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Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification.

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Τίτλος: Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification.
Συγγραφείς: Xiong Z; Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia., Gao Y; School of Computer Science and Engineering, Central South University, Changsha, China., Liu F; School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Australia., Emery D; Department of Biomedical Engineering, University of Alberta, Edmonton, Canada., Butcher K; School of Clinical Medicine, University of New South Wales, Sydney, Australia., Wilman AH; Department of Biomedical Engineering, University of Alberta, Edmonton, Canada., Sun H; School of Engineering, University of Newcastle, Newcastle, Australia.
Πηγή: Magnetic resonance in medicine [Magn Reson Med] 2026 Aug; Vol. 96 (2), pp. 596-610. Date of Electronic Publication: 2026 Mar 24.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley Country of Publication: United States NLM ID: 8505245 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2594 (Electronic) Linking ISSN: 07403194 NLM ISO Abbreviation: Magn Reson Med Subsets: MEDLINE
Imprint Name(s): Publication: 1999- : New York, NY : Wiley
Original Publication: San Diego : Academic Press
Ιατρικοί όροι (MeSH): Intracranial Hemorrhages*/diagnostic imaging , Imaging, Three-Dimensional*/methods , Diffusion Magnetic Resonance Imaging*/methods , Image Processing, Computer-Assisted*/methods, Image Interpretation, Computer-Assisted/methods ; Brain/diagnostic imaging ; Humans ; Algorithms ; Echo-Planar Imaging ; Computer Simulation ; Female ; Reproducibility of Results ; Male
Περίληψη: Purpose: To develop a generative diffusion model-based approach for robust and efficient quantitative susceptibility mapping (QSM) reconstruction in intracranial hemorrhage (ICH), applicable to both standard gradient echo (GRE) and rapid echo planar imaging (EPI) acquisitions.
Methods: QSMDiff, an unsupervised diffusion model for 3D QSM dipole inversion, was proposed. Three volumetric partitioning strategies including 2D slices, 3D patches, and 2.5D slabs were evaluated, and the memory-efficient 2.5D slab approach was adopted to balance accuracy and efficiency while maintaining anatomical fidelity. A conditional sampling mechanism ensured consistency with measured local fields, and a three-stage training data-generation strategy combining public dataset, synthetic QSM, and ICH lesions simulated from in vivo patients was implemented to overcome data scarcity.
Results: QSMDiff achieved the best overall performance in simulation studies, with SSIM of 0.97 ± 0.07, RMSE of 0.04 ± 0.03, and HFEN of 4.49 ± 0.83, demonstrating superior structural fidelity and noise suppression. For in vivo ICH patients scanned with rapid EPI, QSMDiff showed strong agreement with SWI-QSM references (R2 = 0.83), producing susceptibility estimates with minimal bias and variance. Qualitative evaluation confirmed enhanced resolution and effective artifact suppression in conditions of low SNR, limited resolution, and motion.
Conclusion: QSMDiff achieves high-quality and accurate QSM reconstruction from both standard GRE and rapid EPI scans for ICH assessment. By integrating a 2.5D training strategy with synthetic ICH augmentation, it delivers accurate and reliable susceptibility maps even from lower-quality acquisitions, offering a practical solution for fast and robust ICH assessment.
(© 2026 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)
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Grant Information: DE20101297 Australian Research Council; DP230101628 Australian Research Council; 2030157 National Health and Medical Research Council; 62301616 National Natural Science Foundation of China; 2024JJ6530 Natural Science Foundation of Hunan
Contributed Indexing: Keywords: QSMDiff; diffusion models; echo planar imaging (EPI); intracranial hemorrhage (ICH); quantitative susceptibility mapping (QSM)
Entry Date(s): Date Created: 20260324 Date Completed: 20260616 Latest Revision: 20260726
Update Code: 20260726
PubMed Central ID: PMC13269196
DOI: 10.1002/mrm.70358
PMID: 41873533
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1522-2594
DOI:10.1002/mrm.70358