Automated CT marker segmentation for image

INSTITUTE OF PHYSICS PUBLISHING

PHYSICS IN MEDICINE AND BIOLOGY

Phys. Med. Biol. 46 (2001) N269–N279

PII: S0031-9155(01)26583-X

NOTE

Automated CT marker segmentation for image registration in radionuclide therapy Periklis Papavasileiou, Glenn D Flux, Maggie A Flower and Matthew J Guy Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Trust, Downs Road Sutton, SM2 5PT, Surrey, UK E-mail: [email protected]

Received 6 July 2001, in final form 12 September 2001 Published 14 November 2001 Online at stacks.iop.org/PMB/46/N269 Abstract In this paper a novel, automated CT marker segmentation technique for image registration is described. The technique, which is based on analysing each CT slice contour individually, treats the cross sections of the external markers as protrusions of the slice contour. Knowledge-based criteria, using the shape and dimensions of the markers, are defined to enable marker identification and segmentation. Following segmentation, the three-dimensional (3D) markers’ centroids are localized using an intensity-weighted algorithm. Finally, image registration is performed using a least-squares fit algorithm. The technique was applied to both simulated and patient studies. The patients were undergoing 131 I-mIBG radionuclide therapy with each study comprising several 99mTc single photon emission computed tomography (SPECT) scans and one CT marker scan. The mean residual 3D registration errors (±1 SD) computed for the simulated and patient studies were 1.8 ± 0.3 mm and 4.3 ± 0.5 mm respectively.

1. Introduction Different imaging modalities provide different but complementary information. Single photon emission computed tomography (SPECT) images provide functional information whereas computed tomography (CT) images are superior in revealing the anatomy. Both functional and anatomical information are combined to aid diagnosis and improve the outcome of interventional and treatment-planning procedures. Image registration is required to enable mapping between the co-ordinates of one imaging space to the other. In biologically targeted radionuclide therapy (BTRT), administration of a 131I-labelled tumour-specific agent is followed by a series of sequential SPECT scans to characterize the dose distribution to both tumour and normal organs. Registration of the sequential 131I SPECT scans is essential to allow 3D dose calculations to be generated. CT scans are acquired, 0031-9155/01/120269+11$30.00

© 2001 IOP Publishing Ltd Printed in the UK

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prior to the administration of the radioactivity, to allow delineation of volumes of interest (VOIs) around tumour sites and normal organs for which 3D dosimetry is to be performed. Furthermore, 3D dosimetry requires accurate SPECT quantification and registered CT scans are potentially useful for attenuation correction of the SPECT scans. Several image registration methods have been developed and used in medical imaging including voxel-based and point-based methods (Maes et al 1997, Eberl et al 1996, Yu et al 1995, Woods et al 1993, Hill et al 1991). Voxel-based methods rely on computing the transformation parameters by optimizing an appropriate similarity criterion, whereas pointbased approaches require the determination of two sets of corresponding points in order to compute the transformation matrix by a least-squares fit. Those points can be either intrinsic, e.g. anatomical/functional landmark points or extrinsic, e.g. centroids of external markers. Point-based registration based on external markers is widely used in radionuclide therapy mainly because the lack of anatomical information in the SPECT images renders voxelbased registration difficult to implement. External markers are attached to the patient’s skin and the process involves localization of the 3D markers’ centroids on both scans to be registered. Localization of the markers’ centroids is usually done manually. A semi-automated method was described by Flux (1995) which consisted of providing an initial estimate for the centroid and then employed an intensity-weighted algorithm to compute the centroid. An automated marker centroid localization process has recently been proposed for SPECT marker centroid localization (Papavasileiou et al 2001). Wang et al (1996) described an automated method based on automated segmentation of markers fixed on the patient’s skull (CT and MRI head scans) during interventional neurosurgical procedures. In this paper, we describe a new automated segmentation method for CT external markers attached to the patient’s skin. The method analyses each CT slice contour individually and treats the markers’ cross sections as protrusions of the slice contours. Knowledge-based criteria are used to identify the markers and localization of the markers’ centroids is carried out using an intensity-weighted algorithm. The technique is validated on both simulated and patient studies. The algorithms were written in C and IDL (Research Systems Inc., Colorado, USA) and are included in the Royal Marsden Dosimetry Package (RMDP) (Guy 2000). 2. Methods 2.1. Markers The external markers used for CT scanning consist of Perspex discs, 15 mm in diameter and 12 mm deep, containing a spherical cavity of 10 mm diameter, which is stopped with a small nylon screw and is filled with BaCl2. Two contrast agents are available in our department, BaCl2 and BaSO4. Even though BaCl2 is poisonous, it does not precipitate like BaSO4 does. Therefore, BaCl2 was chosen to be the more appropriate as a CT marker contrast agent. The diameter of marker cavity matched the slice thickness of the CT scans. The size of the SPECT markers is smaller than that of those used on CT, though the geometry and the shape are the same. The reason for that discrepancy is that the scatter and the poor resolution of the SPECT scans result in the marker being shown on several slices even with smaller marker size. Skin markings are made on the patient skin to allow accurate and reproducible placement of the external markers for the 99mTc SPECT acquisition. The markers are taped on the patient’s skin before each scan. The number and the distribution of the markers have to ensure that the tumour as well as normal organs, for which dose distributions have to be generated,

