Development of Machine Learning Based Algorithm for Computational Multidimensional Correlation Magnetic Resonance Imaging

Abstract

The possibility of non-invasively performing quantitative measurements of the physical properties of living tissue such as the diffusion coefficient and the relaxation times T₁ and T₂ during magnetic resonance imaging (MRI) require length acquisition time. To guarantee a sufficient signal-to-noise ratio (SNR) the image resolution is often sacrificed. The available method presently in use requires repeated acquisitions and averaging them, which is time-consuming. This model offers a computational method with the aid of a machine learning approach. The python approach obtained the result in 3 seconds for  and 2 seconds for  respectively.