Novel FBP based sparse-view CT reconstruction scheme using self-shaping spatial filter based morphological operations and scaled reprojections

Abstract

Abstract Computed tomography (CT) has been a major contributor in revolutionizing and commercializing the medical imaging industry. However, most of the commonly used CT reconstruction algorithms need sufficiently dense angular sampling views or a substantial dose of hazardous X-ray radiations. Reducing the radiation dose causes degradation in the quality of the reconstructed image, due to additional artifacts. The paper presents a novel algorithm for efficient CT reconstruction from under-sampled projections; which leads to radiation dose reduction with quality image reconstruction. The Sparse-View projection data is enhanced using a series of post-processing algorithms and computer based reprojection. The process involves enhancement through self-shaping/amoeba based morphological spatial filtering. The use of self-shaping spatial filter kernel in the area of under-sampled CT reconstruction is a novel contribution. The scheme is supported by computer simulations using fan-beam projections of clinically reconstructed and simulated head CT phantoms. The proposed scheme is compared with classical reconstruction techniques for reconstruction image quality, accuracy, speed, and robustness in the presence of noise. Promising results indicate the efficacy of proposed scheme. An efficient scheme for image enhancement of Sparse-View CT is presented. The results demonstrate that the proposed scheme is visually and statistically better than classical CT reconstruction techniques, as evaluated using various image quality matrices. The presented scheme is more robust to noise in CT projections and effective for enhancing few-views reconstruction.