Automated CT marker segmentation for image registration in radionuclide therapy

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START for each slice Grey-level thresholding Generation of the 3D markers connected components

Contour extraction N

Computation of the 3D marker centroids.

Y

if contour sampling incomplete

Define a contour segment of 10 mm length.

END Apply the Collinearity criterion to test if the contour-segment voxels lie on a line. N

if Collinearity

Y Marker identification criterion. - Define a vector perpendicular to the sampled contour segment. - Define 4 artificial segments along the perpendicular vector parallel to the linear contour segment.

The intersections of the parallel segments with the contour are counted. If #intersections is above a threshold of 6, a marker is identified.

Figure 1. Flowchart of the automated CT marker segmentation process.

are included within the patient volume defined by the markers. The size of the patient is also a factor affecting the number of markers; 5–10 markers are used for marker registration at our centre. 2.2. CT marker segmentation The automated marker segmentation algorithm is an iterative algorithm analysing each CT slice individually. The marker segmentation and centroid localization process consists of the following steps, which are illustrated in the flowchart in figure 1: 1. Grey-level thresholding. The patient image is separated from the background using greylevel thresholding. The threshold is computed automatically for each individual slice using a method described by Bae et al (1993). The main assumption of this approach is that the grey levels within the patient region differ significantly from those in the background. The mean intensity of the voxels, along a horizontal line, from the edge to the centre of the image is computed cumulatively. Since the intensity of the patientregion voxels is significantly higher than that of the background, the mean intensity is relatively constant until the patient-region voxels are included. The mean intensity of the voxels from the centre to the edge is also computed and it is relatively constant until the background voxels are included. At the boundary between the patient region and the background the maximum difference between the two mean intensities occurs. At that point, the mean intensities of the patient and background regions are computed and the threshold is given by the average of those two intensities. All voxels above the threshold

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are assigned an intensity of 1, whereas all voxel below threshold are assigned an intensity of 0. 2. Contour extraction. Following grey-level thresholding, the contour of each slice is extracted based on an edge-tracking algorithm first described by Henrich (1979). The location of the starting patient-region edge voxel is found by sampling a horizontal line, on the thresholded image, from the right-hand image edge to the centre and identifying the first non-zero voxel. That voxel is the starting point for contour extraction. Edge tracking is carried out anti-clockwise. The eight-voxel neighbourhood of the starting voxel is examined anti-clockwise for the first non-zero voxel; this voxel is marked as the next contour voxel and the searching process is repeated in its eight-voxel neighbourhood. The algorithm ‘follows’ the patient-region edge until it reaches the starting voxel (i.e. a closed contour is obtained). 3. Contour sampling. The markers are treated as protrusions of the slice contour with a flat surface parallel to skin surface. The shape of the markers’ cross sections is close to rectangular but the exact height, width and shape depends on the orientation of the marker relative to the CT x-ray beam. The height of the markers’ cross sections ranges between 12.0 and 13.0 mm, whereas their width ranges from 9.0 to 12.0 mm. From each contour voxel, a contour segment of 10.0 mm length is defined; the number of voxels comprising the sampled contour segment depends on the image digital resolution (voxel size) and on the curvature of the sampled segment. 4. Collinearity criterion. A collinearity criterion is applied (Pavlidis, 1982) to test if the sampled contour-segment voxels lie on a straight line. A line is drawn between the end-points of the sampled segment and is given by the equation, x(yj − yk ) + y(xk − xj ) + yk xj − yj xk = 0

