APPLICATION OF SCATTER AND ATTENUATION CORRECTION TO EMISSION TOMOGRAPHY IMAGES USING INFERRED ANATOMY FROM ATLAS

Abstract: A method of applying scatter and attenuation correction to emission tomography images of a region of interest of a patient under observation comprises the steps of aligning a three-dimensional computer model representing the density distribution within the region of interest with the emission tomography images (56, 58) and applying scatter and attenuation correction to the emission tomography images using the aligned computer model as a guide (60).

Description: APPLICATION OF SCATTER AND ATTENUATION CORRECTION TO EMISSION TOMOGRAPHY IMAGES USING INFERRED ANATOMY FROM ATLAS Technical Field The present invention relates to emission tomography and in particular to a method and apparatus for applying scatter and attenuation correction to emission tomography images using anatomy inferred from an atlas. Background Art Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) are nuclear medicine diagnostic imaging techniques used to measure the three-dimensional distribution of a radiopharmaceutical within the body. Brain SPECT and PET imaging techniques are primarily used to measure regional cerebral blood flow in a patient injected with a radiopharmaceutical to assist in the evaluation of stroke and the diagnosis of dementias such as Alzheimer's disease. Although SPECT and PET are useful imaging techniques, their poor quantitative accuracy has been an obstacle in the ability to achieve increased diagnostic reliability. Quantitative accuracy of brain SPECT and PET imaging has however been improved significantly through the application of scatter and attenuation correction to SPECT and PET brain images. To be sufficiently accurate, the application of scatter and attenuation correction to SPECT and PET brain images must be guided by the distribution of density within the head. Unfortunately, the distribution of density within the head cannot be obtained from SPECT and PET brain scans and therefore, separate measurements are required. Transmission imaging has been used to measure the distribution of density within the head to allow scatter and attenuation correction to be applied to SPECT and PET brain images. Unfortunately, the hardware necessary for making transmission measurements is complex, unreliable and requires extensive maintenance. Also, the need to make transmission imaging measurements in addition to the SPECT or PET brain images, increases the time required to complete the overall imaging procedure. SPECT and PET imaging procedures are themselves lengthy and require a patient to remain motionless to ensure accurate brain images. For sick and elderly patients, this is a difficult task. Adding to the length of the imaging procedure increases the likelihood that patients will not remain motionless. Movement of a patient during the transmission imaging procedure results in inaccurate measurements of the distribution of density within the head. This of course provides an inaccurate guide for the application of scatter and attenuation correction to SPECT and PET brain images. Accordingly, improved methods to increase the diagnostic reliability of emission tomography images are desired. It is therefore an object of the present invention to provide a novel method and apparatus for applying scatter and attenuation correction to emission tomography images. Disclosure Of The Invention Broadly stated, the present invention provides a method and apparatus for applying scatter and attenuation correction to emission tomography images which estimates or "infers" the distribution of density within a region of interest of a patient under observation using a three-dimensional computer model of the region of interest. It has been found that scatter and attenuation correction guided by a computer model of the region of interest under observation produces results similar to those when using transmission images of the actual region of interest as the guide to the application of scatter and attenuation correction. According to one aspect of the present invention there is provided a method of applying scatter and attenuation correction to emission tomography images of a region of interest of a subject under observation comprising the steps of: aligning a three-dimensional computer model representing the density distribution within said region of interest with said emission tomography images; and applying scatter and attenuation correction to said emission tomography images using said aligned computer model as a guide. In a preferred embodiment, the computer model is in the form of a two- component atlas. During the aligning step, a functional component of the atlas is firstly aligned with the emission tomography images to generate a set of spatial transformation parameters. Following this, an anatomical component of the atlas is aligned with the emission tomography images using the set of spatial transformation parameters. The atlas may be selected from a database of atlases based on degree of registration with the emission tomography images. Alternatively, multiple atlases maybe combined to yield a resultant atlas which better registers with the emission tomography images. According to another aspect of the present invention there is provided in an emission tomography imaging system where emission tomography images of a region of interest of a subject are taken for analysis and are corrected for scatter and attenuation, the improvement comprising: using a three-dimensional computer model approximating the density distribution within the region of interest as a guide to the application of scatter and attenuation correction. According to still yet another aspect of the present invention there is provided an emission tomography image processing system comprising: memory storing emission tomography images of a region of interest of a subject; said memory also storing at least one three-dimensional computer model of said region of interest, said computer model representing the density distribution within said region of interest; and a processor for registering said computer model with said emission tomography images and for applying scatter and attenuation correction to said emission tomography images using said registered computer model as a guide. According to still yet another aspect of the present invention there is provided an emission tomography imaging system comprising: means for taking emission tomography images of a region of interest of a subject to form a three-dimensional image of said region of interest; memory to store said emission tomography images, said memory also storing at least one three-dimensional computer model of said region of interest, said computer model representing the density distribution within said region of interest; and