average values for the whole plane (13,000 pixels) of 0.25% for the 500 frame reconstruction (Table 3) .
  • the effective "noise to signal" figures are 1:400.
  • the power of the approach described originates in the freehand acquisition of extensive, compounded, data sets.
  • the freehand aspect allows advantage to be taken of instinctive scanning behaviours and ensures a high density of data in the areas of greatest interest.
  • the grid-mapping procedure described ensures that the echo intensity for each of the points in the 3D reconstruction is calculated from a very significant number of observed data values.
  • compounding ensures an extensive positional sampling of echo intensities in the immediate neighbourhood of the reconstruction point and a range of insonation angles; this results in speckle reduction.
  • the overall improvement in image quality allows reliable and accurate structure segmentation and volume assessment on the basis of greyscale, 3D reconstructed echo intensities.
  • the format of the reconstruction allows routine handling of ultrasound image data in the advanced 3D analysis environments that once were exclusive to MRI and CT.
  • Reference features can then be used to provide cross correlation of structures and their intensities between instruments and subsequent data sets. This has been checked using three ultrasound scanners and two calibrated 7.5 MHz linear array transducer/EPOS sensor configurations in various combinations .
  • An additional advantage of the methodology described is common to all 3D imaging techniques and derives from the intrinsic limitations of 2D data in volumetric analysis. While any 3D image can be considered as a series of parallel 2D plane images, volume assessments using 2D data employ a relatively small number of independent planes. These are used to establish a set of ID or 2D parameters to define a 3D "model" and compute its volume.
  • the quality of the predicted volume will depend on the accuracy of the measurement of the parameters, the precision with which the planes selected meet the geometric requirements of the model, and the degree to which the structure "conforms" to the model.
  • the major limitation lies with the "model” itself. Simple models make assumptions about the symmetry and regularity of structure. Even the more sophisticated, adaptive, modelling techniques which use training sets to select the most appropriate parametric model, become progressively less valid with increasing degrees of pathology ( Cootes et al . 1994, Syn et al . 1995. The emphasis placed in our approach on acquiring over-determined data sets to support the critical steps associated with 3D registration and reconstruction is in marked contrast to what is described elsewhere.
  • 3D shape modelling has been used with constraints provided by the structure boundaries established on the various, registered 2D image planes. While, in general, the appearance of such surface rendered objects will be good, the reliance on relatively low data densities and single insonation angle must restrict the precision associated with such segmentation and volume assessments.
  • the 3D reconstruction techniques described are not restricted to use with linear array transducers and can, in principle, be applied wherever 2D ultrasound is used. Reconstruction of data acquired with other probe types has been achieved in our laboratory. Extension of the system to address in vivo studies in a Research or Clinical environment requires the physiological monitoring of cardiac and respiratory functions that can produce relative motion or distortion of the structures of interest. The facility to select those 2D ultrasound images captured under identical cardiac cycle conditions has been described. Extension to handle respiratory motion has also been developed. The second stereo channel of the S-VHS tape is used to record such information. Provided that the selection criteria are sufficient to effectively "freeze" the associated motion, a consistent 3D reconstruction can then be obtained. This has already been demonstrated in this laboratory through 3D reconstruction of in vivo vascular structures using only the 2D ultrasound images recorded close to the midpoint of diastole (Allott, et al . 1995).
  • the readily derived volume measurement may be used to follow progression of disease process or regression during treatment where the dimensions of a structure are affected by the disease process - e.g. Tumour growth, Atherosclerotic plaque, cardiac hypertrophy and renal disease.
  • Other diagnostic functions may include the maturation of ovarian follicles or endometria in fertility monitoring or foetal development applications.
  • Tissue characteristics rather than dimensions may change during some disease processes. These changes may be monitored by analysis of the greyscale attribution within the 3D data set, which is independent of view and acquisition angle. These characteristic changes may be found in a wide variety of disease processes where tissue damage and subsequent scarring occurs as a result of vascular insufficiency, toxic or fibrotic response e.g. renal disease, liver disease and infarction.
  • the ability to interrogate all the data contributing to the 3D volume, from a variety of angles, allows analysis of the contributing greyscale attribution and position with the angle of insonation being known. Using this data the surface characteristics (i.e. roughness) may also be determined. This may have application in monitoring cartilage or ulceration of atherosclerotic plaque which is known to promote thrombus formation and lead to vascular occlusion.
  • compounded data in generation of a regular 3D data block allows the data to be presented as a slice at any preferred angle or orientation, generating a novel 2D ultrasound image that is independent of the orientations used in data acquisition.
  • the isotropic nature of the data voxels within the reconstructed compound data block make such images meaningful and useful in presenting views that are unobtainable in conventional ultrasound scanning. For example an orientation of scan that illustrates the carotid bifurcation in plan may be obtained, as described and illustrated later herein. This would equate to a view taken with the transducer positioned inside the head or chest of the subject.
  • Reconstruction of interrogations may also provide an aid to surgical planning.
  • the spatial relationships between structures such as blood vessels is of great importance in determination of best approach for a surgical procedure.
  • Reconstruction in 3D with the ability to differentiate structures and tissue character and display and rotate these on screen provides a comprehensive overview so that such planning can be made with increased confidence.