(1)

where (xj , yj ) and (xk , yk ) are co-ordinates of the two end-points of the segment. If a contour voxel with co-ordinates (u, v) does not lie on the line given by equation 1, then its distance from the line is equal to the magnitude d/L where d and L are given by: d = u(yj − yk ) + v(xk − xj ) + yk xj − yj xk L = (yj − yk )2 + (xk − xj )2 .

(2) (3)

The arc length, La , of the sampled segment is also computed: La =

k

(xi − xi−1 )2 + (yi − yi−1 )2 .

(4)

i=j +1

Collinearity can be established by evaluating either the d/L, that is, setting a maximum threshold on the distance of any point from the fitted, or the L/La , that is, comparing the length of the arc, along the point sequence, with the length of the line. We chose the L/La criterion because it takes into consideration all the points in the segment. If the ratio La /L is less than 1.1, collinearity is confirmed (Pavlidis, 1982) and the process continues to step 5. Otherwise the process returns to step 3 to sample another contour segment of 10.0 mm length. An La /L value of 1.1 means a deviation of 10% from a perfect linear segment (La /L value of 1.0). 5. Marker identification criterion. A vector perpendicular to the linear contour segment is defined. Artificial segments parallel to the contour segment are then generated along the perpendicular vector as shown in figure 2(a). Those segments are generated only on one side of the sampled contour segment; the choice of side is pre-defined since the direction of contour generation is known. The number and the length of the artificial segments depend


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contrast-filled spherical cavity

C

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intersection normal to segment AB

body contour

(a)

A

E

End point of the 2D marker polygon

B

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Starting point of the 2D marker polygon

body contour

(b)

Figure 2. (a) Graphic illustration of the CT marker identification criterion. AB and CD are contour segments, which satisfy both the length and the collinearity criteria. Only AB belongs to a marker because it satisfies the intersection criterion. (b) Generation of the 2D marker polygon defined by the sampled segment AB and the intersections, S and E, of the artificial segment furthest away from AB.

on the height and width of the marker respectively; four parallel, artificial segments, 3.0 mm apart, with length of 14.0 mm are defined for the linear contour segment. The intersections of these artificial segments with the slice contour are counted and if their number exceeds a threshold of six (three pairs of intersections), a marker is identified. The voxels at the intersection furthest away from the linear contour segment mark the endpoints of the two-dimensional (2D) marker representation. A 2D polygon is defined by the sampled contour segment and the above two endpoints as shown in figure 2(b). The voxels enclosed in that polygon are saved. The process returns to step 3 to continue the contour sampling from the contour voxel corresponding to the endpoint of the 2D polygon. When contour sampling is completed the process continues to step 6. 6. Generation of 3D marker connected component. Following analysis of all CT slices, the segmented markers’ voxels are copied to a temporary binary file where all marker voxels are assigned an intensity of 1. 3D six-neighbourhood connectivity is employed to allow generation of the marker 3D-connected components; the four immediate 2D neighbours within the slice and the two immediate ones in the neighbouring slices are considered to establish connectivity.

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a)

b)

c)

d)

Figure 3. (a) Slice through a CT marker. (b) Slice contour. (c) The linear marker segment and the perpendicular vector are superimposed on the slice contour. The two highlighted contour voxels correspond to the start and end of the 2D segmented marker polygon. (d) The segmented marker.

7. Marker centroid computation. The marker centroid is determined using the intensities in the original image of the voxels comprising the marker 3D-connected component. Initially, the arithmetic centroid of the segmented marker is computed which serves as an initial estimate of the geometric centroid. Then, an intensity-weighted algorithm is applied to calculate the marker centroid more accurately. An example of the automated marker segmentation process is shown in figure 3, where the main steps of the algorithm are illustrated. 2.3. Marker registration One-to-one correspondence between the two CT and SPECT marker centroid sets (marker matching) was established based on an automated method (Papavasileiou et al 2001). The least-squares fit algorithm developed by Arun et al (1987) was then employed to compute the six transformation parameters (X, Y, Z translation, XY, XZ and YZ rotation) required to


