Application of the row action maximum likelihood algorithm with spherical basis functions to clinical PET imaging

Three-dimensional (3D) reconstructions from fully 3D positron emission tomography (PET) data can yield high-quality images but at a high computational cost. The 3D row action maximum likelihood algorithm (3D RAMLA) with spherically-symmetric basis functions (blobs) has recently been modified to reconstruct multi-slice 2D PET data after Fourier rebinning (FORE) but still using 3D basis functions (2.5D RAMLA). In this study 2.5D RAMLA and 3D RAMLA were applied to several patient and phantom PET datasets to assess their clinical performance. RAMLA performance was compared to that for the reconstruction techniques in routine clinical use on our PET scanners. Torso phantom and whole-body patient scans acquired on the C-PET scanner were reconstructed after FORE with filtered back-projection (FORE+FBP), the ordered subsets expectation maximization algorithm (FORE+OSEM), and FORE+2.5D RAMLA for various reconstruction parameters. The 3D Hoffman brain phantom scanned on the HEAD Penn-PET scanner was reconstructed with the 3D reprojection algorithm (3DRP) and 3D RAMLA, as well as FORE+FBP, FORE+OSEM, and FORE+2.5D RAMLA. Our results demonstrate improvement of 3D and 2.5D RAMLA with blob basis functions, compared to the reconstruction methods currently in clinical use, in terms of contrast recovery and noise, especially in regions of Limited statistics.