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Assessing the Performance of SPM Analyses of Spect Neuroactivation Studies
NeuroImage 12, 757–764 (2000) doi:10.1006/nimg.2000.0658, available online at http://www.idealibrary.com on
RAPID COMMUNICATION Assessing the Performance of SPM Analyses of Spect Neuroactivation Studies P. Lahorte,* ,† S. Vandenberghe,‡ K. Van Laere,† K. Audenaert,§ I. Lemahieu,‡ and R. A. Dierckx† *Department of Subatomic and Radiation Physics, Radiation Physics Group, Ghent University, Proeftuinstraat 86, B-9000 Ghent, Belgium, Member of IBITECH; †Division of Nuclear Medicine, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium, Member of IBITECH; ‡Department of Electronics and Information Systems, Division of Medical Imaging and Signal Processing (MEDISIP), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium, Member of IBITECH; and §Department of Psychiatry and Neuropsychology, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium Received February 21, 2000
Several simulations of SPECT neuroactivation studies have been performed in order to determine the influence of both study size and activation focus characteristics on the detection of brain activation foci following a pixel-based statistical analysis. This was achieved by developing a methodology based on the Hoffman software brain phantom, SPECT acquisition simulation software, standard reconstruction software, and the Statistical Parametric Mapping (SPM96) package. We present results on the minimal activation levels required for focus detection. Furthermore, the improved sensitivity of the analysis resulting from the use of an iterative reconstruction technique (OSEM) with regard to the classical filtered backprojection (FBP) is assessed quantitatively, and the various physical, processing, and physiological parameters that potentially influence the detection of foci are discussed. Finally, the influence is investigated of the height threshold as implemented in SPM96 upon the size of the detected foci. Practical guidelines are proposed with regard to the number of subjects per group for SPECT activation studies following the split-dose design. © 2000 Academic Press
INTRODUCTION Several imaging techniques for conducting brain activation studies are presently available to the experimental neuroscientist. Positron Emission Tomography (PET) is currently the gold standard for functional imaging of the brain. Functional Magnetic Resonance Imaging (fMRI), although still in its infancy, is nowadays a subject of intense research offering extensive opportunities (Aihner et al., 1997; Kim and Ugurbil, 1997; Ogawa et al., 1998). Third, Single Photon Emission Computed Tomography (SPECT) has proved over
the last few years to be a promising technique for conducting neuroactivation studies. With increasing system sensitivity and resolution (Kouris et al., 1993) and a good cost-effectiveness, new clinical and research applications of neuroactivation studies are being developed at a rapid pace (Biersack et al., 1997). One of the most serious limitations of SPECT activation studies is the limited degrees of freedom usually available for analysis of the study. Evidently this is a result of the moderate complexity of the experimental design of SPECT activation studies which itself is linked to the physical half-life of the SPECT isotopes frequently used for brain imaging (Morgan and Costa, 1993). Mostly a so-called split-dose design is adopted (Holm et al., 1994). Practically, the acquisition is limited to one baseline and one activation scan within one session. The ensuing lack of information on withinsubject variation in most experimental SPECT activation paradigms implies that the number of subjects included in the study will have to be increased in order to reach sufficient degrees of freedom, required to perform a valid and sensitive statistical analysis. In the early nineties, the technique of Statistical Parametric Mapping (SPM) has come forward and is now regarded as the method of choice in the statistical analysis of functional imaging data in general (Friston et al., 1995). Although the main fields of application are PET and fMRI studies, already some clinical SPECT activation studies have been analyzed within an SPM framework (Crawford et al., 1996; Ebmeier et al., 1997; Fukuyama et al., 1997; Goodwin et al., 1997a,b; Patterson et al., 1997; Shajahan et al., 1997; Montaldi et al., 1998; Laatsch et al., 1999; Imon et al., 1999). Evidently, a valid statistical analysis of SPECT data requires that the proper system parameters are accounted for, that the experimental design is optimized
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1053-8119/00 $35.00 Copyright © 2000 by Academic Press All rights of reproduction in any form reserved.
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and that an analysis is made of the performance of the statistical tests used. To the best of our knowledge, little work has been done in these fields. Barnes et al. (1997) have performed a simulation study using an SPM approach to investigate whether SPECT neuroactivation studies would benefit from trying to get an estimate of within-subject variability by increasing the number of scans per task and accepting poorer individual scan quality. Very recently, Stamatakis et al. (1999), performed simulations in an attempt to validate the analysis of SPECT lesion studies using SPM. With regard to PET activation studies, Fahey et al. (1998) have developed a tool for quantifying regions of interest in registered MR and PET data. This software was validated by comparizing its performance with SPM96 in detecting spherical activation features in phantom and simulated FDG PET studies. With regard to the statistical power of activation studies, Kapur et al. (1995) have tried to provide an empirical basis for selecting the number of subjects for cognitive studies using 15O-water PET. Finally, assessments are available from a theoretical point of view of the performance of the statistical tests underlying the inference procedure in the SPM96 package (Friston et al., 1994b). A rigorous validation, however, of the use of the SPM technique with regard to the assessment of the power of SPECT neuroactivation studies based on the splitdose design has not yet been performed. Therefore, we haven taken up the goal of investigating in this respect the influence of the most important parameters of the experimental design (study size, number of replications, acquisition time, image quality per scan). Here we present the first step in this process in which we investigated the interplay between activation foci characteristics (dimensions and activation level) and the study size (i.e., the number of subjects included in the study) in the detection of foci in the brain following an SPM analysis. MATERIALS AND METHODS Data Simulation Three spheres of equal size were created in the three-dimensional Hoffman software brain phantom. This is a 128 ⫻ 128 ⫻ 128 matrix representation (voxel size 2 ⫻ 2 ⫻ 2 mm 3) of the hardware analogue (Hoffman et al., 1990). These spheres were positioned in different brain structures (corpus cingulus anterior, corpus cingulus posterior, and nucleus lentiformis). Three different diameters were chosen: 8, 16, and 24 mm. The voxel values within the spheres were increased by a certain percentage of the initial voxel value of the center of each sphere. In this way, spherical activation foci, i.e., regions with increased regional cerebral blood flow (rCBF), were mimicked. In what
FIG. 1. Schematic representation of the steps followed in the simulation: (A) Hoffman phantom with activation foci; (B) sinogram; (C) reconstructed image; (D) statistical parametric map.
