Abstract #4931

SHARQnet - Sophisticated Harmonic Artifact Reduction in Quantitative Susceptibility Mapping using a Deep Convolutional Neural Network

Mathias Vassard Olsen¹, Morten Skaarup Larsen¹, Matilde Holm Kristensen¹, Mads Jozwiak Pedersen¹, Lasse Riis Østergaard¹, Kieran O'Brien^2,3, Christian Langkammer⁴, Markus Barth², and Steffen Bollmann²

¹Department of Health Science and Technology, Aalborg University, Aalborg East, Denmark, ²Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, ³Siemens Healthcare Pty Ltd, Brisbane, Australia, ⁴Department of Neurology, Medical University of Graz, Graz, Austria

We propose a fully convolutional neural network for background field removal in MR phase images for Quantitative Susceptibility Mapping. Our proposed method, SHARQnet, learns to solve the background field problem from theoretical simulations of background field distributions, and the results are compared to current state-of-the-art methods like SHARP, V-SHARP, and RESHARP.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.