Performance Comparison between Compressed Sensing and Statistical Iterative Reconstruction Algorithms

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they provide accurate physical noise modeling. The newly developed compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. In x-ray CT reconstructions, the CS algorithm can be implemented in the statistical reconstruction framework. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least square and q-GGMRF) to the CS algorithm. In assessing the image quality using these non-linear reconstructions it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. A quality factor which accounts for the noise performance and the spatial resolution was introduced to objectively evaluate the performance of the algorithm under two conditions: 1) constant undersampling factor comparing different algorithms at different dose levels and 2) varying undersampling factors and dose levels for the CS algorithm. To facilitate this comparison the original CS method was also formulated in the framework of the statistical image reconstruction algorithm. This is also a novel aspect of this work. Important conclusions of the measurements are that: for realistic anatomy over 100 projections are needed to avoid streak artifacts even with CS reconstruction, regardless of the algorithm employed it is beneficial to distribute the total dose to many views as long as each view remains quantum noise limited, and the CS method is not appropriate for low dose levels because while it can mitigate streaking artifacts the images being to exhibit a patchy behavior.