follows, the percentage of the voxel value of the center added to each initial voxel value in the spheres, will be conveniently denoted as the “activation level” of the foci. In our studies the Hoffman phantom itself represents a baseline image (A) from a single task activation design, whereas the Hoffman phantom containing the “activation foci” represents an activation image (B). Both scans, “baseline” and “activation” were copied to produce 3, 5, 10, and 15 SPECT paired data sets (A, B), respectively. Combining the study sizes [(3,3), (5,5), (10,10), and (15,15)] obtained in this way, with the three different sphere diameters, resulted in a total of 12 studies for which the minimal activation level required for detection of the foci was determined. It is clear that before SPECT simulation and reconstruction, all rest (A) and activation (B) images in a specific study are identical to each other. In this way, all potential effects due to physiological CBF changes, intersubject cerebral anatomical differences and image misalignment are effectively excluded from the simulation. The further set-up of the simulation is schematically illustrated in Fig. 1. After construction of the foci and appropriate replication of the images, all image pairs included in each specific study were projected, thus simulating the acquisition by a SPECT gamma camera. This was realized with the use of shareware sinogram simulation software (Hudson and Larkin, 1994) to which the relevant technical characteristics of the triple-head Toshiba GCA-9300A SPECT gamma camera were feeded as input parameters (Kouris et al., 1993). More specifically, an acquisition with LEHR parallel hole collimators at the minimal radius of rotation of 132 mm was simulated using a linearly increasing depth-dependent point spread function (PSF at 0
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TABLE 1 Resolution of the Software-Simulated Images as Compared to Experimental Resolution Radial
Tangential
FWHM
FWTM
FWHM
FWTM
Radial distance
Simul.
Exptl.
Simul.
Exptl.
Simul.
Exptl.
Simul.
Exptl.
0 40 80
10.4 10.3 10.2
9.8 9.9 10.0
16.6 16.5 16.6
17.6 17.4 17.8
10.2 10.1 9.0
9.6 9.5 8.1
15.8 16.1 15.6
17.5 17.5 17.4
Note. Experimental values from Kouris et al. (1993); p. 1784, Table 3; All values are in mm.
mm ⫽ 2.30 mm, PSFscale ⫽ 0.0179 ). Attenuation was simulated by using a uniform attenuation coefficient of 0.12 cm ⫺1 in a cylinder around the software Hoffman phantom with the same diameter as the hardware phantom (190 mm). For every acquisition simulation, 90 projections were obtained at 4° intervals. Consequently, Poisson noise was added to the projections. With these acquisition parameters implemented, the simulation resulted in images containing each about 3.2 M counts that were of comparable quality as those obtained during typical routine clinical brain studies on the aforementioned triple-head camera (Kouris et al., 1993). Data Processing The acquisition data were reconstructed using two standard nuclear medicine algorithms. Filtered backprojection (FBP) was performed with the HERMES software package (Nuclear Diagnostics Ltd., Sweden). All sinograms were prefiltered with a low-pass Butterworth filter with cut-off frequency 0.33 cycles/pixel (pixel size 2 mm) and order 8. The reconstructed images were postfiltered with a Butterworth filter of cutoff frequency 0.75 cycles/pixel and order 8. In the absence of unambiguous literature data, these filter parameters were adopted from the values that are used in the processing of comparable routine brain SPECT studies at the department of nuclear medicine of the Ghent university hospital. The second algorithm used was the iterative Ordered Subsets Expectation Maximization (OSEM) algorithm (Hudson and Larkin, 1994) with attenuation correction (9 subsets of 10 projections, 4 iterations) using the aforementioned uniform attenuation map and depthdependent PSF. After appropriate reorientation, all images were converted from the Interfile to the Analyze format using in-house written software. Data Analysis To establish statistically significant effects between baseline and activation images in each study, Statisti-
cal Parametric Maps (SPMs) of the rCBF were constructed with the SPM96 version of the SPM software (Friston, 1994a). By construction, baseline and activation images are coincident, so no realignment was necessary. All images were spatially normalized using a six-parameter rigid body transformation into Talairach space (three nonlinear transformations, 2 ⫻ 3 ⫻ 2 basis functions) to the implemented PET template. The transformation parameters were derived from a baseline image in each study. “Richer” transformations (e.g., 12-parameter linear affine transformations), although potentially yielding a better normalization, did not prove to be adequate. This is due to the anatomical discrepancies between the Hoffman brain phantom and the “real brain” PET-template as implemented in SPM96. The images were then smoothed with a three-dimensional Gaussian filter of 12-mm fullwidth-at-half-maximum (FWHM). The data were analyzed within the single subject, replication of conditions design. Proportional scaling was used to achieve normalization of global blood flow (grey matter threshold ⫽ 0.4). RESULTS AND DISCUSSION Evaluation of Image Quality of Simulated Data The tomographic spatial resolution of images created by our simulation procedure was investigated by creating a 128 ⫻ 128 ⫻ 128 matrix in which three line sources were placed, parallel to the camera rotation axis and radially spaced at 0, 40, and 80 mm from the center of the field of view. This was basically also the approach followed by Kouris et al. (1993). The voxel size was chosen identical to the one of the Hoffman software phantom (2 ⫻ 2 ⫻ 2 mm 3). Ninety projections at 4° intervals were acquired with the aforementioned projection software (Hudson and Larkin, 1994). After reconstruction with standard FBP, FWHM, and fullwidth-at-tenth-maximum (FWTM) of both tangential and axial resolution was determined with the HERMES software. The results are displayed in Table 1, together with the available experimental data
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FIG. 2. Minimal foci activation levels required for detection within an SPM96 analysis for FBP and OSEM reconstruction and the three foci diameters considered in this study; (–䊐–) FBP, 8 mm; (–■–) OSEM, 8 mm; (–‚–) FBP, 16 mm; (–Œ–) OSEM, 16 mm; (–E–) FBP, 24 mm; (–F–) OSEM, 24 mm.
