SVD reconstruction algorithm in 3D SPECT imaging

Singular value decomposition (SVD) method was used for image reconstruction in single photon emission computed tomography (SPECT). The 3D system transition matrix and the projection data were produced by Monte-Carlo simulation based on NCAT human torso phantom. Generalized matrix inverse of system transition matrix was computed. NMSE and Contrast parameters were chosen to evaluate the image quality. The relationship between reserve singular value number and reconstructed image quality is discussed. Reconstructed image in best quality was obtained when the optimized number of preserved singular value was chosen, and compared with routine OSEM reconstruction methods. Results show that SVD reconstruction algorithm, which can reduce noise influence effectively and improve the reconstruction result greatly, is a valuable image reconstruction algorithm. It can be improved to solve the coded mask SPECT imaging problem.