  • Fine D Three-dimensional ultrasound imaging of the gallbladder and dilated biliary tree: reconstruction from real - time B-scans. Br J Radiol 1991; 64: 1056-????.
  • FIG. 1 Diagram of 3D Freehand ultrasound system components
  • Figure 2 a) Photograph of the physical arrangement of ultrasound transducer and Polhemus EPOS receiver b) Co-ordinate systems and transformations for 3D spatial location of 2D ultrasound images.
  • Foam rubber cylinder and 0.5mm diameter supporting wire used as a phantom for the reported studies.
  • Figure 4 Segmentation of pixels in the boundary of a foam phantom using a level of 40 and a window of 2.
  • Panel a represents the segmentable pixels in the original 2D image.
  • Panel b represents the segmentable pixels in a compounded reconstructed plane identical in location.
  • volume estimated Volume measured from 3D from 2D slices ultrasound
  • 3D reconstruction of ultrasound images allows volumes, and other variables related to volume, to be measured independently of the data acquisition views and angles (Picano 1985) . Accurate sequential monitoring of pathology and communication of the information then becomes available as the whole structure is included at each interrogation.
  • 3D reconstruction of ultrasound data reviews see Rankin, 1993; Vogel, 1995; Levine 1992).
  • the image positioning can be obtained from simultaneous recording of the position and orientation of the transducer using mechanical arm, acoustic spark gap or electromagnetic sensor techniques
  • Satisfactory reconstruction of 3D data from freehand acquired 2D ultrasound images requires accurate spatial registration of the ultrasound image in a fixed reference frame. This allows the data from each image to be extracted and positioned appropriately within a common 3D data block.
  • the 3D reconstruction must be available within a short time of completing the scan and be in a format that allows use of the highly efficient image processing and analysis tools that have been developed for other medical imaging modalities.
  • a successful system also requires that the 3D image quality is improved beyond that of a typical 2D ultrasound frame in order that segmentation on the basis of grey-scale, reconstructed echo intensities, can be used and lead to a sufficiently high degree of confidence and reliability in the results of automated segmentation and subsequent volume measurement and analysis.
  • FIGURE 2 Co-ordinate systems and transformations
  • the position and orientation of the 2D ultrasound image plane in 3D space was established using an the electromagnetic position and orientation system (EPOS) .
  • the sensor was attached to the ultrasound probe while the origin and axes of the transmitter established the fixed "registration frame" ( designated XYZ in figure 2b) .
  • the EPOS tracked the receiver and provided read-outs of the vector and the three eulerian angles which defined the position and orientation of the receiver and its co- ordinate frame (designated xyz ) , relative to the fixed transmitter frame.
  • the transformation relating the co- ordinates of a point in frame xyz to those of the same point in frame XYZ was designated M .
  • the co-ordinate frame associated with the ultrasound image itself (designated qrs ) , while fixed relative to the ultrasound probe and thus the receiver frame xyz, has an offset in both position and orientation.
  • This as the Delta (A) transformation was defined.
  • the transformations M and ⁇ have translation and rotation components and can either be represented by a vector and a 3x3 rotation matrix, or by a single 4x4 matrix using the formalism of homogeniuos co-ordinates (Newman 1973 ) .
  • the ⁇ transformation was determined by scanning a calibration phantom consisting of two crossed threads suspended in a 20% w/v galactose solution using a wide range of transducer angles and positions.
  • the crossover provides a point in space whose co-ordinates are unknown but fixed in relation to the EPOS transmitter.
  • the galactose solution provides a medium matched to the sound transmission velocity of normal tissue and thus to the internal depth scaling of the ultrasound instrument.
  • After digitisation on the Silicon Graphics Indy the frames containing images where the cross was visible were extracted from the moviefile. These 2D frames were then displayed and the image location of the centre of the cross (r,s) determined and noted. A minimum of two observations were made for each attitude, totalling some 50 observations of this unique point in space.
  • volume phantom measurements and their analysis became the basis for a refinement process in its own right.
  • a single cylinder of foam rubber was mounted in a 20% galactose bath and scanned with the equipment described.
  • Several data sets were recorded using a variety of scan directions and angles, but each set containing only scans from approximately the same alignment. These data sets were then processed individually to provide multiple observations of the phantom volume, acquired from different positions and orientations .
  • Reconstructed phantoms, from a series of scanning passes, using less than perfect ⁇ show an occurrence of multiple discrete images which converge into one when the same data is processed using an optimised ⁇ matrix.
  • Two hypotheses can be drawn up immediately. - First, the degree to which reconstructed images exhibit convergence of components stemming from different sub- sets of scanned Ultrasound images can provide a measure of the quality of ⁇ or degree to which it has converged and is optimal .
  • Quantification can be achieved by using any one of several BOOLEAN operations between volume reconstructions from two or more scanning subsets, as long as each is sufficient to reconstruct the whole object.
  • the relevant BOOLEAN operators are INTERSECTION, SUBTRACTION and UNION.
  • the convergence of the separate reconstructed volumes to a common value can also be used.
  • Equation la The analysis starts with equation la but with P and p redefined to be the position of a generalised data point in the two co-ordinate systems so that the equation now represents registration of the data for a whole 2D ultrasound image plane.
  • the U s transformation is a product of rotations and translations and so will itself correspond to a translation and a rotation about an axis.