(Kouris et al., 1993). In most of the cases the difference between experimental FWHM and our simulated phantom data FWHM is smaller than 1.5 mm. The differences are largest in the case of the FWTM, which could be due to exclusion of scatter in our simulations. Detection of Activation Foci as Function of Diameter and Study Size For each of the combinations of study size and foci diameter, the activation level was systematically reduced in steps of 5% for the high activity levels (⬎30% activation) through 1.25% for the low activity levels (⬍10% activation). The minimal activation levels required for detection of the foci within an SPM96 analysis are shown in Fig. 2 for both of the reconstruction algorithms used. At the indicated activation levels, all foci appeared as significant at the P ⬍ 0.05 level (default height threshold value of 0.001) corrected for multiple comparisons based on both peak height and spatial extent, and no false (de)activations were reported. As illustrated in Fig. 2, the use of the iterative OSEM reconstruction technique over the more conventional FBP, greatly improves the sensitivity of the statistical analysis. For a study size of 10 baseline and 10 activation images, activation foci with diameters of 16 mm can be detected from a minimal activation level of 15% with FBP and 10% with OSEM, respectively. The improved image quality resulting from an iterative reconstruction algorithm as compared to FBP has already been well established (Kim et al., 1993; Llacer et al., 1993). Nevertheless, the results reported in Fig. 1 are, to the best of our knowledge, the first to clearly indicate the quantitative difference between the two reconstruction schemes with regard to the performance of an ensuing statistical analysis. As a caveat however, it should be mentioned that in the current study, the difference between the two reconstruction schemes may be biased in favor of OSEM due to the fact that the
projector component of the projector-backprojector pair used in the OSEM software, is identical to the projector used in the simulation. Consequently, if both projectors are the same, the OSEM is “matched” to the simulation, whereas the FBP algorithm does not have access to the exact physics model used. This potential bias could be eliminated by using a different SPECT simulator such as SIMSET (Harrison et al., 1993) or SIMIND (Ljungberg and Strand, 1989). As these are Monte Carlo programs, their use represents a very significant computational liability even with the computer power presently available, and therefore this option was not considered for the current study where a large number of images had to be created. As can be expected, the minimal activity difference required for focus detection shows a marked decrease with both increasing focus diameter and study size. The results for the foci diameter of 8 mm, which is smaller than the experimental 10.2 mm tomographic FWHM for the HR-PH collimators on the aforementioned Toshiba camera (Kouris et al., 1993), clearly reflect the influence of the partial volume effect: high activation levels are necessary for detection of foci smaller than the system resolution. Focusing on the OSEM results, it can be seen that the superior sensitivity of an analysis based on OSEM versus FBP reconstructed images, becomes more pronounced toward smaller numbers of available scans and smaller activation foci sizes. The difference becomes marginal for the larger foci when enlarging the study size from 10 to 15 image pairs. In conclusion, our simulation shows that, using the OSEM reconstruction algorithm, activation foci with a diameter of 24 mm, and a minimal activation level of 3.75% can be detected in a study size of 10 baseline and 10 activation images. However, it should be noted in this respect that the size of some brain structures may be smaller (e.g., the anterior cingulate, which is frequently involved in cognitive activation paradigms). Consequently, the need for relatively large activation foci (e.g., a minimal diameter of about 16 mm as indicated by our simulations) might be a practical restraint imposed on SPECT neuroactivation experiments. As already stated above, in the present study a perfect alignment between all scans was assumed together with a constant activation level for each given combination of study size and foci diameters. However, in real studies a certain degree of misalignment might be inevitably present in the images due to an incomplete correction for motion or an imperfect repositioning of a subject in the camera. Some of the present authors have already investigated the performance of SPECT– SPECT, within-subject coregistration algorithms. (Van Laere et al., 2000a). These authors showed that the absolute registration errors of a six-parameter affine transformation, as is also implemented within SPM96, did not exceed 2.8 mm for the translation and 1° for the
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rotation component for SPECT perfusion images, which were acquired and processed similarly to the images created in the present study. Consequently, taking into account the fact that the FWHM for the simulated set-up with parallel hole collimators is about 10 mm, shifting the activated images with the registration error is unlikely to result in large changes in SPM’s detection of the relatively large foci that are practically amenable to investigation by a SPECT activation study. Nevertheless, the authors agree that for the simulation of real clinical studies, the issue of image mismatch is potentially important with respect to normalization of images originating from different subjects into a standard stereotactic space. Finally, it should also be taken into account that incorporation of the effect of scatter in the simulations would result in a decreased image contrast (Van Laere et al., 2000b). Apart from the several physical factors, such as attenuation and scatter, geometric detector response, detector efficiency, as well as processing parameters such as reconstruction algorithm, filtering and coregistration, another class of factors that can confound the detection of activation regions, are the kinetic and physiological properties of the perfusion tracers used. In this respect, it has been demonstrated that significant regional variations in activity uptake and kinetic pattern exist between the two most widely used 99mTechnetium labeled tracers HMPAO and ECD (Deutsch et al., 1997; Imran et al., 1998; SiennickiLantz et al., 1999), and that inhomogeneous regional washout can occur (Flores et al., 1999; Ichise et al., 1997). In addition, physiological differences in uptake exist within and between subjects (Jonsson et al., 2000; Van Laere et al., 1999). From these considerations it should be clear that for a comparable set-up in a clinical setting the minimal activation levels required for foci detection will almost certainly be higher than those resulting from our simulations. The exact impact of the aforementioned physical, processing, and physiological effects was, however, beyond the scope of the present study. The reported activation levels should therefore be considered as indicators of the best possible theoretical performance of SPECT activation studies, for comparable triple-head systems. In this respect it is clear that fan beam collimators and brain-dedicated hardware would result in a superior image resolution and total number of counts and would consequently lead to a higher lesion or activation foci detection (Moore et al., 1995). Obviously, for exploratory activation studies the exact localisation of activation foci in the brain, if any at all, are not known a priori to the experimenter, let alone their size, shape, or activation level. Therefore, the exact number of subjects one decides to include in a particular study is at best a compromise between the hope to detect any main experimental effects and the desire to minimize both the number of volunteers or
