Brain PET Attenuation Correction without CT: An Investigation

Abstract

In the last decade, Brain PET Imaging has taken big strides in becoming an effective diagnostic tool for dementia and epilepsy disorders, particularly Alzheimer's. CT is often used to provide information for PET attenuation correction. However, for dementia patients, which often require multiple follow-ups, the elimination of CT is desirable to reduce the radiation dose. In this paper, we present a robust algorithm for PET attenuation correction without CT. The algorithm involves building a database of non-attenuation corrected (NAC) PET and CT pairs (model scans). Given a new patient's NAC PET, a learning-based algorithm is used to detect key landmarks, which are then used to select the most similar model scans. Deformable registration is then employed to warp the model CTs to the subject space, followed by a fusion step to obtain the virtual CT for attenuation correction. Besides comparing the normalized AC values with ground truth, we also use a diagnostic tool to evaluate the solution. In addition, a diagnostic evaluation is conducted by a trained nuclear medicine physician, all with promising results.