Meeting Banner
Abstract #1045

Retrospective relaxometry from conventional contrasts by physics-informed deep learning: A pilot on Tumor, MS, Stroke and Epilepsy patients

Jelmer van Lune1, Stefano Mandija1, Martin B. Schilder1, Luuk Jacobs2, Jordi P. D. Kleinloog1, Matteo Maspero1, Sarah M. Jacobs3, Cornelis A. T. van den Berg1, and Alessandro Sbrizzi1
1Computational Imaging Group for MRI Therapy & Diagnostics, University Medical Center Utrecht, Utrecht, Netherlands, 2Institute of Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 3Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, Netherlands

Synopsis

Keywords: Diagnosis/Prediction, Quantitative Imaging, Neurology, AI reconstruction

Motivation: Quantitative MRI could enhance disease diagnosis, but current limited availability restricts its clinical application. Existing deep learning (DL) models for retrospective quantitative mapping lack evaluation of broader clinical populations.

Goal(s): Evaluate the potential of retrospectively generated quantitative maps by physics-informed DL in healthy controls and a mixed-clinical population.

Approach: Quantitative T1- and T2-maps were generated by a self-supervised, physics-informed DL model. The model was trained and evaluated on a heterogenous dataset, including tumor, multiple sclerosis, stroke, and epilepsy patients.

Results: Generated quantitative maps agree with literature values for normal tissue and effectively discerned various lesions, demonstrating the potential of the model’s generalization ability.

Impact: This study demonstrates the use of self-supervised, physics-informed deep learning to generate full-brain quantitative T1- and T2-maps from conventional MRI on a heterogeneous dataset of neurological patients, potentially enabling future applications on large-scale datasets to improve diagnostic tools.

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.

Keywords