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patients to be subjected to the procedure and the significant amount of experimental work involved. Practical guidelines as to how many subjects should be included in a specific study on the basis of detection of foci, can only come from simulations as the ones presented here. On the basis of the current results and the discussion above, it may be advised for split-dose SPECT activation studies to examine groups consisting of at least 10 to 15 subjects on a high-performance gamma camera (e.g., triple-head with HR-FB collimators) with the iterative OSEM reconstruction technique. Evaluation of the Spatial Extent of Detected Foci The second goal of the present study was to investigate the evolution of the spatial extent of foci upon variation of the height threshold of the bivariate test implemented in SPM96, following statistical analysis. Figures 3A and 3B display the volumes of detected foci analyzed at a significance level of P ⬍ 0.05 when varying the study size or the foci diameter, respectively. From Fig. 3A it can be seen that the detected volumes of the foci at a particular threshold, converge systematically with increasing number of images. This is evidently due to a better sampling of the two populations represented by the rest and activation images, respectively. The three sizes of spherical foci included in this study (8, 16, and 24 mm diameter) correspond to a volume of 0.27, 2.1, and 7.2 ml, respectively, or clusters of 10.3, 82.3, and 277.6 voxels in the SPMs (voxel size 2.9 ⫻ 2.9 ⫻ 3.1 mm 3). As can be seen from Fig. 3B, at the default intensity threshold value of 0.001, the detected average foci volume reflects the true volume only for the foci of 16 mm in diameter. For the larger size of 24 mm, the height threshold has to be lowered to about 0.01. Analogously, the threshold has to be increased for smaller foci sizes. As illustrated for the 8-mm foci included in the study, the impact of the partial volume effect prohibits an accurate study of intense focal activations beyond the system resolution. These results confirm quantitatively the remark made by Friston et al. (1994b) that, on principle, a different threshold should be used depending on the suspected type of activation. The indicated levels of the height threshold could serve as a guideline in the statistical analysis of SPECT activation studies where there is an a priori hypothesis concerning the identity and consequently the size of the to-be activated foci. In this respect, it is our conviction that mainly the order of magnitude of the reported preferable height threshold is important as the exact value might be influenced by factors such as the amount of smoothing or the effect of additional image transformations applied to the images. Any attempt to quantify the actual size of activation foci would also require an assessment of
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FIG. 3. Evolution of the volume of detected foci when varying the height threshold in SPM96: (A) effect of the study size for focus diameter of 16 mm (B) effect of the foci diameter for a study size of 10 rest and activation image pairs; All images reconstructed with OSEM; SPM voxel size 2.9 ⫻ 2.9 ⫻ 3.1 mm 3; (–}–) corpus cingulum anterior, (–Œ– ) corpus cingulum posterior, (–F–) nucleus lentiformis.
those factors which is again beyond the scope of the present study. CONCLUSIONS In the present study we investigated the influence of the study size and the activation foci characteristics (dimension and activation level) in SPECT neuroactivation studies on the detection of the foci following statistical analysis with statistical parametric mapping. A simulation was performed with the use of the Hoffman software brain phantom, sinogram simulation software, and two standard image reconstruction algorithms (FBP and OSEM). For selected combinations of study sizes and foci diameters, minimal activation levels required for detection were determined. It
was found that the sensitivity of a statistical analysis based on images reconstructed using the iterative OSEM technique is significantly higher than in the case of the more conventional FBP. From our simulations we propose the number of 15 subjects as a guideline for the size of groups in SPECT activation studies where small activation intensities (⬍5%) are expected (e.g., cognitive paradigms). For more robust activation levels (around 10%), the group size can be lowered to minimally 10. Furthermore, the effect of a varying height threshold on the size of the detected foci was assessed in function of both study size and initial foci diameter. This work represents a preliminary effort to identify the validity domain of pixel-based statistical analyses of SPECT activation studies. Effects that can be ex-
SPM PERFORMANCE FOR BRAIN SPECT STUDIES
pected to lead to a reduced detection of activation foci and that are currently not incorporated in the simulations include scatter and the effect of (imperfect) normalization of multiple-subject images into a stereotactic space. On the other hand, the modeling of fan beam collimators and brain-dedicated hardware will yield an increased image resolution and total number of counts, thus positively influencing the detection of foci. The presented methodology provides a powerful tool to simulate and optimize these and other parameters in the experimental design of brain activation studies in general and to estimate the theoretical performance of SPECT activation studies in particular. ACKNOWLEDGMENTS The authors thank E. Nolf for making available the MEDCON medical image conversion software and Nuclear Diagnostics and SUN microsystems for their technical support in the implementation of image fusion in the Division of Nuclear Medicine of the Ghent University Hospital. Y. D’Asseler and M. Koole are acknowledged for their fruitful discussions.
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teristic study comparing filtered backprojection and maximum likelihood estimator images in FDG PET studies. J. Nucl. Med. 34: 1198 –1203. Ljungberg, M., and Strand, S. E. 1989. A Monte Carlo program for the simulation of scintillation camera characteristics. Comput. Methods Prog. Biol. 29: 257–272. Montaldi, D., Mayes, A. R., Barnes, A., Pirie, H., Hadley, D. M., Patterson, J., and Wyper, D. J. 1998. Associative encoding of pictures activates the medial temporal lobes. Hum. Brain Mapp. 6: 85–104. Morgan, G. F., and Costa, D. C. 1993. Radiopharmaceuticals for conventional blood-brain barrier and brain perfusion studies. In New Trends in Nuclear Neurology and Psychiatry (D. C. Costa, D. C. Morgan, N. A. Lassen, and F. Aihner, Eds.), pp. 65– 84. Libbey, London. Moore, S. C., Devries, D. J., Nandram, B., Kijewski, M. F., and Mueller, S. P. 1995. Collimator optimization for lesion detection incorporating prior information about lesion size. Med. Phys. 22: 703–713. Ogawa, S., Menon, R. S., Kim, S. G., and Ugurbil, K. 1998. On the characteristics of functional magnetic resonance imaging of the brain. Annu. Rev. Biophys. Biomol. Struc. 27: 447. Patterson, J. C., Early, T. S., Martin, A., Walker, M. Z., Russell, J. M., and Villanueva-Meyer, H. 1997. SPECT image analysis using statistical parametric mapping: Comparison of technetium-99m-HMPAO and technetium-99m-ECD. J. Nucl. Med. 38: 1721–1725.