  • M and ⁇ each have rotational and translational components defined in three dimensions by 3 angles and a vector. Representing the rotational and translational components of M , ⁇ T and C for the 2D images with general index j and k by Rot Mj , Tran Mj , Rot Mk , Tran Mk , Rot DT , Tran DT , Rot c and Tran c respectively, the rotation components must satisfy the equation
  • V will be the axis of the rotation matrix defined by tthhee pprroodduucctt RRoott MM **RRoott DDTT **lRot M " .
  • the quality of an image can be improved by taking an average over multiple, identically obtained, samples.
  • the basis for this is that the signal is systematic while the noise is random.
  • the discrimination between the signal and the noise is increased over that for a single sample by the square root of n' . This is done in MRI to improve the image, but requires that corresponding points in succeeding samples are identically placed. Under these condition where the samples being combined differ only in their noise content, the signal to noise ratio itself provides an important characterisation of quality.
  • VOI on the basis of intensity. This requires a measure that will reflect the consistency or "coherence” of intensities within the sub-set of pixels that define the boundary (or interface) of the VOI.
  • level and window parameters common to statistical segmentation methods e.g. IBM's TOSCA -
  • a more automatic and tool independent measure has been implemented by applying a 3x3 inverse distance filter (F) at each pixel location in the 2D image plane.
  • the filter result for each pixel in the image was then normalised by the original pixel value and the absolute value of the result expressed as a percentage. These percentage values can be used to colour-code a display to reveal areas of high or low coherence within a region of interest, greater coherence again represented by the lower percentage values. The average of these percentage values taken over a region offers an over-all coherence measure.
  • a "Coherence Number" for the reconstructed map or an individual plane which is equal to 100 divided by the mean of the percentage values for the map or plane.
  • the Coherence Number quantifies the degree to which the value of each pixel is correlated to those of its immediate neighbours, i.e. the pixels that will form the interface or boundary of the VOI. It also provides a measures of the continuity of an edge.
  • the structure of the grid mapping algorithm itself indicates that the execution time will be the sum of three terms .
  • the first relates to the input of the ultrasound data frames and the selection of the relevant sub-set of data for each. This term will be linear in the number of frames in the input set .
  • the second relates to the grid map computation itself. This will be proportional to the average number of ultrasound data used per grid point and to the total number of grid points. This term will dominate for all but the lowest R values .
  • the third term relates to the output of the computed grid-map and will be linear with the grid-mapped volume.
  • the grid spacing used in the reconstructions was 0.2 mm giving a total number of grid points per map equal to 125 times the map volume in mm.
  • the 2D ultrasound frames were cropped to 31 by 32 mm and have a data spacing of 0.2mm. each frame yielding a maximum of 24,800 input data values to the reconstruction.
  • the value of 9.09 predicted by the model for the constant of proportionality for the three maps (A,B and C) with a volume of 11,440 mm 3 is consistent with the values determined for the whole map and for the reconstructed central, key plane. These values are respectively 4.53 and 9.45, indicating that some 50 % of the total ultrasound data points fell within the grid- mapped volume while in the central plane, which was the focus for the scans, the density of ultrasound data was highest .
  • V.3 IMAGE QUALITY ESTIMATIONS Table XI lists the observed values for parameters that relate to image quality, as the number of data planes and the limiting radius R vary for a fixed grid mapped volume.
  • the extent to which compounding is used in the reconstruction is represented by the entries showing the average number of data used in calculating the values for each grid point in the reconstructed central KEY plane. This average increases with both the number of ultrasound frames in the input data set and with the grid-mapped cut-off radius ( R ) . Using the statistics for the whole of the grid-mapped volume , a very strong linear correlation can be shown to exists be the average number of data used per grid point and the product of the number of frames with the cube of the cut-off radius R.
  • the percent coefficient of variation of the 1/r weighted values indicates the "tightness" of the distribution of values being averaged during the grid- mapping procedure.
  • the average pixel value for the reconstructed KEY frame lies within the range 39.9 to 40.6 for the 15 cases presented.
  • the relationship between this % CoV and the degree of compounding as represented by the statistics on the average number of data / grid point and thus the product of the number of frames with the cube of the cut-off radius R is apparent.
  • the Coherence number correlates well with another Edge Continuity measure based directly on the Level and Window parameters of a TOSCA segmentation. As with the Signal to Noise measure, the Coherence Number improves as the sample size increases, but as the results show the relationship involves the cube root of the product of ultrasound frames and the cube of the limiting radius, or, in fact to the average number of data contributing values to a grid point (all distinct, i.e. with different angles of insonation) rather than the square root of ' n' (the number of repeated samples with potentially the same value) .
  • the Edge continuity measures for a fixed segmentation level involved determining the width of the "Window” needed to establish a continuous perimeter for a VOI and the count of the number of pixels in that perimeter for a standard setting of the Window. These attributes relate directly to TOSCA segmentations.
  • the tabulated results correspond to the "Level” being set to the mean value of pixels in the reconstructed plane, that is 40, and the standard Window for the pixel count being set to 2. This gives a maximum, "target”, pixel count of 345, obtained using map A with 1009 frames and an R of 0 . 50 mm .
  • the Window entries provide an independent confirmation that the Coherence number represents an appropriate, objective and quantitative way to characterise the quality of a reconstruction in regard grey scale TOSCA segmentation.
