Lossless Compression and Linear Recovery of a System Matrix Based on a Polar Adaptive Pixel

The nondestructive characteristics of γ-photon imaging technology make it attractive potential in the industry. However, industrial detection with a large range and high resolution, iteration method, image reconstruction algorithm which is most widely used, faces challenge an overly system matrix, current compression algorithms using geometric symmetry positron emission tomography (PET) have problems complex pixel division recovery mode. Therefore, this study proposes lossless linear matrix based on polar adaptive (LCLR-PAP). Based structure ring rotation circle, field view (FOV) designed as cylinder circular slice divided into several sectors. pixels are adaptively within sector to realize from structure, angle change can be converted transformation achieve recovery. A partial partition optimized compensate for unevenness size center image. Experiments show that LCLR-PAP provide efficient solution large-scale problem, is, through simple convenient sparsity axial symmetry, compressed less than 100,000th original, fast