Enhancing and accelerating brain MRI through deep learning reconstruction using prior subject-specific imaging.
Authors
Affiliations (7)
Affiliations (7)
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada. Electronic address: [email protected].
- Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, AB, Canada; Department of Radiology, University Hospital Basel, Basel, Switzerland.
- Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, AB, Canada; Department of Neurology, University Hospital Basel, Basel, Switzerland.
- Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada.
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada; Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
Abstract
Magnetic resonance imaging (MRI) is a crucial medical imaging modality. However, long acquisition times remain a significant challenge, leading to increased costs, and reduced patient comfort. Recent studies have shown the potential of using deep learning models that incorporate information from prior subject-specific MRI scans to improve reconstruction quality of present scans. Integrating this prior information requires registration of the previous scan to the current image reconstruction, which can be time-consuming. We propose a novel deep-learning-based MRI reconstruction framework which consists of an initial reconstruction network, a deep registration model, and a transformer-based enhancement network. We validated our method on a longitudinal dataset of T1-weighted MRI scans with 2,808 images from 18 subjects at four acceleration factors (R5, R10, R15, R20). Quantitative metrics confirmed our approach's superiority over existing methods (p < 0.05, Wilcoxon signed-rank test). Furthermore, we analyzed the impact of our MRI reconstruction method on the downstream task of brain segmentation and observed improved accuracy and volumetric agreement with reference segmentations. Our approach also achieved a substantial reduction in total reconstruction time compared to methods that use traditional registration algorithms, making it more suitable for real-time clinical applications. The code associated with this work is publicly available at https://github.com/amirshamaei/longitudinal-mri-deep-recon.