  • the pixel count shows the anticipated gradual fall-off from the maximum value of 345 as the number of ultrasound frames used for reconstruction or the cut-off radius R is reduced.
  • each data value in an ultrasound frame has potential to contribute to the grid map calculation for two neighbouring grid points. This potential becomes a certainty when R exceeds the length of the grid diagonal (0.346 mm) .
  • Table XI Quality parameters for Grid mapped central plane which is co-incident with the location of the KEY 2D ultrasound frame.
  • the maps A, B and C are of a fixed size (26x20x22 mm. ) but represent reconstructions based on data sets with respectively 1009, 505 and 253 ultrasound frames.
  • the need here with ultrasound is not to just characterise the statistics of each picture element (pixel or voxel) in the reconstructed object as in independent entity, but to provide a measure that relates to the ability to segment a volume of interest (VOI) on the basis of intensity.
  • the "Coherence Number" defined in the paper has the appropriate properties as it quantifies the degree to which the value of each pixel is correlated to those of its immediate neighbours, i.e. the pixels that will form the interface or boundary of the VOI. It also provides a measures of the continuity of an edge.
  • the Coherence number correlates well with another Edge Continuity measure based directly on the Level and Window parameters of a TOSCA segmentation.
  • the Coherence Number improves as the sample size increases, but as the results presented in Figures X3 and X5 show the relationship involves the cube root of the product of the number of ultrasound frames and the cube of the limiting radius. or, in fact to the average number of data contributing values to a grid point (all distinct, i.e. with different angles of insonation) rather than the square root of 'n' (the number of repeated samples with potentially the same value) .
  • Table XX A summary of the "typical values" for various statistical properties of raw and reconstructed image planes. (Cut-off radius R between 0.2 and 0.3 mm. - Maps A,B & C) .
  • McPherson (Sci. American). Individual US planes (usually approximately parallel) are registered in a common 3D reference frame and then surface rendered using interpolation methods to fill in the gaps. Prager, Gee (Cambridge) use similar method but also acquire data on transverse planes and are working on ways to "adjust" intersecting planes so as to bring boundaries into alignment .
  • Previous workers have taken sequences of 2D images and assessed the position of each one in space. They have then calculated the position of every data point in each 2D image and assigned its data value to a nearest grid position on a 3D Cartesian grid. At best they have had one single data value (subject to speckle and shadow artifacts) for each grid position and where there was no value they have drawn information from adjoining positions using a nearest neighbour procedure.
  • the positional information used has relied on either all the 2D planes being parallel or, for freehand scanning, that the image plane has been parallel to the axes of the electromagnetic positioning device attached to the ultrasound wand. Neither of these two assumptions is desirable or even valid in many cases.
  • the 'path' had a mean pixel value of 40, which in order to achieve continuity based on the 2-D image would have required the window width to be set at 8 or 9, in other words grey level limits of 31.5 - 48.5.
  • the S-VHS videotape provided a cost effective and efficient archive as the equivalent of around 300 Gbytes of information could be stored on each tape. Data acquired, free- running, can be subsequently extracted as data sub-sets, without the need to re-interrogate the subject. This ability to post process video tape archives and extract data sub-sets from specified periods of the cardiac cycle will allow comparison of co-registered structure shape, volume and texture patterns during these different phases.
  • the system described here provides a means to monitor volume accurately from conventional 2D ultrasound scanning techniques, the basic equipment and software used being for the most part commercially available. However it is the customised hardware and computer software described in this paper that have developed this prototype laboratory tool into a system sufficiently flexible and efficient to propose starting its use in the clinical environment. In our laboratory some 2,000 3D reconstructions of ultrasound data sets have been made with this system in the past 12 months, each providing 3D images of potential clinical relevance.
  • the increased data density has had a second benefit in that the precision of the location of individual data points in each 2-D image has been greatly improved: this is because the DELTA matrix obtained using a point phantom has been optimised using a linear least squares minimisation approach with this vastly over-determined data set, giving precision to the level of 0.01 degrees and 10 microns for each of the three beam axes and probe coordinates respectively. Previous workers, using only individual data points in their optimisations have had to stop at an rms deviation of 1.0 mm or more.
  • the 2D Ultrasound images will be correctly registered in the 3D transmitter frame XYZ and the discrete images will converge into a single object.
  • the registration of the 2D ultrasound images in 3D will not be perfect, so that the individual discrete objects will differ in shape and, in general, volume and will not converge exactly.
  • the degree to which the 2D ultrasound images are mis-registered and the individual discrete objects are distorted and mis- positioned depends partly on the estimate of ⁇ but also on the translations and orientations that define the EPOS values (M' s) for the images making up the particular scan subset.
  • Deviations of the refined ⁇ from the true optimum ⁇ will also contribute to positional uncertainty.
  • the ⁇ transformation has a profound effect on the quality and accuracy of the 3D reconstruction and a considerable degree of precision is required.
  • the over-determined system of equations has been found extremely well behaved, yielding a single minimum and a long range, almost quadratic dependence of the criteria function on each of the six parameters of ⁇ .
  • Refinements from widely differing starting trial ⁇ parameters converge to the same solutions for ⁇ and P(XYZ) . These estimates are better than can be measured directly, (angles to within 0.01 degrees, positions to within 10 microns) .