a processor for aligning said computer model with said emission tomography images and for applying scatter and attenuation correction to said emission tomography images using said aligned computer model as a guide. According to still yet another aspect of the present invention there is provided a computer readable medium including computer program code for applying scatter and attenuation correction to emission tomography images of a region of interest of a subject, said computer readable medium including: computer program code for aligning a three-dimensional computer model representing the density distribution within said region of interest with said emission tomography images; and computer program code for applying scatter and attenuation correction to said emission tomography images using said aligned computer model as a guide. The present invention provides advantages in that by using a three- dimensional computer model of the region of interest of a subject under observation that approximates its density as a guide for the application of scatter and attenuation correction to emission tomography images, the need for transmission imaging is obviated. Therefore, in the case of SPECT and PET imaging, the imaging procedures do not need to be lengthened. Also, since the distribution of density in the region of interest under observation is approximated by a three-dimensional computer model, additional hardware is not required to create the guide for the application of scatter and attenuation correction. This makes the present invention significantly less expensive and more flexible than transmission imaging systems. In addition, since a three-dimensional computer model of the region of interest under observation is used as the guide for the application of scatter and attenuation correction, scatter and attenuation correction can be applied retrospectively to existing databases which include significant numbers of emission tomography images for which no transmission imaging measurements were acquired. Brief Description Of The Drawings An embodiment of the present invention will now be described more fully with reference to the accompanying drawings in which: Figure la shows a two-dimensional emission tomography brain image without the application of scatter and attenuation correction; Figure lb shows the two-dimensional image of Figure la with the application of scatter and attenuation correction; Figure lc is a two-dimensional transmission brain image; Figures Id and le are two-dimensional emission tomography brain images with appurtenant anatomy derived from transmission images applied thereto; Figure 2 is a block diagram showing a method for applying scatter and attenuation correction to emission tomography images in accordance with the present invention; Figure 3a shows a two-dimensional emission tomography brain image of a head phantom with scatter and attenuation correction; Figure 3b is a two-dimensional transmission brain image; Figures 3c and 3d are two-dimensional emission tomography brain images with appurtenant anatomy derived from transmission images applied thereto; Figures 3e and 3 f are two-dimensional emission tomography brain images with inferred anatomy derived from an atlas applied thereto; Figures 4 and 5 show two-dimensional brain images and a graph comparing uniform appurtenant anatomy, uniform inferred anatomy and brain contour; Figure 6 shows two-dimensional brain images with non-uniform appurtenant and non-uniform inferred anatomy applied thereto; Figures 7 and 8 show quantitative evaluation of head phantom SPECT reconstructions; Figure 9 shows two-dimensionl SPECT brain images; Figure 10 shows SPECT brain images superimposed onto transmission reconstruction and inferred anatomy; Figure 11 shows profiles taken through reconstructions of a SPECT brain image; Figure 12 is a graph showing a comparison of regional cerebral blood flow from reconstructed SPECT images guided by inferred anatomy and transmission scans; and Figure 13 is a graph showing the correlation between the reconstructed SPECT images of Figure 12. Best Mode for Carrying Out The Invention During emission tomography imaging such as for example SPECT and PET, a patient is injected with a radiopharmaceutical. Two-dimensional images or projections of a region of interest (ROI) of a patient are taken along an arbitrary axis. The projections are digitized and conveyed to a computer for storage in a three-dimensional array in computer memory to reconstruct a three-dimensional image of the region of interest. Once the three- dimensional image is reconstructed, two-dimensional slices of the region of interest can be viewed along virtually any arbitrary axis using conventional software. Figure la shows a two-dimensional emission tomography brain image. During the imaging procedure, the emissions from the radiopharmaceutical are scattered and/or attenuated by different density tissue, air cavities and/or bones in the region of interest under observation. As can be seen in Figure la, scattering and attenuation of radiopharmaceuticals affects quantitative image quality. As a result, acquired emission tomography images are often unreliable. Applying scatter and attenuation correction to emission tomography images, using transmission images of the same region of interest taken during the same imaging procedure, is known but suffers from the disadvantages discussed previously. Figure lb shows the two-dimensional image of Figure la with scatter and attenuation correction applied using transmission images taken of the same anatomy. Figure lc shows an example of a transmission image and Figures Id and le show appurtenant anatomy (sagittal and transverse respectively) derived from the transmission images applied to emission tomography brain images. In the present invention, a three-dimensional computer model or "atlas" of the region of interest, which provides accurate density distribution of the region of interest, is stored in a database in computer memory and is used as a guide for the application of scatter and attenuation correction to emission tomography images. In the present embodiment, the atlas includes two components, namely a functional component simulating a SPECT OR PET scan of the region of interest and an anatomical component simulating a transmission scan of the region of interest. The atlas can be created from existing transmission images or x-ray CT scans of similar regions of interest from other patients and averaged to form a suitable computer model or atlas. Multiple models for each region of interest and models for different regions of interest can be stored in the database and accessed individually during scatter and attenuation correction of emission tomography images. Since a computer model of the region of interest is used, additional hardware and