Siennicki-Lantz, A., Lilja, B., and Elmstahl, S. 1999. How to interpret differing cerebral blood flow patterns estimated with Tc99(m)-HMPAO and Tc-99(m)-ECD SPET in a healthy population. Nucl. Med. Comm. 20: 219 –226. Shajahan, P. M., Glabus, M. F., Blackwood, D. H. R., and Ebmeier, K. P. 1997. Brain activation during an auditory ‘oddball task’ in schizophrenia measured by single photon emission tomography. Psychol. Med. 27: 587–594. Stamatakis, E. A., Glabus, M. F., Wyper, D. J., Barnes, A., and Wilson, J. T. 1999. Validation of statistical parametric mapping (SPM) in assessing cerebral lesions: A simulation study. Neuroimage 10: 397– 407. Van Laere, K., De Sadeleer, C., Dobbeleir, A., Bossuyt, A., De Deyn, P., and Dierckx, R. 1999. Quantification of Tc-99(m)-HMPAO brain SPET in two series of healthy volunteers using different tripleheaded SPET configurations: Normal databases and methodological considerations. Nucl. Med. Comm. 20: 1031–1040. Van Laere, K., Koole, M., D’Asseler, Y., Audenaert, K., Dumont, F., and Dierckx, R. 2000a. Automatic stereotactic standardization of brain receptor data using single-photon transmission images. J. Nucl. Med. 41: P61. Van Laere, K., Koole, M., Kauppinen, T., Monsieurs, M., Bouwens, L., and Dierckx, R. 2000b. Non-uniform transmission in brain SPET using 201-Tl, 99m-Tc and 153-Gd static line sources: antropomorphic dosimetry studies and influence and brain quantification. J. Nucl. Med., in press.
RAPID COMMUNICATION Assessing the Performance of SPM Analyses of Spect Neuroactivation Studies P. Lahorte,* ,† S. Vandenberghe,‡ K. Van Laere,† K. Audenaert,§ I. Lemahieu,‡ and R. A. Dierckx† *Department of Subatomic and Radiation Physics, Radiation Physics Group, Ghent University, Proeftuinstraat 86, B-9000 Ghent, Belgium, Member of IBITECH; †Division of Nuclear Medicine, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium, Member of IBITECH; ‡Department of Electronics and Information Systems, Division of Medical Imaging and Signal Processing (MEDISIP), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium, Member of IBITECH; and §Department of Psychiatry and Neuropsychology, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium Received February 21, 2000
Several simulations of SPECT neuroactivation studies have been performed in order to determine the influence of both study size and activation focus characteristics on the detection of brain activation foci following a pixel-based statistical analysis. This was achieved by developing a methodology based on the Hoffman software brain phantom, SPECT acquisition simulation software, standard reconstruction software, and the Statistical Parametric Mapping (SPM96) package. We present results on the minimal activation levels required for focus detection. Furthermore, the improved sensitivity of the analysis resulting from the use of an iterative reconstruction technique (OSEM) with regard to the classical filtered backprojection (FBP) is assessed quantitatively, and the various physical, processing, and physiological parameters that potentially influence the detection of foci are discussed. Finally, the influence is investigated of the height threshold as implemented in SPM96 upon the size of the detected foci. Practical guidelines are proposed with regard to the number of subjects per group for SPECT activation studies following the split-dose design. © 2000 Academic Press
INTRODUCTION Several imaging techniques for conducting brain activation studies are presently available to the experimental neuroscientist. Positron Emission Tomography (PET) is currently the gold standard for functional imaging of the brain. Functional Magnetic Resonance Imaging (fMRI), although still in its infancy, is nowadays a subject of intense research offering extensive opportunities (Aihner et al., 1997; Kim and Ugurbil, 1997; Ogawa et al., 1998). Third, Single Photon Emission Computed Tomography (SPECT) has proved over
the last few years to be a promising technique for conducting neuroactivation studies. With increasing system sensitivity and resolution (Kouris et al., 1993) and a good cost-effectiveness, new clinical and research applications of neuroactivation studies are being developed at a rapid pace (Biersack et al., 1997). One of the most serious limitations of SPECT activation studies is the limited degrees of freedom usually available for analysis of the study. Evidently this is a result of the moderate complexity of the experimental design of SPECT activation studies which itself is linked to the physical half-life of the SPECT isotopes frequently used for brain imaging (Morgan and Costa, 1993). Mostly a so-called split-dose design is adopted (Holm et al., 1994). Practically, the acquisition is limited to one baseline and one activation scan within one session. The ensuing lack of information on withinsubject variation in most experimental SPECT activation paradigms implies that the number of subjects included in the study will have to be increased in order to reach sufficient degrees of freedom, required to perform a valid and sensitive statistical analysis. In the early nineties, the technique of Statistical Parametric Mapping (SPM) has come forward and is now regarded as the method of choice in the statistical analysis of functional imaging data in general (Friston et al., 1995). Although the main fields of application are PET and fMRI studies, already some clinical SPECT activation studies have been analyzed within an SPM framework (Crawford et al., 1996; Ebmeier et al., 1997; Fukuyama et al., 1997; Goodwin et al., 1997a,b; Patterson et al., 1997; Shajahan et al., 1997; Montaldi et al., 1998; Laatsch et al., 1999; Imon et al., 1999). Evidently, a valid statistical analysis of SPECT data requires that the proper system parameters are accounted for, that the experimental design is optimized
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and that an analysis is made of the performance of the statistical tests used. To the best of our knowledge, little work has been done in these fields. Barnes et al. (1997) have performed a simulation study using an SPM approach to investigate whether SPECT neuroactivation studies would benefit from trying to get an estimate of within-subject variability by increasing the number of scans per task and accepting poorer individual scan quality. Very recently, Stamatakis et al. (1999), performed simulations in an attempt to validate the analysis of SPECT lesion studies using SPM. With regard to PET activation studies, Fahey et al. (1998) have developed a tool for quantifying regions of interest in registered MR and PET data. This software was validated by comparizing its performance with SPM96 in detecting spherical activation features in phantom and simulated FDG PET studies. With regard to the statistical power of activation studies, Kapur et al. (1995) have tried to provide an empirical basis for selecting the number of subjects for cognitive studies using 15O-water PET. Finally, assessments are available from a theoretical point of view of the performance of the statistical tests underlying the inference procedure in the SPM96 package (Friston et al., 1994b). A rigorous validation, however, of the use of the SPM technique with regard to the assessment of the power of SPECT neuroactivation studies based on the splitdose design has not yet been performed. Therefore, we haven taken up the goal of investigating in this respect the influence of the most important parameters of the experimental design (study size, number of replications, acquisition time, image quality per scan). Here we present the first step in this process in which we investigated the interplay between activation foci characteristics (dimensions and activation level) and the study size (i.e., the number of subjects included in the study) in the detection of foci in the brain following an SPM analysis. MATERIALS AND METHODS Data Simulation Three spheres of equal size were created in the three-dimensional Hoffman software brain phantom. This is a 128 ⫻ 128 ⫻ 128 matrix representation (voxel size 2 ⫻ 2 ⫻ 2 mm 3) of the hardware analogue (Hoffman et al., 1990). These spheres were positioned in different brain structures (corpus cingulus anterior, corpus cingulus posterior, and nucleus lentiformis). Three different diameters were chosen: 8, 16, and 24 mm. The voxel values within the spheres were increased by a certain percentage of the initial voxel value of the center of each sphere. In this way, spherical activation foci, i.e., regions with increased regional cerebral blood flow (rCBF), were mimicked. In what
FIG. 1. Schematic representation of the steps followed in the simulation: (A) Hoffman phantom with activation foci; (B) sinogram; (C) reconstructed image; (D) statistical parametric map.