  • compounding reduces the effect of speckle while the TOSCA product allows a profile of intensities to be obtained across a representative slice of the data block. This allows an appropriate level and window to be selected for the region of interest for segmentation throughout the block and reference features to be used to provide cross correlation of structures and their intensities between machines and subsequent data sets. Without this approach grey-level segmentation f ai l s .
  • 17 to 60 data values/grid point means 17 - 60 separate data planes contributing (no plane providing more than one point) it is easy to see that the adverse effect of one or two "faulty" planes is rapidly diminished. This has not been available to previous workers .
  • segmented objects may be displayed and rotated to provide views that are unobtainable in normal scanning. Filters and colour maps may be applied to highlight features or textures. Any plane required through the data block may be displayed, and positioned relative to segmented 3D objects. This will have relevance when comparing ultrasound with histological sections or co-registered images from other modalities such as MRI or CT. Data may be mapped to a regular surface such as a model of an organ or structure. This may provide an ability to locate abnormalities in shape, texture or provide a map of distribution of a feature.
  • Fine D Three-dimensional ul trasound imaging of the gallbladder and dilated biliary tree : reconstruction from real - time B-scans . Br. J. Radiol 64 : 1056 1991 .
  • M and ⁇ each have rotational and translational components defined in three dimensions by 3 angles and a vector. It is convenient to use an abbreviated form of homogeneous co-ordinate notation (MAXW46 & 51 / 176 & 175 Roberts 233 and Newman & Sproull) . Such transformations can then be represented symbolically by a 2 x 2 partitioned matrix of the form
  • Rotation matrices are represented by matrices that are Unitary and have several important properties that are made use of in the main text. Specifically :
  • the generalised rotation matrix has three mutually orthogonal eigen vectors, one real and coincident with the rotation axis, the other two complex.
  • the eigen values and vectors are : Eigen value : 1.0 Eigen vector : (a,b,c) Eigen value : Cos (T) + i Sin( ⁇ ) Eigen vector : 0.707 (VA - i VB)
  • VA is the normalised vector formed from the cross product of (a,b,c) with the vector (1,0,0) and VB is the normalised vector formed from the cross product of (a,b,c) with VB . 5)
  • the POLHEMOUS transformation, M must be defined in terms of the parameters available from the device, three positional and three directional. Following the conventions set out in the POLHEMUS manual ( reference ??) the rotation component Rot is defined by the 3 Eulerian angles designated as Azimuth, Elevation and Roll (here shortened to a, e and r) representing Z, Y and X axis rotations. Noting that rotation matrices do not, in general, commute so that the order is critical
  • Rot (a,e,r) Z(a) * Y(e) * X(r) or, writing this product out in full as a 3 x 3 matrix
  • Rot (a , e, r) 1 sa *ce sa *se *sr+ca * cr sa *se *cr-ca "sr -se ce *sr ce *cr where sa, ca, se, ce, sr and cr are the sine and cosine of the angles a, e and r respectively.
  • the progression of atherosclerotic lesions in the Watanabe rabbit and the modification of lesion components by Probucol treatment were sequentially monitored over a 30 week period using 3D reconstructed ultrasound image data.
  • the 3D ultrasound data generated by spatial orientation and gridmapping of a large number (>500) of image frames from conventional 2D hand held ultrasound interrogation, was heavily compounded over the angles of insonation used, improving both signal to noise and structure coherence. This compounding of high data density was found to allow accurate and reproducible greyscale analysis and segmentation of the data, providing sequential volume measurements of structure components identified and selected on the basis of differing greyscale intensity.
  • volume measurements from such images normally require manual segmentation or model fitting algorithms as the data within the single images is often contaminated by speckle generated throughout the system and is not of sufficient integrity to allow automated greyscale segmentation techniques (Nelson , 1995 , 1996 ; Pretorius, 1995) .
  • Multiple observations from a variety of interrogation angles of any single point within a volume, and compounding of these observations, allows the speckle in ultrasound images to be reduced, with significant improvements in image quality (Nelson, 1996) . Shadowing may also be reduced or eliminated if a sufficiently wide range of angle is utilised during interrogation.
  • the various components of atherosclerotic plaque possess differing arrangements and densities of cellular and particulate matter that act as reflectors and scatterers of ultrasound to differing degrees.
  • the textural appearance within the ultrasound image will therefore depend on the proportions of reflectors and scatterers, their size, arrangement and density, also the frequency of the ultrasound beam and the angle at which it is presented. This textural information is well recognised and often applied in the subjective assessments of plaque structure, allowing differentiation of plaque into four or five classes indicating the homogeneity of the plaque depending on the degree and distribution of calcification, fibrosis and lipid content.
  • lipid component of such plaques having few reflector or scatterer particles, may be identified by its echolucent nature and appearance as low level greyscale attribution in the ultrasound image (Geroulakos et al . ; 1993, 1994) . Differentiation of such low level greyscale attribution from the blood filled lumen of a blood vessel is however often difficult.
  • the angle dependence of ultrasound image generation makes the sequential comparison of textural information extremely difficult. Obtaining identical views, using precisely the same acquisition angles, from different interrogations in vivo, is virtually impossible.
