A comparison of transform and iterative reconstruction techniques for a volume-imaging PET scanner with a large axial acceptance angle

The authors present the results of comparing 3D transform and iterative reconstruction methods with measured PET data from the HEAD PENN-PET scanner, which has a very large axial acceptance angle of 27 deg. The algorithms compared are the 3D-reprojection (3DRP) method, the iterative algebraic reconstruction technique (ART), and the maximum likelihood (ML-EM) algorithm. For the iterative methods, alternatives to the cubic voxel basis functions are also implemented. The comparisons are based on a series of figures of merit, including the point-spread function, the contrast recovery coefficient, and signal to noise ratios (SNR). The results show that the ART method implemented with the new basis functions requires only 1 cycle through the data to produce images with measured SNR values comparable to or better than the 3DRP method. The ML-EM method requires 6 to 8 iterations for the test phantom used and produced images with significantly lower SNR values. These are in agreement with previous simulation studies