Publications
Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleSveinsson B, Vangel M, Rowe OE, et al., 2026,
Case Series: Feasibility of Longitudinal Assessment of the Sciatic Nerve in CMT1A Using High-Resolution 7T MRI.
, Muscle Nerve, Vol: 73, Pages: 1155-1159INTRODUCTION/AIMS: There is limited data on the sensitivity and responsiveness of high-resolution imaging techniques in the longitudinal assessment of hereditary neuropathies. In this study, our aims were to investigate the ability of ultra-high field magnetic resonance imaging to detect longitudinal changes in the peripheral nerves of Charcot-Marie-Tooth (CMT) 1A patients, and to evaluate the potential benefits of doing so at the nerve fascicle level. METHODS: We performed magnetic resonance imaging (MRI) to simultaneously obtain high-resolution anatomical and quantitative data at ultra-high 7 Tesla field strength in peripheral nerves of four patients with CMT1A disease at baseline and follow up. We compared the resulting measurements of T2 in sciatic, tibial, and fibular nerves within individual fascicles of the three nerve regions. RESULTS: Analyzing individual fascicle distributions, we demonstrated a significantly elevated T2 in the fibular nerve over the course of the study, with a mean increase of 3.55 ms (p = 0.01). Changes in the sciatic nerve were marginally significant (mean increase 1.42 ms, p = 0.05), and tibial nerve changes were not significant (mean increase 1.31 ms, p = 0.18). Combining fascicles across subjects showed significant changes in all three nerves over time. DISCUSSION: Our results indicate that longitudinal MRI assessment of individual nerve fascicles may serve as a quantitative biomarker of disease progression in patients with hereditary neuropathies. Further, our study demonstrates that the data provided by fascicle-level analysis may provide better analytical abilities than whole-nerve imaging.
-
Journal articleZhou T, Li M, Ruan S, et al., 2026,
A reliable framework for brain tumor segmentation via multi-modal fusion and uncertainty modeling
, Information Fusion, Vol: 129, ISSN: 1566-2535Accurate brain tumor segmentation from MRI scans is critical for effective diagnosis and treatment planning. Recent advances in deep learning have significantly improved brain tumor segmentation performance. However, these models still face challenges in clinical adoption due to their inherent uncertainties and potential for errors. In this paper, we propose a novel MR brain tumor segmentation approach that integrates multi-modal data fusion and uncertainty quantification to improve the accuracy and reliability of brain tumor segmentation. Recognizing that each MR modality contributes unique insights into the tumor’s characteristics, we propose a novel modality-aware guidance by explicitly categorizing the modalities into ”teacher” (FLAIR and T1c) and ”student” (T2 and T1) groups. Since the teacher modalities are the most informative modalities for identifying brain tumors, we propose a multi-modal teacher-student fusion strategy. This strategy leverages the teacher modalities to guide the student modalities in both spatial and channel feature representation aspects. To address prediction reliability, we employ Monte Carlo dropout during training to generate multiple uncertainty estimates. Additionally, we develop a novel uncertainty-aware loss function that optimizes segmentation accuracy while quantifying the uncertainty in predictions. Experimental results conducted on three BraTS datasets demonstrate the effectiveness of the proposed components and the superior performance compared to the state-of-the-art methods, highlighting their potential for clinical application.
-
Journal articleHasan MK, Yang G, Yap CH, 2026,
An efficient, scalable, and adaptable plug-and-play temporal attention module for motion-guided cardiac segmentation with sparse temporal labels.
, Med Image Anal, Vol: 110Cardiac anatomy segmentation is essential for clinical assessment of cardiac function and disease diagnosis to inform treatment and intervention. Deep learning (DL) has improved cardiac anatomy segmentation accuracy, especially when information on cardiac motion dynamics is integrated into the networks. Several methods for incorporating motion information have been proposed; however, existing methods are not yet optimal: adding the time dimension to input data causes high computational costs, and incorporating registration into the segmentation network remains computationally costly and can be affected by errors of registration, especially with non-DL registration. While attention-based motion modeling is promising, suboptimal design constrains its capacity to learn the complex and coherent temporal interactions inherent in cardiac image sequences. Here, we propose a novel approach to incorporating motion information in the DL segmentation networks: a computationally efficient yet robust Temporal Attention Module (TAM), modeled as a small, multi-headed, cross-temporal attention module, which can be plug-and-play inserted into a broad range of segmentation networks (CNN, transformer, or hybrid) without a drastic architecture modification. Extensive experiments on multiple cardiac imaging datasets, such as 2D echocardiography (CAMUS and EchoNet-Dynamic), 3D echocardiography (MITEA), and 3D cardiac MRI (ACDC), confirm that TAM consistently improves segmentation performance across datasets when added to a range of networks, including UNet, FCN8s, UNetR, SwinUNetR, and the recent I2UNet and DT-VNet. Integrating TAM into SAM yields a temporal SAM that reduces Hausdorff distance (HD) from 3.99 mm to 3.51 mm on the CAMUS dataset, while integrating TAM into a pre-trained MedSAM reduces HD from 3.04 to 2.06 pixels after fine-tuning on the EchoNet-Dynamic dataset. On the ACDC 3D dataset, our TAM-UNet and TAM-DT-VNet achieve substantial reductions in HD, from 7.97 mm to 4.23 mm
-
Journal articleWang F, Wang Z, Li Y, et al., 2026,
Toward Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge.
