Total Variant Based Average Sparsity Model With Reweighted Analysis for Compressive Sensing of Computed Tomography

Abstract

Computed tomography (CT) in medical is an imaging procedure employed to generate detailed images of bones, soft tissue, internal organs, and blood vessels. However, prolonged acquisition time is yet a bottleneck that can lead to patient discomfort in addition to the cost constrain and exposure to X-rays used by CT. In medical imaging technologies and implementations, effective sampling and transmission techniques are some of the main areas of study to overcome such problems. To fulfill this requirement, the compressive sensing (CS) technique was introduced demonstrating that such compression is possible and can be accomplished throughout the process of data restoration; and that the uncompressed frames can be recovered employing a scalable approach of computational optimization. Sparsity averaging reweighted analysis (SARA) was proposed in compressed imaging, exploiting multi-basis sparsity with averaging approach and basis pursuit denoise (BPDN) with high signal to noise (SNR) results. In SARA, the processing time is not considered due to the high processing time because of iteration in the reweighted process and it is not feasible for the medical image that needs fast processing with high SNR result. To fulfill this gap, this paper proposes total variation based average sparsity model with reweighted analysis for CT imaging. The SNR, structural similarity index (SSIM), and processing time are used as performance metrics for the comparison of the proposed and existing techniques. From detailed experimental results, the proposed technique outperforms the existing CS techniques and is considered as a feasible solution for compressed sensing (CS) based CT images compression with faster delay process and better visual quality for medical images.