Work

Multi-Shot Sensitivity-Encoded Diffusion MRI Using Model-Based Deep Learning (Modl-Mussels)

Year: 2019

Type: conference-paper

Abstract: We propose a model-based deep learning architecture for the correction of phase errors in multishot diffusion-weighted echo-planar MRI images. This work is a generalization of MUSSELS, which is a structured low-rank algorithm. We show that an iterative reweighted least-squares implementation of MUSSELS resembles the model-based deep learning (MoDL) framework. We propose to replace the self-learned linear filter bank in MUSSELS with a convolutional neural network, whose parameters are learned from exemplary data. The proposed algorithm reduces the computational complexity of MUSSELS by several orders of magnitude, while providing comparable image quality.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi.2019.8759514

Authors: Hemant K. Aggarwal, Merry P. Mani, Mathews Jacob

Institution: University of Iowa

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funder: National Institute of Biomedical Imaging and Bioengineering

Award: Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI