Fast and accurate Magnetic Resonance Image (MRI) reconstruction with NABLA-N network

Md. Zahangir Alom; Lili He; Tarek M. Taha; Vijayan K. Asari

doi:10.1117/12.2570795

20 August 2020 Fast and accurate Magnetic Resonance Image (MRI) reconstruction with NABLA-N network

Md. Zahangir Alom, Lili He, Tarek M. Taha, Vijayan K. Asari

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110F (2020) https://doi.org/10.1117/12.2570795
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

Reducing Magnetic Resonance (MR) image acquisition time can potentially help to reduce cost and make the MRI based assessment more accessible for the patients care. The GRAPPA and Compressive Sensing (CS) have been applied for image reconstruction from the data where original sampled at a rate inferior to the Nyquist-Shannon sample theory. However, most of the existing techniques are iterative and time-consuming, as a result these methods are not suitable for the real-time applications for MRI reconstruction problem. Nowadays, the Deep Learning (DL) based methods have been showing state-of-the-art performance for different image acquisition and reconstruction tasks including MRI reconstruction. The DL based solution is more robust, generic, transferable, and faster. Hence, the deep learning-based solution can be used to deploy a real-time and faster MRI reconstruction system. In this work, we applied the NABLA-N deep learning model for the MRI reconstruction in both image-domain and frequency-domain (k-space). For evaluating the model, the experiments have conducted on publicly available MRI reconstruction challenge dataset. The results demonstrate that the NABLA-N Net (∇^N-Net) model is capable to reconstruct high-quality MR images and show that kspace reconstruction model is better and more capable compared to image-space based reconstruction model.

Citation Download Citation

Md. Zahangir Alom, Lili He, Tarek M. Taha, and Vijayan K. Asari "Fast and accurate Magnetic Resonance Image (MRI) reconstruction with NABLA-N network", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110F (20 August 2020); https://doi.org/10.1117/12.2570795

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Magnetic resonance imaging

Statistical modeling

Image restoration

Data modeling

Image acquisition

Image segmentation

Magnetism

Show All Keywords

Keywords/Phrases

Search In:

Publication Years