Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm

Abstract

It is challenging to recover the required compressed CT (Computed Tomography, CT) image, which is got by transferred through the internet or is stored in a signal library after being compressed. We present a recovery method for compressed sensing CT images. At present, minimizing 0-norm, 1-norm and p-norm is used to recover compressed sensing signals. However, sometimes 0-norm is an NP problem, 1-norm has no solution in theory and p-norm is not a convex function. We introduce a recovery method of compressed sensing signal based on regularized smooth convex optimization. In order to avoid solving the non-convex optimization problems and no solution condition, a convex function is designed as the objective function of optimization to fit 0-norm of signal and a fast iterative shrinkage-thresholding algorithm is proposed to find solution with the convergence speed is quadratic convergence. Experimental results show that our method has a sound recovery effect and is well suitable for processing big data of compressed CT images.

CT Imaging Characteristics and Influence Factors of Renal Dialysis-Associated Peritoneal Injury

“…DWI is a magnetic resonance functional imaging technique based on the diffusion motion of water molecules in the living body, and with the application of MRI ultrafast imaging sequences, especially planar echo imaging sequences, DWI has been gradually applied to abdominal imaging diagnosis. e kidney is located in the retroperitoneal space and is relatively unaffected by respiratory motion, and the same satisfactory image quality can be obtained with free breathing as with breath-holding [22][23][24]. DWI signal intensity mainly reflects the tissue structure, nucleoplasmic ratio, distribution of water molecules inside and outside the cells, cell density, and so forth.…”