BRIEF RESEARCH REPORT article
Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1544376
This article is part of the Research TopicNeuroimaging Innovations for Encephalitis, Neuroinfectious Diseases, and NeuroinflammationView all 11 articles
A pilot study assessing the clinical utility of deep learning-reconstructed 3D-Echo-Planar-Imaging-based Quantitative Susceptibility Mapping in Multiple Sclerosis
Provisionally accepted
Dimitrios G. Gkotsoulias1,2,3*
Matthias Weigel1,2,3,4
Alessandro Cagol1,2,3,5
Nina de Oliveira Soares Siebenborn1
Josef Pfeuffer6
Cristina Granziera1,2,3- 1Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- 2Department of Neurology, University Hospital of Basel, Basel, Switzerland
- 3Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital and University of Basel, Basel, Switzerland
- 4Division of Radiological Physics, Department of Radiology and Nuclear Medicine, University Hospital of Basel, Basel, Switzerland
- 5Department of Health Sciences, University of Genova, 16132, Genova, Italy
- 6Application Development, Siemens Healthineers AG, Erlangen, Germany
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Background:Quantitative Susceptibility Mapping(QSM) has emerged as a promising paraclinical tool in Multiple Sclerosis (MS). This retrospective pilot study aims to evaluate whether a recently proposed deep learning-assisted, k-space-operating reconstruction, denoising and super-resolution technique(DLR) applied on 3D-Echo-Planar-Imaging(3DEPI) protocols, has the potential to improve the quality and clinical utility of QSM in Multiple Sclerosis, at 3T. Secondarily, we assess whether applying DLR versus a Conventional Reconstruction(CR) can improve the quality of QSM based on noise-susceptible, fast 3DEPI protocols.Methods:3T MRI 3DEPI-data were acquired on 7 MS patients and offline-reconstructed using CR and DLR. A sample size of 433 lesions was identified, based on FLAIR segmentation. Two experts, independently and method-blinded, rated lesion-wise the CR- and DLR-3DEPI-derived QSM, assessing the confidence in identifying paramagnetic rim lesions(PRLs), central vein sign(CVS), QSM hyper/isointense lesions and image quality. Gradient-Recalled-Echo(GRE), 2- and 1-average 3DEPI (Acquisition time:7:02/3:44/1:56min, respectively) from a healthy individual were offline-reconstructed using CR and DLR. Derived QSM maps were compared visually and quantitatively. Results:DLR-3DEPI-based QSM was rated significantly higher for the confidence in identification of the MS-specific biomarkers(hyper/isointense lesions:P<0.001, CVS: P=0.01) and overall image quality(P<0.001), compared to CR-3DEPI-based. Inter-method agreement was high for both raters(Cohen’s κ=0.98/0.92), suggesting that DLR improves the quality without changing the rater’s perception of the individual QSM-related clinical findings. Additionally, QSM derived from fast DLR-3DEPI with a 4-fold acquisition-time reduction compared to GRE, exhibited excellent visual and quantitative consistency with GRE-based QSM.Conclusion:Our results constitute a first demonstration of the enhanced quality and clinical utility of the DLR-3DEPI-based QSM in MS.
Keywords: Quantitative susceptibility mapping, GRE: Gradient Recalled Echo, 3DEPI: 3-dimensionalsegmented-Echo Planar Imaging, MS: Multiple Sclerosis, χ: Susceptibility, CVS: central vein sign, PRL: paramagnetic rim lesion, AI: artificial intelligence
Received: 12 Dec 2024; Accepted: 12 Jun 2025.
Copyright: © 2025 Gkotsoulias, Weigel, Cagol, de Oliveira Soares Siebenborn, Pfeuffer and Granziera. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dimitrios G. Gkotsoulias, Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
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