Kristine Storm Sørensen, Pia Christine Høy, Matilde Holm Kristensen, Mathias Vassard Olsen, Morten Skaarup Larsen and Mads Jozwiak Pedersen present:

Progress Report Seminar on DeepQSM

Abstract: Quantitative susceptibility mapping (QSM) extracts magnetic susceptibility from MR signal phase and has the potential to give further insight into neurodegenerative diseases.

Speakers: Matilde Holm Kristensen, Mathias Vassard Olsen, Morten Skaarup Larsen, Mads Jozwiak Pedersen

Background field removal is a fundamental step in the QSM processing pipeline with the goal to remove contributions of the magnetic field from outside the region of interest. The inverse problem relating the measured field perturbation to the underlying magnetic susceptibility distribution can then be solved to provide the final quantitative susceptibility map. Deep learning has been shown to be a powerful method for solving the field-to-source inversion problems of QSM without the use of regularization parameters during prediction by learning the forward field solution and efficiently solving the inverse problem. We developed a new algorithm to remove the background field from GRE MR phase images based on simulations of realistic field configurations and show that we can solve the problem in a very efficient way.

Speakers: Kristine Storm Sørensen, Pia Christine Høy

QSM algorithms based on deep convolutional neural networks have shown to produce artefact-free susceptibility maps. However, clinical scans often have a large variability, and it is unclear how a deep learning-based QSM algorithm is affected by discrepancies between the training data and clinical scans. We investigated the effects of different B0 orientations and noise levels of the tissue phase on the final quantitative susceptibility maps.

Hosted by Dr Steffen Bollmann

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