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register the two marker sets. Scaling of the image data was carried out automatically using the known voxel sizes of the scans to be registered. 2.4. Simulated study In order to validate the CT marker segmentation process, a simulated study was generated. Nine copies of a CT marker scan, which was acquired as part of a patient therapy study and in which six markers were used, were generated. These were subjected to a range of transformation parameters (0–20.0 mm and 0–10◦ for translation and rotation respectively). The 3D matrix dimensions of the CT scan were 256 × 256 × 15 (1.5625 × 1.5625 × 10.0 mm3). Each of the ten scans of the simulated study was registered to the remaining nine resulting in 45, i.e. (n1 (n2 − 1))/2, registration pairs, where n1 = n2 = 10. The robustness of the collinearity criterion was also assessed with the simulated study. The segmentation process was repeated for La /L value of 1.05 and 1.08, respectively, and the number of markers segmented for each of the above values was computed. 2.5. Patient studies Two different patient studies (A and B) were used to test the automated CT marker segmentation algorithm described in this paper. Both studies analysed were abdominal studies. Both patients had received therapeutic doses of iodine-131 metaiodobenzylguanidine (131I-mIBG) for treatment of neuroblastoma (Hoefnagel et al 1987). Each patient study consisted of one CT and five 99mTc-SPECT marker scans. The CT scans were acquired, prior to the administration of the therapeutic activity, on a Siemens DR Somatom CT scanner. The 99mTc-SPECT scans (matrix 64 × 64 × 50, voxel dimensions 6.4 × 6.4 × 6.4 mm3) were acquired on a General Electric STARCAM camera (General Electric Medical Systems, Milwaukee, WI) with a highenergy, high-resolution collimator; the high-energy collimator is required because an 131I scan is acquired after the 99mTc marker scan. The SPECT scans were then interpolated to 256 × 256 × 50 matrices (1.6 × 1.6 × 6.4 mm3). The matrix dimensions of each CT slice were 256 × 256; the voxel dimensions were 0.78 × 0.78 × 10.0 mm3 and 1.5625 × 1.5625 × 10.0 mm3 for studies A and B respectively. The CT image planes were contiguous. SPECT marker segmentation and centroid computation were carried out using an automated approach (Papavasileiou et al 2001). Each 99mTc-SPECT marker scan was registered to the CT scan. Seven and six markers were used for studies A and B respectively. 3. Results 3.1. Simulated study The automated segmentation process resulted in all six markers being segmented on each registration pair. In order to test the accuracy of the registration process, the 3D registration residual error was computed by measuring the root-mean-square (rms) distance between coordinates of pairs of corresponding marker centroids following image registration. Subvoxel registration accuracy was achieved, with a mean 3D residual error (±1 SD) for the 45 registration pairs of 1.8 ± 0.3 mm. The value of the collinearity criterion was shown to affect the number of identified markers (figure 4) with a value less than 1.1 resulting in failure of the algorithm to identify all the markers.

P Papavasileiou et al Number of segmented markers

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Scan index

Figure 4. Number of segmented markers in the simulated study for three different values of the collinearity criterion, La /L, 1.05 (♦), 1.08 () and 1.10 () respectively. Scan index refers to the corresponding scan analysed in the simulated study.

