• Prof. Jyoti Ishwar Nandalwar

This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and effective imaging. Under sampling in k-space violates Nyquist Sampling and creates artifacts in the image domain. In the proposed method, we consider the reconstruction problem as a de-aliasing problem in complex spatial domain. To test the proposed method, from fully sampled k-space data under sampling in k-space was performed in the phase-encode direction based on a probability density function which ensures maximum rate of sampling in low frequency regions. For the deep convolutional neural network (CNN) we chose the U-net architecture.

Deep learning, magnetic resonance imaging, prior knowledge, convolutional neural network

"ACCELERATING MAGNETIC RESONANCE IMAGING VIA DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 1, page no.d49-d56, January-2022, Available :http://www.jetir.org/papers/JETIR2201307.pdf

"ACCELERATING MAGNETIC RESONANCE IMAGING VIA DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 1, page no. ppd49-d56, January-2022, Available at : http://www.jetir.org/papers/JETIR2201307.pdf