procedure time is not required to apply scatter and attenuation correction to emission tomography images. An example of the application of scatter and attenuation correction to emission tomography brain images in accordance with the present invention will now be described. Referring now to Figure 2, a block diagram illustrating the present method for applying scatter and attenuation correction to emission tomography brain images is shown. Initially, emission tomography brain images of a patient (block 50) are acquired in a known manner. The acquired brain images are in the form of two-dimensional projections of the radiopharmaceutical distribution in the brain. Initially, a preliminary reconstruction of the acquired brain images is performed to digitize and assemble the two-dimensional projections into a three-dimensional array in computer memory to create a three-dimensional image of the brain (block 52). An atlas of the head (the region of interest in this case) is then downloaded from the database into a datafile (block 54) and the functional component of the head atlas is identified. Once identified, the functional component of the head atlas, in this embodiment the brain component, is copied to a three-dimensional array. Following this, an alignment procedure to register spatially the brain component of the head atlas with the three-dimensional brain image is performed (block 56). In the present embodiment, a simplex algorithm is used to register spatially, the brain component of the head atlas with the brain image as described in "Numerical Recipes In C" by Press et al, 2 nd edition, New York, New York, Cambridge University Press, 1992. Those of skill in the art will however appreciate that other registration algorithms can be used to align the brain component of the head atlas to the three- dimensional brain image. During this alignment procedure, a set of 3D spatial transformation parameters representing the three-dimensional transformations, including but not limited to rotation, shifting and scaling, that are necessary to register the brain component of the head atlas with the three-dimensional brain image, is calculated and is stored in the computer memory as a matrix. Once the set of 3D transformation parameters is calculated and stored, the 3D spatial transformation parameters in the set are applied to the anatomical component of the head atlas to register it with the three-dimensional brain image (block 58). With the anatomical component of the head atlas aligned with the three-dimensional brain image, the atlas can be used as a density distribution guide to the application of scatter and attenuation correction to the three-dimensional brain image. With an accurate density distribution guide established, scatter correction is applied to the three-dimensional brain image followed by attenuation correction in a known manner (block 60). Once scatter and attenuation correction has been applied to the three- dimensional brain image, the brain image is reconstructed into a three-dimensional array in computer memory to complete the image correction process (block 62). The corrected three- dimensional brain image can then be analyzed (block 64). Appendix A includes pseudocode representing the above-identified process. Figures 3a to 3f show a comparison of two-dimensional brain images of a head phantom with anatomy derived from transmission images (appurtenant anatomy) and anatomy derived from a head atlas (inferred anatomy) applied. The present method and apparatus was tested using an anthropomorphic head phantom modeling soft tissue, hard tissue and air cavities within a skull and including a two- compartment brain reservoir. The two compartments of the reservoir were separately filled with two water solutions of Tc-99m, having a specific activity ratio of 4:1. Fan-beam SPECT was acquired followed by a Tc-99m transmission scan sixty hours later. The reconstructed transmission image is referred to as appurtenant anatomy. Five scatter and attenuation correction schemes were evaluated based on non- uniform appurtenant anatomy, uniform appurtenant anatomy, non-uniform inferred anatomy, uniform inferred anatomy, and uniform brain contour. For uniform scatter and attenuation correction, the inferred and appurtenant anatomies were segmented and assigned uniform attenuation coefficients of soft tissue (0.15cm "1 for Tc-99m). A SPECT reconstruction of the head phantom was similarly processed to facilitate scatter and attenuation correction guided by brain contour. Scatter correction was based on a non-stationary deconvolution scatter subtraction as described in the paper authored by Stodilka et al entitled "The Relative Contributions of Scatter and Attenuation Correction Toward Brain SPECT Quantification", Phys Med Biol, 1998. Attenuation correction/reconstruction was subsequently performed by ordered-subsets expectation-maximization as set out in the paper authored by Hudson HM et al entitled "Accelerated Image Reconstruction using Ordered Subsets of Projection Data", IEEE Trans Med Imaging 13 601-609, 1994. Although particular examples of scatter correction, attenuation correction and reconstruction algorithms are described, those of skill in the art will appreciate that other scatter correction, attenuation correction and reconstruction methods can be used. Uniform scatter and attenuation correction should be guided by the contour of the attenuating medium if an acceptable level of accuracy for objective diagnosis is to be expected. Figures 4 and 5 show that uniform appurtenant anatomy is better approximated by uniform inferred anatomy than by brain contour. As can be seen in Figure 6, non-uniform appurtenant anatomy and non-uniform inferred anatomy are similar. Head phantom SPECT reconstructions were quantitatively evaluated by four metrics, namely bias, uniformity, contrast-recovery, and relative quantification (see Figures 7 and 8). As will be appreciated, scatter and attenuation correction guided by inferred anatomy provides quantitative accuracy that is distinctively superior to scatter and attenuation correction guided by brain contour. Application of scatter and attenuation correction to emission tomography images of anatomy other than the head can also be performed. For example, inferred anatomy can also be used to apply scatter and attenuation correction to cardiac images. During construction of the cardiac atlas, an anatomical component of the cardiac atlas representing the anatomical features of the thorax is created and includes: soft-tissues such as the heart, liver, muscle, and fat; very low-density soft-tissues such as the lungs; and high-density tissues such as bone and cartilage in the ribs and spine. A functional component of the cardiac atlas is also created and simulates a SPECT or PET cardiac image. Appropriate data to construct