Compressed sensing MRI using masked DCT and DFT measurements

TL;DR

This paper enhances the TwIST algorithm for MRI reconstruction by testing various masks and transformation domains, demonstrating improved performance in compressive sensing MRI with experimental validation.

Contribution

It introduces modifications to TwIST for better MRI image reconstruction using different masks and transforms, with comprehensive experimental analysis.

Findings

Different masks and transforms affect reconstruction quality

2D DCT and DFT coefficients yield comparable results

Mask shape influences measurement efficiency

Abstract

This paper presents modification of the TwIST algorithm for Compressive Sensing MRI images reconstruction. Compressive Sensing is new approach in signal processing whose basic idea is recovering signal form small set of available samples. The application of the Compressive Sensing in biomedical imaging has found great importance. It allows significant lowering of the acquisition time, and therefore, save the patient from the negative impact of the MR apparatus. TwIST is commonly used algorithm for 2D signals reconstruction using Compressive Sensing principle. It is based on the Total Variation minimization. Standard version of the TwIST uses masked 2D Discrete Fourier Transform coefficients as Compressive Sensing measurements. In this paper, different masks and different transformation domains for coefficients selection are tested. Certain percent of the measurements is used from the mask, as well as small number of coefficients outside the mask. Comparative analysis using 2D DFT and 2D DCT coefficients, with different mask shapes is performed. The theory is proved with experimental results.