follows, the percentage of the voxel value of the center added to each initial voxel value in the spheres, will be conveniently denoted as the “activation level” of the foci. In our studies the Hoffman phantom itself represents a baseline image (A) from a single task activation design, whereas the Hoffman phantom containing the “activation foci” represents an activation image (B). Both scans, “baseline” and “activation” were copied to produce 3, 5, 10, and 15 SPECT paired data sets (A, B), respectively. Combining the study sizes [(3,3), (5,5), (10,10), and (15,15)] obtained in this way, with the three different sphere diameters, resulted in a total of 12 studies for which the minimal activation level required for detection of the foci was determined. It is clear that before SPECT simulation and reconstruction, all rest (A) and activation (B) images in a specific study are identical to each other. In this way, all potential effects due to physiological CBF changes, intersubject cerebral anatomical differences and image misalignment are effectively excluded from the simulation. The further set-up of the simulation is schematically illustrated in Fig. 1. After construction of the foci and appropriate replication of the images, all image pairs included in each specific study were projected, thus simulating the acquisition by a SPECT gamma camera. This was realized with the use of shareware sinogram simulation software (Hudson and Larkin, 1994) to which the relevant technical characteristics of the triple-head Toshiba GCA-9300A SPECT gamma camera were feeded as input parameters (Kouris et al., 1993). More specifically, an acquisition with LEHR parallel hole collimators at the minimal radius of rotation of 132 mm was simulated using a linearly increasing depth-dependent point spread function (PSF at 0
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TABLE 1 Resolution of the Software-Simulated Images as Compared to Experimental Resolution Radial
Tangential
FWHM
FWTM
FWHM
FWTM
Radial distance
Simul.
Exptl.
Simul.
Exptl.
Simul.
Exptl.
Simul.
Exptl.
0 40 80
10.4 10.3 10.2
9.8 9.9 10.0
16.6 16.5 16.6
17.6 17.4 17.8
10.2 10.1 9.0
9.6 9.5 8.1
15.8 16.1 15.6
17.5 17.5 17.4
Note. Experimental values from Kouris et al. (1993); p. 1784, Table 3; All values are in mm.
mm ⫽ 2.30 mm, PSFscale ⫽ 0.0179 ). Attenuation was simulated by using a uniform attenuation coefficient of 0.12 cm ⫺1 in a cylinder around the software Hoffman phantom with the same diameter as the hardware phantom (190 mm). For every acquisition simulation, 90 projections were obtained at 4° intervals. Consequently, Poisson noise was added to the projections. With these acquisition parameters implemented, the simulation resulted in images containing each about 3.2 M counts that were of comparable quality as those obtained during typical routine clinical brain studies on the aforementioned triple-head camera (Kouris et al., 1993). Data Processing The acquisition data were reconstructed using two standard nuclear medicine algorithms. Filtered backprojection (FBP) was performed with the HERMES software package (Nuclear Diagnostics Ltd., Sweden). All sinograms were prefiltered with a low-pass Butterworth filter with cut-off frequency 0.33 cycles/pixel (pixel size 2 mm) and order 8. The reconstructed images were postfiltered with a Butterworth filter of cutoff frequency 0.75 cycles/pixel and order 8. In the absence of unambiguous literature data, these filter parameters were adopted from the values that are used in the processing of comparable routine brain SPECT studies at the department of nuclear medicine of the Ghent university hospital. The second algorithm used was the iterative Ordered Subsets Expectation Maximization (OSEM) algorithm (Hudson and Larkin, 1994) with attenuation correction (9 subsets of 10 projections, 4 iterations) using the aforementioned uniform attenuation map and depthdependent PSF. After appropriate reorientation, all images were converted from the Interfile to the Analyze format using in-house written software. Data Analysis To establish statistically significant effects between baseline and activation images in each study, Statisti-
cal Parametric Maps (SPMs) of the rCBF were constructed with the SPM96 version of the SPM software (Friston, 1994a). By construction, baseline and activation images are coincident, so no realignment was necessary. All images were spatially normalized using a six-parameter rigid body transformation into Talairach space (three nonlinear transformations, 2 ⫻ 3 ⫻ 2 basis functions) to the implemented PET template. The transformation parameters were derived from a baseline image in each study. “Richer” transformations (e.g., 12-parameter linear affine transformations), although potentially yielding a better normalization, did not prove to be adequate. This is due to the anatomical discrepancies between the Hoffman brain phantom and the “real brain” PET-template as implemented in SPM96. The images were then smoothed with a three-dimensional Gaussian filter of 12-mm fullwidth-at-half-maximum (FWHM). The data were analyzed within the single subject, replication of conditions design. Proportional scaling was used to achieve normalization of global blood flow (grey matter threshold ⫽ 0.4). RESULTS AND DISCUSSION Evaluation of Image Quality of Simulated Data The tomographic spatial resolution of images created by our simulation procedure was investigated by creating a 128 ⫻ 128 ⫻ 128 matrix in which three line sources were placed, parallel to the camera rotation axis and radially spaced at 0, 40, and 80 mm from the center of the field of view. This was basically also the approach followed by Kouris et al. (1993). The voxel size was chosen identical to the one of the Hoffman software phantom (2 ⫻ 2 ⫻ 2 mm 3). Ninety projections at 4° intervals were acquired with the aforementioned projection software (Hudson and Larkin, 1994). After reconstruction with standard FBP, FWHM, and fullwidth-at-tenth-maximum (FWTM) of both tangential and axial resolution was determined with the HERMES software. The results are displayed in Table 1, together with the available experimental data
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FIG. 2. Minimal foci activation levels required for detection within an SPM96 analysis for FBP and OSEM reconstruction and the three foci diameters considered in this study; (–䊐–) FBP, 8 mm; (–■–) OSEM, 8 mm; (–‚–) FBP, 16 mm; (–Œ–) OSEM, 16 mm; (–E–) FBP, 24 mm; (–F–) OSEM, 24 mm.