the cardiac atlas can be obtained in a variety of ways including, but not limited to, imaging a phantom or human subject by X- ray CT, MRI, or gamma-camera transmission computed tomography; or computer simulation. Also, the cardiac atlas can be constructed by amalgamating a plurality of patient scans. The procedure of using inferred cardiac anatomy to apply scatter and attenuation correction to cardiac images remains the same as with brain imaging described above. First, a preliminary reconstruction of the patient's cardiac SPECT data is performed. The functional component of the cardiac atlas is then registered to the preliminary reconstruction. The registration includes a spatial transformation that may include shifting, rotation, scaling, and/or non-linear operations such as warping. The registration procedure calculates a matrix representing the spatial transformation that maps atlas space into patient- specific space. Once the matrix has been calculated, the matrix is applied to the anatomical component of the cardiac atlas, thus inferring anatomy in the chest. Although the above methods describe the use of a single generic atlas of the anatomy under observation, those of skill in the art will appreciate that custom atlases can be developed and stored in the database. For example, disease specific atlases such as an Alzheimer's disease atlas or a stroke atlas can be developed and used when correcting emission tomography images of patients suffering from those diseases. The disease specific atlases may be tracer or lesion specific to allow for local concavities in radiopharmaceutical uptake. Patients with severe Alzheimer's or Pick's disease do not have normal cerebral blood flow. Areas with flow deficits can limit the accuracy of the registration of the emission tomography images with the functional component of the head atlas if the head atlas assumes normal blood flow and hence uniform radiopharmaceutcial uptake. Atlases can also be developed to take into account physical traits such as for example, an exceptionally large nasal sinus. During scatter and attenuation correction, the operator can select the appropriate atlas to use. Alternatively, the atlas can be selected automatically by computer software. In this case, the software, performs a preliminary reconstruction using each custom atlas and registers the atlas to the preliminary reconstruction to measure the accuracy of the registration. The atlas with the highest registration accuracy is then selected. If desired, the software can use fuzzy logic, theoretical calculations or other criteria to combine two or more atlases to yield a single resultant atlas, which provides a better degree of registration. EXAMPLE Example 1; Inferred Anatomy and Brain Imaging The following example is described for the purposes of illustration and is not intended to limit the scope of the present invention. Inferring Anatomy From A Head Atlas A head atlas was prepared as follows. A Zubal three-dimensional digitized MRI head phantom [Zubal et al 1994] was segmented to produce two data sets, namely a SPECT atlas simulating a SPECT scan of the phantom, and an anatomical atlas simulating a transmission scan of the phantom. The SPECT atlas consisted of voxels containing gray- matter and white-matter, to which 99m Tc-HMPAO relative uptakes of 4 and 1 had been assigned respectively. The anatomical atlas consisted of voxels containing hard-tissue, soft- tissue and nasal sinus, to which the correspondent 140keV narrow-beam attenuation coefficients of 0.25, 0.15, and 0.075 cm "1 were assigned. A patient SPECT scan was then reconstructed without scatter and attenuation correction. Facial activity was removed to yield a data set referred to as the preliminary patient reconstruction. The SPECT atlas was then registered to the preliminary patient reconstruction and the spatial transformation was recorded. This transformation was then applied to the anatomical atlas, thus inferring the location of the patient's soft-tissue, hard- tissue and air cavities (see Figure 9). A general-purpose radiological analysis program (Hermes, Nuclear Diagnostics, Stockholm, Sweden) was used to perform the unimodality registration. The large variation in head orientation necessitated that a manual registration be first performed. This was followed by an automated refinement. The cost function of the automated registration was defined as the sum of absolute count differences [Hoh et al 1993]. A global minimum was sought by a simplex search [Nelder and Mead 1965] within a parameter space consisting of rotating, shifting, and linear scaling in x, y, and z directions [Holman et al 1991, Slomka et al 1995]. Sequential Transmission And Emission Imaging Ten dementia patients (5 females and 5 males, with a mean age of 64.3 years) were analyzed. For each patient, a transmission scan was first acquired. Patients became very relaxed during the quiet transmission scan, and were then injected with 740 MBq of 99m Tc-HMPAO. The SPECT procedure was started approximately 5 minutes post-injection. The SPECT system, which has transmission capabilities, is described in detail [Kemp et al 1995]. It consists of a General Electric 400AC/T gamma camera (General Electric, Milwaukee, WI) with a 409.6 mm diameter circular field-of-view. Projections were acquired through a fan-beam collimator (Nuclear Fields, Des Plaines, IL) having a 600 mm focal length and 1.5 mm flat-to-flat hexagonal hole width. The transmission component includes a frame mounted onto the camera's collimator that holds a tantalum-collimated 99m Tc line source along the focal line of the fan-beam collimator. Collimation of both the line source and camera minimizes scatter. As a result, the transmission system effectively measures narrow-beam attenuation coefficients [Tsui et al 1989, Kemp et al 1995]. SPECT and transmission scans were acquired with a 20% energy window, centered on the 99n c photopeak of 140 keV. The scans consisted of 128 projections, equally spaced over 360°. Each circular projection was acquired into a 128 x 128 pixel square matrix (1 pixel = 3.2 mm). Both transmission and SPECT scans were 10 seconds per projection and count rates were approximately 70 and 1.5 kcounts per second, respectively. All scans were corrected for uniformity using 100 million count flood images, and transmission scans were normalized to 50 million count blank images. Radii of rotation varied among the patients; the smallest being 170 mm and the largest being 205 mm. Prior to reconstruction, all scans were rebinned to object-plane parallel-hole geometry via two-dimensional cubic interpolation. Scatter And Attenuation Correction And Reconstruction The SPECT data were reconstructed using a maximum-likelihood estimator with an unregularized 32-level ordered subset [Hudson and Larkin 1994] implementation