Fast MRI Reconstruction using Deep Learning Methods: A Feasibility Study

Abstract

<p>As an indispensable imaging modality, magnetic resonance imaging is a non-invasive and radiation-free procedure that could provide high soft-tissue contrast of the patients’ body. However, its time-consuming acquisition process shadows its medical application in clinic. It not only increases the risk of generating motion artifacts but also leads to high cost of MR scanning procesure. Based on both problem, we aim to reduce the MRI acquisition time by undersampling the k-space data while eliminate the aliasing artifacts caused by the undersampling process on the reconstructed images. This dissertation is a feasibility study to investigate how to accelerate the MRI acquisition process while preserving the image quality based on the deep learning methods. The study is unfolded by completing two tasks. The first task is to compare two fast MRI algorithms – the iterative reconstruction methods and deep learning methods – using the 25% Cartesian undersampling scheme in order to evaluate which one could better eliminate the aliasing artifacts on the reconstructed images. The second task aims to compare the Cartesian undersampling schemes against the golden-angle radial undersampling schemes with the undersampling ratios of one quarter, one-sixth, and one-eighth in order to investigate which schemes could better accelerate the MRI acquisition process.</p><p>As demonstrated by the comparison results of task one, the deep learning methods (DC-CNN) algorithm outperformed the iteration reconstruction (IR-TGV) algorithm for image reconstruction in terms of the image quality and computational efficiency. In task two, the comparison results indicated that the golden-angle radial undersampling schemes outperformed Cartesian undersampling schemes in terms of accelerating the MRI scanning process. </p><p>For further investigation, we still endeavor to develop a deep learning model that could better accelerate the MRI acquisition with the Cartesian undersampling schemes. In addition, such evaluation could be expanded to more anatomic sites, MRI sequences, and 3D MRI reconstructions.</p>