Share Email Print

Proceedings Paper

Non-learning based deep parallel MRI reconstruction (NLDpMRI)
Author(s): Ali Pour Yazdanpanah; Onur Afacan; Simon K. Warfield
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand and scan time directly depends on the number of acquired k-space samples. Recently, the deep learning-based MRI reconstruction techniques were suggested to accelerate MR image acquisition. The most common issues in any deep learning-based MRI reconstruction approaches are generalizability and transferability. For different MRI scanner configurations using these approaches, the network must be trained from scratch every time with new training dataset, acquired under new configurations, to be able to provide good reconstruction performance. Here, we propose a new generalized parallel imaging method based on deep neural networks called NLDpMRI to reduce any structured aliasing ambiguities related to the different k-space undersampling patterns for accelerated data acquisition. Two loss functions including non-regularized and regularized are proposed for parallel MRI reconstruction using deep network optimization and we reconstruct MR images by optimizing the proposed loss functions over the network parameters. Unlike any deep learning-based MRI reconstruction approaches, our method doesn’t include any training step that the network learns from a large number of training samples and it only needs the single undersampled multi-coil k-space data for reconstruction. Also, the proposed method can handle k-space data with different undersampling patterns, and the different number of coils. Experimental results show that the proposed method outperforms the current state-of-the-art GRAPPA method and the deep learning-based variational network method.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094904 (15 March 2019); doi: 10.1117/12.2511653
Show Author Affiliations
Ali Pour Yazdanpanah, Harvard Medical School (United States)
Boston Children's Hospital (United States)
Onur Afacan, Harvard Medical School (United States)
Boston Children's Hospital (United States)
Simon K. Warfield, Harvard Medical School (United States)
Boston Children's Hospital (United States)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?