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Despite the advent of sophisticated image analysis algorithms, most SPECT (Single Photon Emission Computerized Tomography)cerebral perfusion studies are assessed visually, leading to unavoidable and significant inter and intra-observer variability. Here, we present an automatic method for evaluating SPECT studies based on a computerized atlas of normal regional cerebral bloodflow(rCBF). To generate the atlas, normal(screened volunteers)brain SPECT studies are registered with an affine transformation to one of them arbitrarily selected as reference to remove any size and orientation variations that are assumed irrelevant for our analysis. Then a smooth non-linear registration is performed to reveal the local activity pattern displacement among the normal subjects. By computing and applying the mean displacement to the reference SPECT image, one obtain the atlas that is the normal mean distribution of the rCBF(up to an affine transformation difference). To complete the atlas we add the intensity variance with the displacement mean and variance of the activity pattern. To investigate a patient's condition, we proceed similarly to the atlas construction phase. We first register the patient's SPECT volume to the atlas with an affine transformation. Then the algorithm computes the non-linear 3D displacement of each voxel needed for an almost perfect shape (but not intensity)fit with the atlas. For each brain voxel, if the intensity difference between the atlas and the registered patient is higher than normal differences then this voxel is counted as "abnormal" and similarly if the 3D motion necessary to move the voxel to its registered position is not within the normal displacements. Our hypothesis is that this number of abnormal voxels discriminates between normal and abnormal studies. A Markovian segmentation algorithm that we have presented elsewhere is also used to identify the white and gray matters for regional analysis. We validated this approachusing 23 SPECT perfusion studies (99mTc ECD)selected visually for clear diffuse anomalies (a much more stringent test than "easy" focal lesions detection) and 21 normal studies. A leave-one-out strategy was used to test our approach to avoid any bias. Based on the number of "abnormal" voxels, two simple supervised classifiers were tested:(1)minimum distance-to-mean and (2)Bayesian. A voxel was considered "abnormal" if its P value with respect to the atlas was lower that 0.01(1%). The results show that for the whole brain, a combination of the number of intensity and displacement "abnormal" voxel is a powerful discriminant with a 91% classification rate. If we focus only on the voxels in the segmented gray matter the rates are slighty higher.
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