(Kouris et al., 1993). In most of the cases the difference between experimental FWHM and our simulated phantom data FWHM is smaller than 1.5 mm. The differences are largest in the case of the FWTM, which could be due to exclusion of scatter in our simulations. Detection of Activation Foci as Function of Diameter and Study Size For each of the combinations of study size and foci diameter, the activation level was systematically reduced in steps of 5% for the high activity levels (⬎30% activation) through 1.25% for the low activity levels (⬍10% activation). The minimal activation levels required for detection of the foci within an SPM96 analysis are shown in Fig. 2 for both of the reconstruction algorithms used. At the indicated activation levels, all foci appeared as significant at the P ⬍ 0.05 level (default height threshold value of 0.001) corrected for multiple comparisons based on both peak height and spatial extent, and no false (de)activations were reported. As illustrated in Fig. 2, the use of the iterative OSEM reconstruction technique over the more conventional FBP, greatly improves the sensitivity of the statistical analysis. For a study size of 10 baseline and 10 activation images, activation foci with diameters of 16 mm can be detected from a minimal activation level of 15% with FBP and 10% with OSEM, respectively. The improved image quality resulting from an iterative reconstruction algorithm as compared to FBP has already been well established (Kim et al., 1993; Llacer et al., 1993). Nevertheless, the results reported in Fig. 1 are, to the best of our knowledge, the first to clearly indicate the quantitative difference between the two reconstruction schemes with regard to the performance of an ensuing statistical analysis. As a caveat however, it should be mentioned that in the current study, the difference between the two reconstruction schemes may be biased in favor of OSEM due to the fact that the
projector component of the projector-backprojector pair used in the OSEM software, is identical to the projector used in the simulation. Consequently, if both projectors are the same, the OSEM is “matched” to the simulation, whereas the FBP algorithm does not have access to the exact physics model used. This potential bias could be eliminated by using a different SPECT simulator such as SIMSET (Harrison et al., 1993) or SIMIND (Ljungberg and Strand, 1989). As these are Monte Carlo programs, their use represents a very significant computational liability even with the computer power presently available, and therefore this option was not considered for the current study where a large number of images had to be created. As can be expected, the minimal activity difference required for focus detection shows a marked decrease with both increasing focus diameter and study size. The results for the foci diameter of 8 mm, which is smaller than the experimental 10.2 mm tomographic FWHM for the HR-PH collimators on the aforementioned Toshiba camera (Kouris et al., 1993), clearly reflect the influence of the partial volume effect: high activation levels are necessary for detection of foci smaller than the system resolution. Focusing on the OSEM results, it can be seen that the superior sensitivity of an analysis based on OSEM versus FBP reconstructed images, becomes more pronounced toward smaller numbers of available scans and smaller activation foci sizes. The difference becomes marginal for the larger foci when enlarging the study size from 10 to 15 image pairs. In conclusion, our simulation shows that, using the OSEM reconstruction algorithm, activation foci with a diameter of 24 mm, and a minimal activation level of 3.75% can be detected in a study size of 10 baseline and 10 activation images. However, it should be noted in this respect that the size of some brain structures may be smaller (e.g., the anterior cingulate, which is frequently involved in cognitive activation paradigms). Consequently, the need for relatively large activation foci (e.g., a minimal diameter of about 16 mm as indicated by our simulations) might be a practical restraint imposed on SPECT neuroactivation experiments. As already stated above, in the present study a perfect alignment between all scans was assumed together with a constant activation level for each given combination of study size and foci diameters. However, in real studies a certain degree of misalignment might be inevitably present in the images due to an incomplete correction for motion or an imperfect repositioning of a subject in the camera. Some of the present authors have already investigated the performance of SPECT– SPECT, within-subject coregistration algorithms. (Van Laere et al., 2000a). These authors showed that the absolute registration errors of a six-parameter affine transformation, as is also implemented within SPM96, did not exceed 2.8 mm for the translation and 1° for the
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rotation component for SPECT perfusion images, which were acquired and processed similarly to the images created in the present study. Consequently, taking into account the fact that the FWHM for the simulated set-up with parallel hole collimators is about 10 mm, shifting the activated images with the registration error is unlikely to result in large changes in SPM’s detection of the relatively large foci that are practically amenable to investigation by a SPECT activation study. Nevertheless, the authors agree that for the simulation of real clinical studies, the issue of image mismatch is potentially important with respect to normalization of images originating from different subjects into a standard stereotactic space. Finally, it should also be taken into account that incorporation of the effect of scatter in the simulations would result in a decreased image contrast (Van Laere et al., 2000b). Apart from the several physical factors, such as attenuation and scatter, geometric detector response, detector efficiency, as well as processing parameters such as reconstruction algorithm, filtering and coregistration, another class of factors that can confound the detection of activation regions, are the kinetic and physiological properties of the perfusion tracers used. In this respect, it has been demonstrated that significant regional variations in activity uptake and kinetic pattern exist between the two most widely used 99mTechnetium labeled tracers HMPAO and ECD (Deutsch et al., 1997; Imran et al., 1998; SiennickiLantz et al., 1999), and that inhomogeneous regional washout can occur (Flores et al., 1999; Ichise et al., 1997). In addition, physiological differences in uptake exist within and between subjects (Jonsson et al., 2000; Van Laere et al., 1999). From these considerations it should be clear that for a comparable set-up in a clinical setting the minimal activation levels required for foci detection will almost certainly be higher than those resulting from our simulations. The exact impact of the aforementioned physical, processing, and physiological effects was, however, beyond the scope of the present study. The reported activation levels should therefore be considered as indicators of the best possible theoretical performance of SPECT activation studies, for comparable triple-head systems. In this respect it is clear that fan beam collimators and brain-dedicated hardware would result in a superior image resolution and total number of counts and would consequently lead to a higher lesion or activation foci detection (Moore et al., 1995). Obviously, for exploratory activation studies the exact localisation of activation foci in the brain, if any at all, are not known a priori to the experimenter, let alone their size, shape, or activation level. Therefore, the exact number of subjects one decides to include in a particular study is at best a compromise between the hope to detect any main experimental effects and the desire to minimize both the number of volunteers or