  • the problems associated with quantification of atherosclerotic plaque progression may be overcome by the use of three- dimensional reconstruction and volumetric analysis of compound data generated from freehand two-dimensional ultrasound imaging as recently described by this group ( Allott,1996) .
  • Volume rather than linear dimensions allows structures, whether they be regular or irregular in shape, to be accurately measured at each interrogation.
  • the various insonation angles used to interrogate the structure during freehand scanning may be compounded to provide more reproducible reflection intensity data. The segmentation of such data throughout the volume of interest more accurately represents the characteristics of the plaque .
  • the Watanabe heritable hyperlipidaemic (WHHL) - rabbit (Watanabe, 1980) has a mutation in the LDL-receptor gene (Kita, 1983) which is similar to that seen in human familial hypercholesterolaemia . These animals develop a persistent increase in plasma cholesterol levels, resulting in an early and accelerated atherosclerosis, including plaque formation in the aortic arch.
  • the WHHL-rabbit has been used by a number of workers to study various aspects of atherosclerotic plaque progression in this vessel ( Carew,1987; Daugherty, 1991 ; Braesen, 1995 ;0' Brien, 1991 ;Nagano , 1992) .
  • WHHL Watanabe Homozygous Hyperlipidaemic Rabbits 12-14 weeks of age (Charles River, UK Ltd.) were housed under standard conditions with free access to water. Lipid content of the diet was controlled by providing 150 g of normal rabbit chow per day, supplemented with 500 g of assorted fresh vegetables and free access to sweet grass hay. The animals were weighed at weekly intervals to ensure no loss of bodyweight . At 5 week intervals the animals were monitored for serum cholesterol and the aortic arch examined by conventional 2D ultrasound.
  • the animals were first sedated with 0.3 ml fentanyl (Hypnorm, Janssen) intramuscularly (im) and 2.5 ml blood withdrawn from an ear vein for serum cholesterol determinations using a commercial colourimetric kit ( Boehringer Mannheim) .
  • the animals were then anaesthetised by intravenous (iv) injection of 0.25 ml midazolam (Hypnovel, Roche) and the aortic arch of each animal interrogated by ultrasound.
  • iv intravenous injection of 0.25 ml midazolam
  • the aortic arch of each animal interrogated by ultrasound.
  • the animals were allocated to two groups.
  • Group 1 received standard diet containing 1% olive oil while group 2 received 0.5% w/w Probucol (Sigma) dissolved in olive oil added to the diet.
  • Animals were allocated on the basis of bodyweight, serum cholesterol levels and appearance of aortic arch as determined by ultrasound, so that all selection criteria were evenly distributed between the two groups. The animals were maintained on the diets for a further 30 weeks. Blood samples for serum cholesterol and ultrasound examinations from which 3D reconstructions were made were continued at 5 week intervals for the remainder of the experiment .
  • a Toshiba SSH 140a ultrasound scanner fitted with a 7.5 MHz linear array transducer was used throughout. Interrogation parameters were pre-set on the scanner by optimisation of images at the start of the experiment. The pre-set was used for all subsequent examinations, only gain and focus settings being optimised for each animal to provide a clear view of the aortic arch.
  • the scanning transducer was fitted with an electromagnetic position and orientation sensor (EPOS -Polhemus - Isotrak II ) . This provided the transducer position and orientation information to enable reconstruction of the freehand 2D ultrasound images into a compounded 3D data block by off line computer processing.
  • EPOS -Polhemus - Isotrak II electromagnetic position and orientation sensor
  • Positional information from the EPOS was processed by a second hardware module and recorded on the second S-VHS recorder audio channel. These signals were later used for post processing selection of ultrasound image frames and spatial orientation.
  • the videotapes containing the image data, positional information and ECG record, were stored until 3D reconstruction and analysis .
  • the image frames from the data set were then further selected utilising a customised motif application on the basis of their temporal location within the ECG cycle.
  • Each selected data set contained approximately 500 image frames from the same portion of the cardiac cycle, but from a variety of interrogation angles.
  • the greyscale intensity data from the selected frames were then extracted, spatially oriented and gridmapped into a regular 3D grid to produce a compound 3D data block.
  • These data blocks were transferred onto an IBM RS 6000 workstation and analysed using IBM 3D automated segmentation and analysis software TOSCA. (TOols for Segmentation, Correlation and Analysis) .
  • Each data set was visualised in TOSCA.
  • a region of interest that avoided the bone shadows cast by the ribs and clavicle was outlined to include the lumen of the aortic arch.
  • Fig 1 Semi-automatic statistical greyscale segmentation using the TOSCA volume growing algorithm (Cootes, 1994) , at fixed window and level settings, selected by using the greyscale profile facility, was used to identify different structures within the images. Segmentations were performed through the outlined region of the 3D data set with the parameters : - a) level 32 + 2 to delineate the lumen of the vessel.
  • level 42 + 4 to delineate hypoechoic structures adjacent to the vessel wall protruding into the vessel lumen b) level 42 + 4 to delineate hypoechoic structures adjacent to the vessel wall protruding into the vessel lumen
  • level 60 ⁇ 10 to delineate structures adjacent to and within the vessel wall and surrounding tissue d) level 90 ⁇ 20 to delineate hyperechoic areas.
  • the excised aortic arches from four animals from each treatment group were chosen at random for histological processing.