of the expectation maximization algorithm [Shepp and Vardi 1982, Lange and Carson 1984] (OSEM). The four projections that were used per sub-iteration were equally spaced about 360°. Attenuation was modelled in the matched projector/backprojector pair, and a scatter estimate [Stodilka et al 1998b] was added as an a priori background following forward projection [Lange and Carson 1984, Bowsher et al 1996, Kadrmas et al 1998]. Both scatter and attenuation modelling incorporated the narrow-beam attenuation coefficients from transmission imaging or inferred anatomy. Detector response was not included. Four iterations of OSEM were used, following initialization with a uniform [Nunez and Llacer 1990] support prior derived from transmission reconstruction or inferred anatomy. Reconstructions were then post- filtered [Nunez and Llacer 1990] using a three-dimensional Butterworth filter with an order of 8 and cutoff at 0.42 cm "1 . The transmission data were also reconstructed by the emission OSEM algorithm, following blank scan normalization and log- transformation. Line source collimation, coupled with limitations in detector count rate capability and patient compliance resulted in less than ideal transmission statistics. To reduce the effects of transmission imaging noise propagation into the SPECT reconstruction [Xu et al 1991, Tung and Gullberg 1994], the transmission reconstructions were segmented as follows. Soft-tissue in the reconstructed transmission volumes was forced to have uniform density. First, a large region of interest (ROI) was drawn around soft-tissue regions, and mean and variance estimates were calculated. Then, all voxels having count densities within ±2 standard deviations of this mean were assigned to 0.15cm "1 . Thus, the transmission reconstructions were characterized by noiseless soft-tissue, yet featured hard-tissue and air cavities. Template-Based Quantification Previous work has identified that a major confound to reproducible quantification, originates from manual and threshold-dependent placement of anatomical ROIs onto SPECT scans [Msaki et al 1998]. To reduce this subjective source of error, a normal template [Msaki et al 1998] onto which twelve bilateral volumetric ROIs are demarcated [Karbe et al 1994] was used. The ability to store the template and its ROIs ensures reproducibility of the analysis. This quantification procedure also introduces standardization to the analysis, which facilitates the exchange of data among different institutions [Evans et al 1988]. All reconstructed scans were registered to the normal template, herein referred to as "spatial normalization". Prior to spatial normalization, voxels previously identified as facial activity were set to zero. Following superposition of the template ROIs onto each scan, the cortical rCBF for each ROI was normalized to cerebellar rCBF [Karbe et al 1994] and corrected for blood flow-dependent tracer reflux [Lassen et al 1988]. Analysis of the absolute concentration of radiopharmaceutical was not performed since currently, absolute rCBF quantification is seldom used in SPECT [Bakker and Pauwels 1997]. Quantitative Error Analysis A sample of inferred anatomy and transmission reconstruction is illustrated in Figure 10. In Figure 11, profiles through the SPECT reconstructions guided by transmission scans and inferred anatomy are presented. The profiles have not been normalized to cerebellar count density. The means and standard errors of regional cerebral blood flow from SPECT scans guided by inferred anatomy and transmission scans were compared ROI-by-ROI (see Figure 12). Statistical analysis was also performed ROI-by-ROI using repeated analysis of variance to determine where there were significant differences between the inferred anatomy versus transmission-guided reconstruction methods. Pooled-ROI and ROI-dependent correlation coefficients were calculated between inferred anatomy reconstruction and transmission-guided SPECT reconstructions. Figure 13 shows the correlation between the two reconstruction and quantification methods by pooling all ROIs and patients together. The p value for significance was set to 0.05 for all tests. Inferred Anatomy Error Propagation Analysis Four sources of error were identified that contribute to discrepancies in ROI quantification: (1) inferred anatomy-guided scatter correction; (2) inferred anatomy-guided attenuation correction; (3) inferred anatomy-guided spatial normalization, which is equivalent to ROI misplacement; and (4) patient motion between transmission and SPECT scans. The first two are inherent limitations to the principle of inferred anatomy, whereas the last two represent artifactual exaggerations of errors in the context of this example. The first three sources of error, namely scatter, attenuation, and ROI misplacement were measured. The propagation of ROI quantification to differences between inferred anatomy scatter correction and transmission-guided scatter correction were analyzed by performing: (1) inferred anatomy-derived scatter correction; (2) transmission-derived attenuation correction; and (3) applying the spatial normalization calculated to be optimal for registering the transmission-derived SPECT reconstruction to the quantification template. A similar analysis for evaluating the effects of inferred anatomy-derived attenuation correction was carried out by performing: (1) transmission -derived scatter correction; (2) inferred-anatomy derived attenuation correction; and (3) applying the spatial normalization calculated to be optimal for registering the transmission-derived SPECT reconstruction to the quantification template. The effects of ROI misplacement were quantified by performing transmission- derived scatter and attenuation correction, and then applying the spatial transformation calculated to be optimal for registering the inferred anatomy-derived SPECT reconstruction to the quantification template. Thus, the full propagation analysis resulted in three reconstructions, each of which was quantitatively compared (via the above-described template-based quantification procedure) with the "gold-standard" transmission-derived SPECT reconstruction and spatial normalization. This procedure was performed for the ten patients, and the results averaged. However, once the errors due to scatter, attenuation, and ROI misplacement were separately quantified, their totals were found to be less than the errors caused by the full inferred anatomy protocol. This additional source of error is termed "unaccountable" in Table 2, and is believed to be caused by patient motion. Qualitative Analysis A sample comparison of inferred anatomy and transmission reconstruction is shown in Figure 10 for mid-sagittal and cortical-axial slices. Good reproduction of soft-tissue and hard-tissue at the cortical level is noted. Some