Detection of diffuse abnormal perfusion in SPECT using a normal brain atlas.

Visual assessment, with significant inter- or intraobserver variability, is still the norm for the evaluation of Single Photon Emission Computerized Tomography (SPECT) cerebral perfusion studies. We present in this paper an automated method for screening SPECT studies to detect diffuse disseminated abnormalities based on a computerized atlas of normal regional cerebral blood flow (rCBF). To generate the atlas, a set of normal brain SPECT studies are registered together. The atlas contains the intensity mean, the nonlinear displacement mean, and the variance of the activity pattern. A patient is then evaluated by registering his or her SPECT volume to the atlas and computing the nonlinear 3-D displacement of each voxel needed for the best shape fit to it. A voxel is counted as "abnormal" if the intensity difference between the atlas and the registered patient (or if the 3-D motion necessary to move the voxel to its registered position) is superior to 3 SD of normal mean. The number of abnormal voxels is used to classify studies. We validated this approach on 24 SPECT perfusion studies selected visually for having clear diffuse anomalies and 21 normal studies. A Markovian segmentation algorithm is also used to identify the white and gray matters for regional analysis. Based on the number of abnormal voxels, two supervised classifiers were tested: (1) minimum distance-to-mean and (2) Bayesian. The analysis of the intensity and displacement "abnormal" voxels allow one to achieve an 80% correct classification rate for the whole brain and a 93% rate if we consider only voxels in the segmented gray matter region.