  • the arches were placed in O.C.T Tissue-Tek embedding compound (Miles Inc.), frozen at -28°C, trimmed and prepared for sectioning using an Klu Scientific Cryotome.
  • the arches were orientated so that sequential cross sections were taken proceeding from the heart end of the vessel .
  • Three successive lO ⁇ sections were taken at 170 ⁇ intervals along the entire length of the aortic arch finishing near the carotid sinus.
  • the sections were mounted on 3-aminopropyl- triethoxysilane (Merck-Schuchardt) coated slides.
  • the second slide of each triplicate was immediately stained with Oil Red 0 (Sigma) to identify lipid within the plaque and counterstained haematoxylin (Shandon) .
  • the remaining slides were snap frozen and stored at -70°C for immunohistochemistry or cytochemical investigation.
  • each section occupied by unstained normal tissue or Oil Red 0 stained tissue was determined by planimetry. Each section was viewed on a colour video monitor using a Zeiss microscope equipped with a Panasonic colour video camera. Areas were traced onto transparent film and subsequently digitised using a TDS Bit Pad. A calibration grid was used to determine the magnification of the tracings and calculate the area measurements. The area values for normal tissue and lesion for each sequential lO ⁇ section was obtained. Volume measurements encompassing the whole arch were calculated assuming a linear relationship for the 190 ⁇ gap between successive sections. The data was expressed as segmented volume (mm 3 ) /cm 3 total tissue.
  • Immunohistochemist The frozen sections stored at -70°C were used to determine the cellularity of the lesions, in particular the relative numbers of smooth muscle cells and macrophages were assessed by the following immunohistochemical approach. Sequential adjacent pairs of slides, covering the entire length of the aortic arch, were selected. The first slide of each pair was probed with a mouse monoclonal antibody (Mab) against rabbit macrophages, RAM-11. This antibody reacts with a macrophage cytoplasmic antigen and has been used extensively in studies investigating the cellular components of atherosclerotic lesions in rabbits.
  • Mob mouse monoclonal antibody
  • the slides were incubated at 21°C with Immunopure peroxidase supressor (Pierce and Warriner) for 1 hour , rinsed in TBS for 5 mins and incubated with 1:50 normal rabbit serum in TBS for 40 minutes. This was followed by incubation for 1 hour with 1:40 Mab anti rabbit macrophage RAM-11 (IgGl) . They were then rinsed for 5 minutes in TBS and incubated with 1:40 Rabbit anti-mouse peroxidase conjugate F(ab) 2 IgG (Serotec) for 1 hour. After a further 5 minute rinse in TBS the slides were developed for 10-15 mins using Metal enhanced 3,3' Diamino Benzidine Tetrahydrochloride (Pierce and Warriner) .
  • the second slide of each pair was probed with an anti- a smooth muscle cell actin (Anti ⁇ -SM-1) which recognises the 42 kD ⁇ -smooth muscle isoform of actin but does not react with other isoforms present in endothelial cells.
  • This actin isoform is the major actin component of vascular tissue in the aorta and is an ideal probe for smooth muscle cells in atherosclerotic lesions (1 reference) .
  • Slides were incubated at 21 °C for 1 hour with alkaline phosphatase suppressor (Pierce and Warriner) , rinsed for 5 mins in Tris buffered saline (TBS) and then incubated with 1:50 normal mouse serum in TBS for 40 mins.
  • TBS Tris buffered saline
  • Frozen sections of rabbit spleen, bladder and brain were simultaneously probed with the same antibodies to provide positive and negative control slides.
  • the atherosclerotic lesions from adjacent sections were then assessed for smooth muscle cell and macrophage content using an arbitrary scoring system from 0 - 5. These arbitrary scores were then converted to relative percentage area values. The proportion of smooth muscle cells to macrophages found in each slide was calculated by comparing matched adjacent areas and a mean score for each parameter was calculated by averaging all the scores for all the lesions from individual rabbits.
  • FIG. 3 A single slice through the aortic arch of one of the Watanabe rabbits from within a compound 3D data set is shown in Figure 3.
  • the right hand panel shows a pixel by pixel profile of the grey scale intensities between point A above the superior curve of the arch , through the vessel wall (w) , lumen
  • FIG. 6 A Histological section through lesion in Probucol treated WHHL Rabbit showed increased smooth muscle cell -Ill- population in lesion cap - Anti a SMC actin conjugated with alkali phosphatase anti SMC actin.
  • hypoechoic region of interest segmented at the 42 ⁇ 4 greyscale intensity, was found toward the edges of the lumen but distinct from the wall itself. Investigation of the contributing values from the 2D image frames proved that this compartment of greyscale value was not a consequence of signal averaging between the low lumen value and higher wall value (unpublished observations) .
  • This region was considered to relate most closely with the macrophage rich, lipid laden portions of the lesion for several reasons. Firstly, it is well recognised that lipid is poorly echogenic and would be associated with regions of the image having low level greyscale attribution. Secondly, the volume of the segmentable structure at this grey scale intensity was demonstrably smaller in normal rabbit aorta ( Fig 9a) .
  • the remaining region of interest had an intermediate grey scale intensity of 60 ⁇ 10, and while also being associated with the lesion, was also to be found near to and merging with the vessel wall .
  • the volumes of this echogenic region in the two groups produced completely the opposite results to those of the hypoechoic regions.