discrepancy is seen near the vertex; however, this region is seldom included in quantitative analysis. Discrepancies near the nasal sinus are most marked. Fortunately, these structures mostly involve low-density areas such as air, and to a much lesser degree, soft-tissues and cartilage, which do not scatter and attenuate photons as much as higher density structures. Mid-brain profiles, shown in Figure 11, compare a SPECT reconstruction guided by transmission imaging with the same SPECT reconstruction guided by inferred anatomy. The profile was taken along the longest axis of the head, which is most sensitive to mis-registered transmission maps following scatter and attenuation correction [Huang et al 1979]. Quantitative Error Analysis Table 1 below shows the results of the repeated analysis of variance and correlation analysis comparing transmission reconstruction and quantitative and inferred anatomy-guided reconstruction and quantification. Left frontal and central sulcus ROIs showed the highest probability of a true difference, reaching statistical significance with p=0.001 and 0.002, respectively. These ROIs also had the highest correlation coefficients relating transmission reconstruction and quantification and inferred anatomy-guided SPECT reconstruction and quantification. This increased correlation may be an artifact of the increased differences in the ROI means. Interestingly, the left frontal and central sulcus ROIs were also found to have marked rCBF deficits, suggesting that inferred anatomy may have difficulties near regions with substantially reduced radiopharmaceutical uptake. Figure 12 shows the means and standard errors for transmission reconstruction and inferred anatomy in each of the 12 ROIs, averaged across the entire population. The mean absolute difference for all ROIs across the whole population was 7.5%. Correlation for all ROIs and all patients was found to be high: 1^=0.92 as illustrated in Figure 13. TABLE 1 Region ANOVA Paired sample Paired sample Significance correlation Significance Left frontal 0.001 0.965 <0.001 Right frontal 0.088 0.953 <0.001 Left central sulcus 0.002 0.957 0.001 Right central sulcus 0.234 0.937 <0.001 Left parietal 0.092 0.772 0.009 Right parietal 0.473 0.862 0.001 Left temporal 0.957 0.939 0.001 Right temporal 0.397 0.835 0.003 Left occipital 0.271 0.835 0.003 Right occipital 0.073 0.858 0.001 Left cerebellar 0.545 0.938 0.001 Right cerebellar 0.603 0.925 O.OOl Error Propag lation Analysis The results from the propagation analysis are presented in Table 2 below. These results are presented as errors relative to the "gold-standard" transmission reconstruction-guided reconstruction and spatial normalization. Three sources of error due to inferring anatomy were analyzed namely, errors in scatter distribution estimates, errors due to misguided attenuation compensation, and errors due to region-of-interest misplacement. The error components, averaged across ten patients, are shown for each of the twelve bilateral regions-of-interest. The table summarizes the percentage that each of these error sources contributed to the total quantitative differences between SPECT reconstructions guided by transmission scans or inferred anatomy. The fourth numerical column indicates the percentage of total differences that could not be accounted for by inferring anatomy. On average, it was found that errors in scatter distribution estimates results in approximately 10.0% of the total quantitative error; attenuation correction: 36.6%, and ROI misplacement: 27.0%. The relative contributions of inferred anatomy-derived scatter and attenuation correction to the total error seem credible. Compensating for attenuation is of considerably greater consequence than removing scattered photons [Rosenthal et al 1995]. Approximately 26.5% of the total discrepancy between inferred anatomy and transmission imaging could not be accounted for in the error propagation analysis of scatter, attenuation, and ROI misplacement but is belived to be as a result of patient motion during data acquisition. TABLE 2 Region Scatter Attenuation Region Unaccountable correction % correction % misplacement % error % Left frontal 6.1 44.5 28.6 20.8 Right frontal 12.0 49.9 29.4 8.7 Left central sulcus 5.7 40.4 17.9 36.0 Right central sulcus 6.3 34.3 16.0 43.4 Left parietal 7.9 25.7 30.3 36.1 Right parietal 10.0 34.1 25.9 30.0 Left temporal 10.0 38.3 20.0 31.8 Right temporal 13.8 27.0 30.7 28.5 Left occipital 13.2 23.2 27.6 36.0 Right occipital 8.3 22.5 29.0 40.2 Left cerebellar 13.5 48.7 31.9 5.8 Right cerebellar 12.5 50.2 36.4 0.8 Average 10.0 36.6 27.0 26.5 Qualitative and Ou; antitative Compa risons Comparing inferred anatomy with transmission reconstructions indicated good reproduction of soft-tissue and hard-tissue in cortical areas for all ten patients. However, in many scans, a discrepancy was indicated near the sinus cavity, as shown in Figure 10. Despite this, inferred anatomy is more robust and accurate in providing estimates of underlying tissue distribution than fitting ellipses to photopeak emission data. The technique of fitting ellipses depends on adequate facial activity since the contours of the brain and head differ so considerably at the level of the cerebellum. Facial activity should not form the basis for estimating underlying tissue distributions since uptake varies with radiopharmaceutical and time between injection and scanning [Leveille et al 1992, Van Dyck et al 1996], making it an unreliable dependency. Parenthetically, the qualitative similarities demonstrated between inferred anatomy and transmission reconstruction indicate confidence in accurately guiding scatter and attenuation correction. However, it is important to note that similar shape is a sufficient, but not necessary, prerequisite for accurate scatter and attenuation correction [Welch et al 1997, Natterer 1993]. Slight truncation effects are noticed on the transmission images for three of the ten scans. Truncation occurred for kyphotic patients with broad shoulders or with short necks. This limitation was generally restricted to transaxial slices below the level of the cerebellum or very near its base, where the gamma camera's circular field-of-view proved to be inconvenient. The truncation only involved nasal cartilage, and is therefore not expected to significantly impact results, as is demonstrated in the quantitative accuracy exhibited at the cerebellar level (see Figure 12). Inferred anatomy had difficulties in regions with marked rCBF deficit, such as the left frontal lobe. Although frontal lobes generally exhibit high variability in HMPAO uptake [Deutsch et al 1997], only the left frontal lobe had statistically significant error. This suggests that regional quantitative errors incurred by inferred anatomy are associated with rCBF deficits. However, previous