Investigation of different Compressed Sensing approaches for respiratory gating in small animal CT

Respiratory gating is necessary in cardio-thoracic small-animal imaging because of the physiological motions that are present during scanning. When applying a low-dose protocol, fewer than 180 noisy projections may be left for the reconstruction of each respiratory phase, leading to streak artifacts. The Prior Image Constrained Compressed Sensing (PICCS) algorithm enables accurate reconstruction of highly undersampled data when a prior image is available. We evaluate three CS algorithms based on the Split-Bregman approach, with different transformations of the prior penalty function: Gradient (TV-PICCS), L1-norm (L1-PICCS), and Wavelet Transform (WT-PICCS), on low-dose data acquired on a micro-CT scanner. All CS methods performed very similarly in terms of noise and resolution, greatly improving filtered back-projection (79 % noise reduction) and eliminating streaks. Wavelet domain was found to be sparser and to show a more natural texture than the commonly used gradient domain.