3.2. Patient studies All 13 markers, 7 for study A and 6 for study B, were segmented by the automated segmentation algorithm. The difference in CT voxel sizes on the two studies, 0.78 × 0.78 × 10.0 mm3 and 1.5625 × 1.5625 × 10.0 mm3 for studies A and B respectively, did not affect the sensitivity of the segmentation process. As with the simulated study, the accuracy of the segmentation process was assessed by computing the 3D residual error of the registration process. A value of 4.3 ± 0.5 mm (mean ±SD) for the 3D registration error was calculated. The computation of the 3D residual error in patient studies made the necessary assumption that the markers are in the same position irrespective of the modality used and/or the time of the scan. Even though we were confident with the process of repositioning the markers, for either SPECT or CT acquisition, contributions to the registration error due to the difference in the scanner beds could not be avoided. However, as shown by the mean residual error value, those contributions were very small. 4. Discussion The automated CT marker segmentation technique described in this paper is based on the assumption that the shape of the 2D marker cross sections is not distorted by the choice of the threshold. The automated thresholding approach employed (Bae et al 1993) neither produced erroneous threshold values nor distorted the shape of the marker cross sections on the abdominal studies analysed; the studies analysed in this paper were abdominal studies. The above thresholding technique will produce erroneous threshold values in situations where the mean background value is significantly higher than zero and/or when the voxel intensity values along the sampled line is not significantly higher than those of the background (e.g. lungs, intestine). In the latter case, manual selection of the threshold might be necessary. The importance of thresholding in automated marker segmentation was reiterated by Wang et al (1996). They employed thresholding to segment the markers and the patient region (i.e. patient’s skull) from the background. Their segmentation approach relied on the shape and dimensions of the contrast-filled marker cavity. As a result, the choice of threshold employed was more critical because the threshold had to be sufficiently higher than the background and at the same time sufficiently low in order to exploit the intensity information within the marker. The above approach required that the thresholding resulted in the head volume being segmented, thus leaving only the markers. Should a threshold associated with the BaCl2 be applied to the abdominal scans, it would result in several anatomical structures,


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i.e. bones, present in the thresholded image, which would have to be analysed as potential markers. Consequently, the segmentation process would be slower. The algorithm described in this paper relies on knowledge of the shape and dimensions of the marker cross sections, thus being less vulnerable to the threshold value. The main requirement for the thresholding process is that the marker cross sections are not distorted so that the collinearity criterion is applicable. The value for the above criterion is important, as is shown in figure 4; a value less than 1.1 was found to be insufficient for the identification of every marker in the simulated study. The above failure was expected since the contour is not continuous but discrete and since thresholding might result in a particular segment deviating from perfect linearity. The choice of image digital resolution (i.e. voxel size) in conjunction with the size of the field-of-view (FOV) might affect the smallest detectable contour feature. For a specific FOV, the smaller the dimension of the contour feature to be identified, the highest the digital resolution required (Kita and Shirai 1991). The digital resolution (i.e. voxel size) has to be such that the number of voxels comprising the sampled contour segment is sufficient for the collinearity criterion to be applied and for an accurate definition of the vector perpendicular to the segment. The matrix size of (256 × 256) employed in this paper, was found to provide sufficient digital resolution for the dimensions of the markers even though the FOV dimensions employed in the two studies were different (FOV of 200 and 400 mm for studies A and B respectively). The algorithm’s sensitivity depends on the number and the frequency of the artificial segments parallel to the linear marker segment. Four artificial segments, with segment separation of 3.0 mm, were defined for each potential contour marker segment. The number and frequency of the artificial segments depend on the marker’s height and voxel size; segment separation must be at least equal to the voxel (x, y) dimension. The algorithm is not dependent on the marker cross section being rectangular. The shape of the cross sections depends mainly on the orientation of the marker relative to the CT x-ray beam but also on the threshold used. This irregularity in shape would render marker segmentation using morphological operators (e.g. opening, closing) difficult. Segmentation based on morphological operators requires the definition of a structural element (e.g. rectangle) which is then convolved with the 2D binary image to identify potential matches, that is potential markers. Definition of more than one structural element would be required to segment external markers placed on the skin. The requirement for segmentation is that the identification criteria, i.e. linear segment and intersections between the artificial segments and the contour, are satisfied. The orientation of the marker with respect to the CT x-ray beam, i.e. the out-ofplane rotation, will have an effect on the shape of the cross section (figure 5). Moreover, it might result in a marker cross section not being identified as shown in figure 5(b). An example of the above situation is shown in figure 6; the cross sections of markers mA and mC are correctly identified wheras the cross sections of mB is not. In order to overcome the above problem, prior to the computation of the marker centroid the volume of the 3D component is enlarged by adding two slices, one below and one above. Therefore, all the information available for the marker is taken into account, making the segmentation process robust to out-of-plane rotations and not dependent on the slice thickness, i.e. the number slices. The appearance of the markers in more than one slice also allows computation of the marker centroid with sub-slice accuracy. The registration technique described in this paper has been shown to be accurate. In order to use the registered CT/SPECT scans in the dosimetric process, e.g. delineation of the anatomical volumes on CT and transfer of those on the SPECT to allow 3D dose calculations, the position of the skin markers to the internal organs should be constant between scans.

