Medizinische Universität Graz - Research portal

Go directly to the nagivation.
Go directly to the content.

Navigation/Search

Selected Publication:

SHR Neuro Cancer Cardio Lipid Metab Microb

Cognolato, F; O'Brien, K; Jin, J; Robinson, S; Laun, FB; Barth, M; Bollmann, S.
NeXtQSM-A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data
MED IMAGE ANAL. 2023; 84: 102700 Doi: 10.1016/j.media.2022.102700
Web of Science PubMed FullText FullText_MUG

Co-authors Med Uni Graz: Robinson Simon Daniel
Altmetrics:
Dimensions Citations:
Plum Analytics:
Scite (citation analytics):
Abstract:: Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem in consecutive steps resulting in the propagation of errors. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous deep learning methods. NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast.
Find related publications in this database (Keywords): Magnetic susceptibility; Electromagnetic tissue properties Simulated training data; Simulated training data; Magnetic susceptibility Data-consistent deep learning