RT Journal Article
SR Electronic
T1 SHARQnet - Sophisticated Harmonic Artifact Reduction in Quantitative Susceptibility Mapping using a Deep Convolutional Neural Network
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 522151
DO 10.1101/522151
A1 Steffen Bollmann
A1 Matilde Holm Kristensen
A1 Morten Skaarup Larsen
A1 Mathias Vassard Olsen
A1 Mads Jozwiak Pedersen
A1 Lasse Riis Østergaard
A1 Kieran O’Brien
A1 Christian Langkammer
A1 Amir Fazlollahi
A1 Markus Barth
YR 2019
UL http://biorxiv.org/content/early/2019/01/17/522151.abstract
AB Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson’s disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and lead to suboptimal or over-regularized solutions requiring a careful choice of parameters that make a clinical application of QSM challenging. We have previously demonstrated that a deep convolutional neural network can invert the magnetic dipole kernel with a very efficient feed forward multiplication not requiring iterative optimization or the choice of regularization parameters. In this work, we extended this approach to remove background fields in QSM. The prototype method, called SHARQnet, was trained on simulated background fields and tested on 3T and 7T brain datasets. We show that SHARQnet outperforms current background field removal procedures and generalizes to a wide range of input data without requiring any parameter adjustments. In summary, we demonstrate that the solution of ill-posed problems in QSM can be achieved by learning the underlying physics causing the artifacts and removing them in an efficient and reliable manner and thereby will help to bring QSM towards clinical applications.