Impact of noise level and edge sharpness of a prior image on the performance of Prior Image Constrained Compressed Sensing (PICCS)

Abstract

Dose reduction using prior image constrained compressed sensing (DR-PICCS) is a method of CT reconstruction which utilizes prior image information in a compressed sensing framework to significantly reduce the noise in images acquired at low dose. The purpose of this study was to investigate the impact of edge sharpness and noise level in the prior image data on the resulting DR-PICCS images. Projection data from a 100 rnA CT myocardial perfusion scan of a swine was combined with numerically simulated projections of vessels of varying geometry (diameter = 4, 3, 2 mm) and contrast levels (600, 400, 200 HU enhancement). Bilateral and mean filters were applied to generate prior images, which were then used with the DR-PICCS algorithm. Vessel diameter, effective blurring kernel, and vessel intensity were compared among prior images as well as among the corresponding PICCS images. Although the filters produced prior images with significantly different spatial resolution characteristics at similar noise levels, these differences were mitigated in DR-PICCS images and the DR-PICCS had improved fidelity in comparison to the priors.