work demonstrates that they may also be sensitive to spatial region. Achieving good quantitative accuracy in extended sources is often difficult and this is particularly true with peripheral ROIs, where reconstructed activity is most sensitive to misregistration of the attenuating medium [Huang et al 1979]. For example, in the brain a mismatch between emission and transmission data no greater than 5mm can produce a 10% error in a 10 mm thick peripheral cortical ROI [Andersson et al 1995b, Huang et al 1979]. In general, extended sources, such as the brain, are affected by misregistration more than compact sources, such as the heart [Andersson et al 1995b, McCord et al 1992, Ter-Pogossian 1992, Matsunari et al 1998]. Since the brain is elliptical, it is expected that regions along the periphery of the long axis of the head will be more sensitive to attenuation map misregistration than those along the short axis. Although a preferred embodiment of the present invention has been described, those of skill in the art will appreciate that variations and modifications may be made to the present invention without departing from the spirit and scope thereof as defined by the appended claims. APPENDIX A emis_proj = //initialize 3D array. Projections are 2D, but we have //many projections. emis_proj_sc = //init 3D array. Store projections here after scatter //correction. emis_proj_sc_ac = //init 3D array. Store projections here after scatter and //attenuation correction. emis_reco_sc_ac = //init 3D array. The reconstructed 3D brain scan. tx_proj = //init 3D array. Transmission projections of anatomy. anatomy = //init 3D array. The 3D distribution of anatomy goes //here . emis_proj = do_patient_emission_scan; // first step is to acquire the patient emission scan. // The acquire data is in the form of projections of the // radiopharmaceutical distribution. // These projections require scatter and attenuation // correction. // In order to perform these corrections, an estimate of // anatomy must be obtained. Current, two methods of // obtaining an anatomy estimate are possible: if (infer==l) anatomy = infer_anatomy (emis_proj ) ; // This is our method. See the // function, below. This is what // we are patenting. βlseif (infer==0) { // Alternatively, we acquire transmission projections of // the patient. // This is very similar to x-ray CT. tx_proj = do_patient_transmission_scan; APPENDIX A anatomy = reconstruct (tx_proj) ; // These projections are // reconstructed into a 3D // distribution of anatomy. Note // that this reconstruction is // almost identical to the // emission reconstruction; // however, transmission // reconstructions do not // require scatter or attenuation // correction. // After we have our emission projections and an anatomy // estimate, we sequentially apply scatter correction and // attenuation correction to the emission data. emis_proj_sc = scatter_correction(emis_proj , anatomy) ; // Perform the scatter correction on the // emission_data. // The scatter correction requires knowing the / / anatomy . emis_proj_sc_ac = attenuation_correction (emis_proj_sc, anatomy) ; // Perform the attenuation correction on the // emission data that has been scatter corrected. // The attenuation correction requires knowing the // anatomy. emis_reco_sc_ac = reconstruct (emis_proj_sc_ac) ; // After correcting the emission data for scatter // and attenuation, perform the emission // reconstruction. Stop APPENDIX A function aligned_head_atlas = infer_anatom (emis_proj ) ; { emis_reco = // init 3D array. This will hold a preliminary emission // reconstruction that has NOT been corrected for scatter // or attenuation. head_atlas = // init 3D array. This will hold the full head // atlas (brain, skull, soft tissue) . The head // atlas is in its original orientation. head_atlas_brain = // init 3D array. This will hold the brain // component of the head atlas. This brain // component will be in its original // orientation. alignment_par = // a set of parameters that represent the 3D // transformations (including, but not limited to: // rotation, shifting, scaling) necessary to align // head_atlas_brain to emis_reco. These // transformations will be applied to head_atlas . aligned_head_atlas = // init 3D array. The alignment parameters // are applied to head_atlas. This variable // is the function's output. emis_reco = reconstruct (emis proj ) // Perform a preliminary // reconstruction of the emission // data. APPE DIX A head_atlas = load_3D_data (atlas_filename) ; // Load the head // atlas. head_atlas_brain = extract (head_atlas,brain) ; // Identify and // extract the brain // from the head // atlas. alignment_par = find_optimal_alignment (emis_reco,head_atlas_brain) ; // Calculate the 3D transformation parameters // to align head_atlas_brain to emis_reco. // This procedure calculates the optimal set // of transformation parameters. In medical // science, this alignment is known as // "registration" . aligned_head_atlas = apply_alignment (head_atlas, alignment_par) ; // Apply the alignment to head_atlas. // aligned_head_atlas // represents an anatomy estimate that is // used for guiding the scatter and // attenuation corrections. (Note that the // variable aligned_head_atlas is passed back // as anatomy to the main program.) 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Claims: What is claimed is: 1. A method of applying scatter and attenuation correction to emission tomography images of a region of interest of a subject under observation comprising the steps of: aligning a three-dimensional computer model representing the density distribution within said region of interest with said emission tomography images; and applying scatter and attenuation correction to said emission tomography images using said aligned computer model as a guide. 2. The method of claim 1 wherein said computer model is in the form of a two- component atlas. 3. The method of claim 2 wherein during said aligning step, a functional component of said atlas is firstly aligned with said emission tomography images to generate a set of spatial transformation parameters and thereafter, the anatomical component of said atlas is aligned with said emission tomography images using said set of spatial transformation parameters. 4. The method of claim 3 wherein said functional component simulates a SPECT or PET scan of said region of interest and wherein said anatomical component simulates a transmission scan of said region of interest. 5. The method of claim 4 wherein said region of interest is the head and wherein said functional component is the brain component of a head atlas. 6. The method of claim 4 wherein said region of interest is the heart, said functional component of said atlas simulating a cardiac image and said anatomical component of said atlas representing anatomical features of the thorax. 