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Zero-shot arbitrary-scale super resolution in susceptibility-weighted imaging for cerebral microbleed analysis
Fengchun Liu1, Rong Zhang1, Zhongyue Lv2
1Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.
Background And Objective:
Susceptibility Weighted Imaging (SWI) plays a pivotal role in detecting cerebral microbleeds (CMBs), key biomarkers of vascular abnormalities and neurodegenerative diseases. Clinical protocols often use larger slice spacing to reduce scan time, resulting in low-resolution SWI with compromised through-plane detail. Although deep learning-based super-resolution (SR) methods show promise, they require large paired high-resolution (HR) and low-resolution (LR) datasets that are difficult to acquire in medical imaging. To address these challenges, we propose MagNeRF, a zero-shot, single-subject arbitrary-scale SR framework that learns an implicit prior from a single LR volume without external paired training data.
Methods:
MagNeRF introduces three key innovations for SWI: (1) a dilated patch-based sampling strategy to improve spatial context and local detail recovery; (2) a spherical sampling strategy to capture the radial gradient decay of magnetic susceptibility signals in SWI; and (3) adaptive loss functions (adaptive multi-scale structural similarity and adaptive mean squared error) that emphasize perceptual fidelity and structural preservation.
Results:
We rigorously evaluated MagNeRF on two SWI datasets targeting CMBs. Results demonstrate that MagNeRF outperforms state-of-the-art methods, producing reconstructed SWI volumes with high visual fidelity and preserved diagnostically relevant structures. Further validation on a T1-weighted dataset and a real-world LR T2*-weighted dataset confirms its robustness across diverse MRI contrasts. Notably, downstream CMB lesion segmentation using the reconstructed HR images achieves performance closely approaching that of the original HR data, underscoring the clinical utility of the proposed approach.
Conclusions:
MagNeRF shows significant potential in preserving clinically meaningful microbleed features in SWI. By enabling HR reconstruction from LR inputs, MagNeRF reduces patient burden, enhances diagnostic accuracy, and broadens the clinical applicability of SWI.
Insights
MagNeRF enhances low-resolution Susceptibility Weighted Imaging (SWI) for detecting cerebral microbleeds (CMBs) without needing paired data. This novel approach improves diagnostic accuracy and reduces patient burden by reconstructing high-resolution SWI from single low-resolution volumes.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neuroscience
Background:
- Susceptibility Weighted Imaging (SWI) is crucial for identifying cerebral microbleeds (CMBs), indicators of vascular issues and neurodegeneration.
- Clinical SWI protocols often sacrifice resolution for speed, compromising the detection of subtle through-plane details.
- Existing deep learning super-resolution (SR) methods require extensive paired high-resolution (HR) and low-resolution (LR) data, which is challenging to obtain in medical contexts.
Purpose of the Study:
- To introduce MagNeRF, a novel zero-shot, single-subject SR framework for arbitrary-scale reconstruction of SWI.
- To enable high-resolution SWI reconstruction from a single low-resolution volume without external paired training data.
- To improve the detection and characterization of cerebral microbleeds (CMBs) in SWI.
Main Methods:
- MagNeRF employs a dilated patch-based sampling strategy to enhance spatial context and local detail recovery.
- A spherical sampling strategy is utilized to effectively capture the radial gradient decay characteristic of SWI magnetic susceptibility signals.
- Adaptive loss functions, including adaptive multi-scale structural similarity and adaptive mean squared error, are implemented to prioritize perceptual fidelity and structural integrity.
Main Results:
- MagNeRF demonstrated superior performance compared to state-of-the-art methods on SWI datasets for CMB detection, yielding high visual fidelity and preserving diagnostically relevant structures.
- The framework's robustness was confirmed across diverse MRI contrasts, including T1-weighted and real-world LR T2*-weighted datasets.
- Downstream CMB lesion segmentation using MagNeRF-reconstructed HR images achieved performance comparable to original HR data, highlighting its clinical relevance.
Conclusions:
- MagNeRF shows significant promise for preserving clinically relevant microbleed features in SWI, offering a powerful tool for enhancing diagnostic accuracy.
- The ability to reconstruct HR SWI from LR inputs can reduce patient scanning time and burden.
- MagNeRF has the potential to broaden the clinical applicability of SWI in diagnosing and monitoring neurovascular and neurodegenerative conditions.