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a)

b)

Figure 5. Simple schematic diagrams of the 3D marker (top) and the potential shape of the marker cross sections (bottom). In (a) the marker top-surface is perpendicular to the CT slice. In (b), the out-of-plane rotation results in the marker cross sections’ shape not being rectangular. Also, one of the marker cross sections is not identified because the marker identification criteria are not satisfied.

mC mB

mA

a)

b)

Figure 6. (a) Slice through a CT marker. (b) Slice contour. Marker, mA , is correctly identified with its cross section being rectangular. The cross section of marker mB is not identified whereas the cross section of mC is correctly identified despite the fact that it is not rectangular.

Provided that this assumption is valid, internal organ movement due to different scanner beds between CT and SPECT scans will be reflected in the residual marker registration error. However, internal organ movement with respect to the skin surface, with the skin marker geometry remaining constant, is not reflected. 5. Conclusion An automated technique to segment external CT markers has been developed for image registration. A priori information on the shape and dimensions of the external markers is employed to localize and segment the markers. The method is very sensitive and robust. The technique has been applied to radionuclide therapy studies resulting in a 3D residual


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registration error of 4.3 mm. The technique is applicable to any diagnostic and/or therapeutic study (e.g. registration of single CT scans, each being acquired prior to the delivery of external beam therapy during fractionated radiotherapy), which involves external CT markers. Acknowledgments We thank Professor Robert J Ott for his useful comments on the manuscript. This work was funded by the Association of International Cancer Research, the Medical Research Council and the Royal Marsden NHS Trust Charitable. References Arun K S, Huang T S and Blostein S D 1987 Least-squares fitting of two 3-D point sets IEEE Trans. Pattern Anal. Machine Intell. 9 698–700 Bae KT, Giger ML, Chen C-T and Kahn CE Jr 1993 Automatic segmentation of liver structure in CT images Med. Phys. 20 71–8 Eberl S, Kanno I, Fulton R R, Ryan A, Hutton B F and Fulham M J 1996 Automated interstudy image registration technique for SPECT and PET J. Nucl. Med. 37 137–45 Flux G D 1995 Multimodality image registration and its application to the dosimetry of intralesional radionuclide therapy PhD Thesis University of London, London, UK Guy M J 2000 The application of quantitative single photon emission tomography to targeted radionuclide therapy PhD Thesis University of London, London, UK Henrich G, Mai N and Backmund H 1979 Preprocessing in computed tomography picture analysis: a bone-deleting algorithm J. Comput. Assist. Tomogr. 3 379–84 Hill D L G, Hawkes D J, Crossman J E, Gleeson M J, Cox T C S, Bracey E E C M L, Strong A J and Graves P 1991 Registration of MR and CT images for skull base surgery using point-like anatomical features Br. J. Radiol. 64 1030–5 Hoefnagel C A, Voˆute P A, de Kraker J and Marcuse H R 1987 Radionuclide diagnosis and therapy of neural crest tumours using iodine-131 metaiodobenzylguanidine J. Nucl. Med. 28 308–14 Kita Y and Shirai Y 1991 Extraction of accurate stomach contours from x-ray images of barium-filled stomachs and its application to detect potential abnormalities CVGP:Graphical Models Imaging Process 53 447–56 Maes F, Collignon A, Vandermeulen D, Marchal and Suetens P 1997 Multimodality image registration by maximisation of mutual information IEEE Trans. Med. Imaging 16 187–97 Papavasileiou P, Flux G D, Flower M A and Guy M J 2001 An automated technique for SPECT marker-based image registration in radionuclide therapy Phys. Med. Biol. 46 2085–97 Pavlidis T 1982 Algorithms for Graphics and Image Processing (Berlin: Springer) Wang M Y, Maurer Jr C R, Fitzpatrick J M and Maciunas R J 1996 An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head IEEE Trans. Biomed. Eng. 43 627–37 Woods R P, Mazziota J C and Cherry S R 1993 MRI-PET registration with automated algorithm J. Comput. Assist. Tomogr. 17 536–46 Yu J N, Fahey F H, Gage H D, Eades C G, Harkness B A, Pelizzari C A and Keys Jr J W 1995 Intermodality, retrospective image registration in the thorax J. Nucl. Med. 36 2333–8