Stochastic Variance-Reduced Gradient Based Medical Image Restoration

Abstract

Image reconstruction is an increasingly complex field in CT. Iterative Reconstruction (IR) is at present an adjunct to standard Filtered Back Projection (FBP) reconstruction, but could become a replacement for it. Because of its potential for filtering at lower radiation dosages, IR has gotten a ton of consideration in the medicinal writing and all sellers offer business arrangements. Its utilization in cardiovascular CT has been driven to some extent because of worries about radiation portion and picture quality. In this paper proposes a novel reconstruction algorithm for different medical image modalities and bring out their performance and utilization in various medical diagnostics. The performance parameters like efficiency, utility, noise characteristics, diagnostic values etc., these measured parameter values are compared with various existing reconstruction algorithm. Our novel algorithm Stochastic Variance-Reduced Gradient is mainly designed to improve the quality of an image and to bring their practical utility to medical practitioners. Various simulation studies with benchmark medical images will be carried out to highlight the utility of the algorithm in diagnostic and medical practice.