7. The method of claim 6 wherein the anatomical features of the thorax include: soft-tissues such as the heart, liver, muscle, and fat; very low-density soft-tissues such as the lungs; and high-density tissues such as bone and cartilage in the ribs and spine. 8. The method of claim 3 further comprising the step of selecting an atlas from a database of atlases prior to performing said aligning step. 9. The method of claim 8 wherein said selecting step is performed manually. 10. The method of claim 8 wherein said selecting step is performed automatically based on the degree of registration of each atlas in said database with said emission tomography images. 11. The method of claim 10 wherein the degree of registration is determined by: performing a preliminary reconstruction of each atlas; and registering the atlas to the preliminary reconstruction. 12. The method of claim 10 further comprising the step of combining multiple atlases to yield a resultant atlas that better registers with said emission tomography images. 13. The method of claim 8 wherein said database includes disease specific atlases, physical trait specific atlases and/or tracer or lesion specific atlases. 14. In an emission tomography imaging system where emission tomography images of a region of interest of a subject are taken for analysis and are corrected for scatter and attenuation, the improvement comprising: using a three-dimensional computer model approximating the density distribution within the region of interest as a guide to the application of scatter and attenuation correction. 15. The emission tomography imaging system of claim 14 wherein said computer model is in the form of a two-component atlas. 16. The emission tomography imaging system of claim 15 wherein during said aligning step, a functional component of said atlas is firstly aligned with said emission tomography images to generate a set of spatial transformation parameters and thereafter, the anatomical component of said atlas is aligned with said emission tomography images using said set of spatial transformation parameters. 17. The emission tomography imaging system of claim 16 wherein said functional component simulates a SPECT or PET scan of said region of interest and wherein said anatomical component simulates a transmission scan of said region of interest. 18. An emission tomography image processing system comprising: memory storing emission tomography images of a region of interest of a subject; said memory also storing at least one three-dimensional computer model of said region of interest, said computer model representing the density distribution within said region of interest; and a processor for registering said computer model with said emission tomography images and for applying scatter and attenuation correction to said emission tomography images using said registered computer model as a guide. 19. An emission tomography image processing system as defined in claim 18 wherein said at least one computer model is in the form of at least one two-component atlas. 20. An emission tomography image processing system as defined in claim 19 wherein said processor firstly registers a functional component of said atlas with said emission tomography images to generate a set of spatial transformation parameters and then registers the anatomical component of said atlas with said emission tomography images using said set of spatial transformation parameters. 21. An emission tomography image processing system as defined in claim 20 wherein said functional component simulates a SPECT or PET scan of said region of interest and wherein said anatomical component simulates a transmission scan of said region of interest. 22. An emission tomography image processing system as defined in claim 21 wherein said memory stores a database of atlases. 23. An emission tomography image processing system as defined in claim 22 wherein said processor selects an atlas from said database automatically based on the degree of registration of each atlas in said database with said emission tomography images. 24. An emission tomography image processing system as defined in claim 23 wherein said processor performs a preliminary reconstruction of each atlas and registers the atlas to the preliminary reconstruction to determine the degree of registration of each atlas. 25. An emission tomography image processing system as defined in claim 23 wherein said processor combines multiple atlases to yield a resultant atlas that better registers with said emission tomography images. 26. An emission tomography image processing system as defined in claim 22 wherein said database includes disease specific atlases, physical trait specific atlases and/or tracer or lesion specific atlases. 27. An emission tomography imaging system comprising: means for taking emission tomography images of a region of interest of a subject to form a three-dimensional image of said region of interest; memory to store said emission tomography images, said memory also storing at least one three-dimensional computer model of said region of interest, said computer model representing the density distribution within said region of interest; and a processor for aligning said computer model with said emission tomography images and for applying scatter and attenuation correction to said emission tomography images using said aligned computer model as a guide. 28. A computer readable medium including computer program code for applying scatter and attenuation correction to emission tomography images of a region of interest of a subject, said computer readable medium including: computer program code for aligning a three-dimensional computer model representing the density distribution within said region of interest with said emission tomography images; and computer program code for applying scatter and attenuation corrections to said emission tomography images using said aligned computer model as a guide. 29. A computer readable medium as defined in claim 28 wherein said computer program code for aligning includes: computer program code for aligning a functional component of said computer model simulating a SPECT or PET scan of said region of interest and for generating a set of spatial transformation parameters; and computer program code for aligning an anatomical component of said computer model simulating a transmission scan of said region of interest using said set of spatial transformation parameters.

Inventors: KEMP BRAD J, NICHOLSON RICHARD L, PRATO FRANK S, STODILKA ROBERT Z, KEMP, BRAD, J., NICHOLSON, RICHARD, L., PRATO, FRANK, S., STODILKA, ROBERT, Z.
Applicants: KEMP BRAD J, NICHOLSON RICHARD L, PRATO FRANK S, STODILKA ROBERT Z, KEMP, BRAD, J., NICHOLSON, RICHARD, L., PRATO, FRANK, S., STODILKA, ROBERT, Z.
Application year: 1999
Main classification: G01T1
IPC codes: G01T1