Low-Dose CT Using a Nonlocal and Nonlinear Principal Component Analysis for Image Restoration

Abstract

Computed tomography (CT) is a widely used medical imaging modality which provides invaluable visual representation of various conditions ranging from neurological lesions, such as haemorrhage, stroke, tumors, etc., to cardiovascular disorders like calcium deposits, pulmonary embolism, and many other pathologies. However, the ionizing radiation from the CT machine’s X-ray tube has to be kept in check, because overexposure is related to elevated risks for genetic mutation or cancer development. In this work, we attempt to reduce the radiation exposure required for high-quality CT image formation by establishing rank sparsity in principal components’ domain and developing a compressed sensing framework based on a novel nonlocal and nonlinear low-rank principal component analysis technique in image denoising, which will be subsequently incorporated as a building block for a sparse-view CT image reconstruction framework under the umbrella of convex analysis. Experiments will show that the proposed strategy provides a viable solution for low-dose CT, outperforming other well-known nonlocal image restoration models in both denoising and reconstruction tasks. In particular, the proposed method will offer 4%–10% improvement in root-mean-squared error relative to other nonlocal methods at little extra computational time.