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patients to be subjected to the procedure and the significant amount of experimental work involved. Practical guidelines as to how many subjects should be included in a specific study on the basis of detection of foci, can only come from simulations as the ones presented here. On the basis of the current results and the discussion above, it may be advised for split-dose SPECT activation studies to examine groups consisting of at least 10 to 15 subjects on a high-performance gamma camera (e.g., triple-head with HR-FB collimators) with the iterative OSEM reconstruction technique. Evaluation of the Spatial Extent of Detected Foci The second goal of the present study was to investigate the evolution of the spatial extent of foci upon variation of the height threshold of the bivariate test implemented in SPM96, following statistical analysis. Figures 3A and 3B display the volumes of detected foci analyzed at a significance level of P ⬍ 0.05 when varying the study size or the foci diameter, respectively. From Fig. 3A it can be seen that the detected volumes of the foci at a particular threshold, converge systematically with increasing number of images. This is evidently due to a better sampling of the two populations represented by the rest and activation images, respectively. The three sizes of spherical foci included in this study (8, 16, and 24 mm diameter) correspond to a volume of 0.27, 2.1, and 7.2 ml, respectively, or clusters of 10.3, 82.3, and 277.6 voxels in the SPMs (voxel size 2.9 ⫻ 2.9 ⫻ 3.1 mm 3). As can be seen from Fig. 3B, at the default intensity threshold value of 0.001, the detected average foci volume reflects the true volume only for the foci of 16 mm in diameter. For the larger size of 24 mm, the height threshold has to be lowered to about 0.01. Analogously, the threshold has to be increased for smaller foci sizes. As illustrated for the 8-mm foci included in the study, the impact of the partial volume effect prohibits an accurate study of intense focal activations beyond the system resolution. These results confirm quantitatively the remark made by Friston et al. (1994b) that, on principle, a different threshold should be used depending on the suspected type of activation. The indicated levels of the height threshold could serve as a guideline in the statistical analysis of SPECT activation studies where there is an a priori hypothesis concerning the identity and consequently the size of the to-be activated foci. In this respect, it is our conviction that mainly the order of magnitude of the reported preferable height threshold is important as the exact value might be influenced by factors such as the amount of smoothing or the effect of additional image transformations applied to the images. Any attempt to quantify the actual size of activation foci would also require an assessment of
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FIG. 3. Evolution of the volume of detected foci when varying the height threshold in SPM96: (A) effect of the study size for focus diameter of 16 mm (B) effect of the foci diameter for a study size of 10 rest and activation image pairs; All images reconstructed with OSEM; SPM voxel size 2.9 ⫻ 2.9 ⫻ 3.1 mm 3; (–}–) corpus cingulum anterior, (–Œ– ) corpus cingulum posterior, (–F–) nucleus lentiformis.
those factors which is again beyond the scope of the present study. CONCLUSIONS In the present study we investigated the influence of the study size and the activation foci characteristics (dimension and activation level) in SPECT neuroactivation studies on the detection of the foci following statistical analysis with statistical parametric mapping. A simulation was performed with the use of the Hoffman software brain phantom, sinogram simulation software, and two standard image reconstruction algorithms (FBP and OSEM). For selected combinations of study sizes and foci diameters, minimal activation levels required for detection were determined. It
was found that the sensitivity of a statistical analysis based on images reconstructed using the iterative OSEM technique is significantly higher than in the case of the more conventional FBP. From our simulations we propose the number of 15 subjects as a guideline for the size of groups in SPECT activation studies where small activation intensities (⬍5%) are expected (e.g., cognitive paradigms). For more robust activation levels (around 10%), the group size can be lowered to minimally 10. Furthermore, the effect of a varying height threshold on the size of the detected foci was assessed in function of both study size and initial foci diameter. This work represents a preliminary effort to identify the validity domain of pixel-based statistical analyses of SPECT activation studies. Effects that can be ex-
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pected to lead to a reduced detection of activation foci and that are currently not incorporated in the simulations include scatter and the effect of (imperfect) normalization of multiple-subject images into a stereotactic space. On the other hand, the modeling of fan beam collimators and brain-dedicated hardware will yield an increased image resolution and total number of counts, thus positively influencing the detection of foci. The presented methodology provides a powerful tool to simulate and optimize these and other parameters in the experimental design of brain activation studies in general and to estimate the theoretical performance of SPECT activation studies in particular. ACKNOWLEDGMENTS The authors thank E. Nolf for making available the MEDCON medical image conversion software and Nuclear Diagnostics and SUN microsystems for their technical support in the implementation of image fusion in the Division of Nuclear Medicine of the Ghent University Hospital. Y. D’Asseler and M. Koole are acknowledged for their fruitful discussions.
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