A Phantom Study of Regularized Image Reconstruction in PET

Image quality was measured for varied tuning parameters of four penalized likelihood potential functions with reconstructed PET data of multiple hot spheres in a warm background. Statistical image reconstruction with potential functions that penalize differences in neighboring image voxels can produce a smoother image, but large differences that occur at physical boundaries should not be penalized and allowed to form. Over-smoothing PET images with small lesions is especially problematic because it can completely smooth a lesion's intensities into the background. Fourteen 1.0-cm spheres with a 6: 1 radioactivity concentration relative to the warm background were positioned throughout a 40-cm long phantom with a 36x21-cm oval cross section. By varying the tuning parameters, multiple image sets were reconstructed with modified block sequential regularized expectation maximization statistical reconstruction algorithm using 4 potential functions: quadratic, generalized Gaussian, logCosh, and Huber. Regions of interest were positioned on the images, and the image quality was measured as contrast recovery, background variability, and signal-to-noise ratio across the ROIs. This phantom study was used to further narrow the choice of potential functions and parameter values to either improve the image quality of small lesions or avoid deteriorating them at the cost of optimizing reconstruction parameters for other image features. Neither the quadratic or logCosh potentials performed well for small lesion SNR because they either over-smoothed the lesions or under-smoothed the background, respectively. Varying the parameter values for the Huber potential had a proportional effect on the background variability and the sphere signal such that SNR was relatively fixed. Generalized Gaussian simultaneously decreased background variability and increased small lesion contrast recovery that produced SNRs as much as two-times higher than the other potential functions.