  • the control animals showed no increase in this region, whereas the Probucol treated animals demonstrated a time dependent increase.
  • These changes in ultrasound image suggested that the Probucol treatment was responsible for the development or recruitment of material which possessed improved echogenic properties within the plaque.
  • Immunohistochemical analysis of the lesions showed an increase in smooth muscle cells in the Probucol treated animals that showed a linear correlation with the echogenic volumes.
  • the Watanabe rabbit proved useful in developing the technology to reconstruct ultrasound images in three dimensions.
  • Rabbit models however, have lesions of very small dimensions and have a limited potential to represent experimentally the full range of features normally associated with human atherosclerotic plaques .
  • the animal lesions are predominantly lipid laden, analogous to the human type 1 plaque (Geroulakos, 1993,1994). Heterogeneous and more echogenic plaques are seldom seen. Nevertheless, it is now widely accepted that it is those, lipid laden plaques which, because of their inherent instability, are the culprit lesions associated with cardiovascular events . The poor differentiation between blood and lipid making these the most difficult to identify and measure in conventional ultrasound images. The present study, therefore, demonstrates that even small culprit lesions may be both identified and quantified reproducibly, and that cellular changes can be monitored in such lesions by volumetric segmentation of selected grey scale intensities. The 3D compounding of a large number of sampled images from a pre-calibrated scanner proved extremely reliable and appeared to eliminate the problems normally associated with such segmentation in both the 2D and multi-slice 3D environment.
  • Respiration has been identified as a potential source of such movement and respiratory gating has subsequently been introduced into the post processing selection of images in addition to the existing ECG selection criteria.
  • the 3D ultrasound system developed at Zeneca was used to interrogate the carotid artery bifurcation in four volunteers on two occasions. There was good correspondence between the reconstructions for each individual in both visualised geometry and measured lumen volume. The four volunteers however differed in the visualised geometry of the vessel bifurcation. One volunteer was seen to have a small area of vascular pathology which was recognised in both scans.
  • the surface rendering of the lumen segmentation showed differences in geometry between the four volunteers. Each scan appeared to have identifying characteristics. Subject 1 had a regular bifurcation, subjects 2 and 3 a more acute divergence into the internal carotid with a much larger external carotid artery, subject 2 having some pathology around the ostium to the internal carotid. Subject 4 had a long length of incomplete septum between the internal and external carotid arteries. The repeat scan in each volunteer showed similar geometry to that seen in the first scan. (Fig. 11)
  • the consistent volume measurements over a 1cm length of the carotid artery would allow sequential measurements to be used for detection of disease progression or regression.
  • Gardner J.E.;Lees W.R. Gillams A. Volume imaging with ultrasound. Radiology 1991; 181(P):133. Geiser E.A.; Ariet M. ,- Conetta D.A. ; Lupkiewcz S.M.;
  • Watanabe heritable hyperlipidemic rabbit an animal model for familial hypercholesterolemia. Proc .Nat1.Acad. Sci 1987 84: 5928 - 5931.
  • Salonen J.T. Salonen R Ultrasonographically assed carotid morphology and the risk of coronary heart disease. Arterioscler. Thromb 1991;11: 1245-1249. Salonen J.T. Salonen R. Ultrasound B-Mode imaging in observational studies of atherosclerotic progression. Circulation 1993; 87: ⁇ suppl II ⁇ II -56 - II- 65.
  • Watanabe Y Serial inbreeding of rabbits with hereditary hyperlipidemia (WHHL-rabbit) incidence and development of atherosclerosis. Atherosclerosis 1980; 36: 261-268.
  • WHHL-rabbit hereditary hyperlipidemia
  • Fine D Three-dimensional ultrasound imaging of the gallbladder and dilated biliary tree: reconstruction from real - time B-scans. Br J Radiol 1991; 64: 1056-????.
  • Shattuck DP von Ramm OT. Compound scanning with a phased array. Ultrason Imag 1982; 4:93-107. Sivewright GJ, Elliot PJ. Interactive region and volume growing for segmenting volumes in MR and CT images. Medical Informatics 1994; 19:71-80.

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Abstract

L'invention concerne un procédé et un dispositif servant à reconstruire en trois dimensions une image d'un objet balayé en deux dimensions une pluralité de fois afin de produire une pluralité de tranches d'image sous des angles d'inclinaison différents. On enregistre et on mémorise ces tranches sur un support d'enregistrement, par exemple, une bande vidéo, sur laquelle les tranches sont enregistrées en séquences avec au moins une référence identifiant les tranches de données d'image comme correspondant à au moins un paramètre physique variable, tel que le positionnement, l'électrocardiogramme ou le cycle respiratoire. On traite ces tranches de données d'image bidimensionnelle de manière à créer un réseau tridimensionnel de points contenant des valeurs de données basées sur au moins une partie d'un objet balayé, ces valeurs de données d'image étant insérées au niveau des points du réseau en conséquence du traitement des tranches de données d'image bidimensionnelle en fonction de ladite référence au moins. On effectue, de préférence, ce balayage au moyen d'une technique de balayage à ultrasons à main levée et on peut le mettre en application afin de déterminer de façon non invasive un état à l'intérieur du corps d'un mammifère.
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CA2269323A1 (fr) 1998-06-04
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