, IEEE Trans Med Imaging, Vol: 45, Pages: 1872-1887Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the clinical reference standard for diagnosing cardiovascular disease. However, its adoption is hindered by long scan times, complex contrasts, and inconsistent quality. While deep learning methods perform well on specific CMR imaging sequences, they often fail to generalize across modalities and sampling schemes. The lack of benchmarks for high-quality, fast CMR image reconstruction further limits technology comparison and adoption. The CMRxRecon2024 challenge, attracting over 200 teams from 18 countries, addressed these issues with two tasks: generalization to unseen modalities and robustness to diverse undersampling patterns. We introduced the largest public multi-modality CMR raw dataset, an open benchmarking platform, and shared code. Analysis of the best-performing solutions revealed that prompt-based adaptation and enhanced physics-driven consistency enabled strong cross-scenario performance. These findings establish principles for generalizable reconstruction models and advance clinically translatable AI in cardiovascular imaging.
-
Journal articleDong Y, Xiao X, Zhuang X-X, et al., 2026,
DeepDrugDiscovery identifies blood–brain barrier permeable autophagy enhancers for Alzheimer’s disease
, Nature Biomedical Engineering, ISSN: 2157-846X -
Journal articleGao Y, Marshall D, Xing X, et al., 2026,
Anatomy-guided radiology report generation with pathology-aware regional prompts
, IEEE Open Journal of Engineering in Medicine and Biology, Vol: 7, Pages: 165-171, ISSN: 2644-1276Goal: Radiology report generation holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy remains challenging, as radiographs often feature intricate structures and subtle pathologies. Methods: To address these challenges, this work introduces an innovative approach that explicitly integrates anatomical and pathological information into report decoding by leveraging pathology-aware regional prompts. Specifically, we develop an anatomical region detector that extracts structured visual features from distinct anatomical areas, coupled with a novel multi-label pathology detector that identifies global abnormalities. Results: Our model demonstrates superior report generation performance in natural language generation and clinical efficacy, surpassing previous state-of-the-art methods. It achieved scores of 0.394 in BLEU-1, 0.302 in ROUGE-L, and 0.470 in F1, reflecting substantial improvements in both linguistic fluency and medical accuracy. Formal expert evaluations further affirmed the model's potential to elevate radiology practice. Conclusion: By integrating anatomical and pathological insights to emulate radiologists' workflow, our model achieves superior accuracy and clinical coherence of radiology reporting. It offers remarkable promise to support clinical decision-making and transform patient management.
-
Journal articleYeung M, Watts T, Tan SYW, et al., 2026,
Stain consistency learning: handling stain variation for automatic digital pathology segmentation
, IEEE Open Journal of Engineering in Medicine and Biology, ISSN: 2644-1276Stain variation poses a major challenge for automated digital pathology. Numerous techniques address this issue, yet show limited success, especially outside H&E stains and classification tasks. We propose Stain Consistency Learning (SCL), combining stain-specific augmentation and a novel consistency loss to learn stain-invariant features. We conduct the first large-scale evaluation of ten methods on Masson's trichrome and H&E datasets for segmentation. Our results demonstrate that traditional stain normalization offers little benefit, while stain augmentation and adversarial learning significantly improve performance. SCL consistently outperforms all other methods. Code is available at:https://github.com/mlyg/stain_consistency_learning.
-
Journal articleWen K, Ferreira PF, Di Biase Oemick A, et al., 2026,
Evaluation of Third-Order Motion-Compensated Cardiac Diffusion Tensor Imaging Across Cardiac Phases Using an Ultra-High-Performance Clinical Scanner.
, Magn Reson MedPURPOSE: To evaluate a third-order motion-compensated spin echo (M3-MCSE) sequence at multiple cardiac phases on a clinical 3 T MRI scanner with ultra-high performance (UHP) gradients (200 mT/m), compared with stimulated echo acquisition mode (STEAM) and second-order MCSE (M2-MCSE) for cardiac diffusion tensor imaging (cDTI). METHODS: Twenty healthy subjects underwent mid-ventricular short-axis cDTI at peak systole and diastasis using STEAM, M2-MCSE, and M3-MCSE. cDTI metrics and image quality were compared. In five additional healthy subjects, diffusion-weighted images were obtained at multiple trigger delays distributed over diastasis to assess motion-induced signal loss. RESULTS: Compared to M2-MCSE, M3-MCSE yielded higher systolic helix angle map scores ( p = 0.007 $$ p=0.007 $$ ) but lower diastolic scores ( p = 0.001 $$ p=0.001 $$ ), with no significant difference in mean diffusivity, fractional anisotropy, helix angle transmurality or sheetlet angle in systole/diastole. STEAM-derived apparent diffusion coefficients (ADC) were consistent across diastasis, while ADC for MCSE sequences increased at sub-optimal trigger delays. CONCLUSION: UHP gradients enabled in vivo evaluation of M3-MCSE, showing superior systolic cDTI but reduced diastolic performance versus M2-MCSE due to reduced signal-to-noise ratio and a longer motion-sensitive window. Future work may consider numerically optimized gradient designs to enhance MCSE robustness throughout the cardiac cycle.
-
Journal articleLuo Y, Ferreira PF, Wen K, et al., 2026,
Optimized Reduced Field of View and Fat Suppression Methods for Interleaved Multislice In Vivo Cardiac Diffusion Tensor Imaging.
, Magn Reson MedPURPOSE: Slice interleaving, a limited phase encode (PE) field of view (FOV), and effective fat suppression are vital for efficient cardiac diffusion tensor imaging (cDTI) with minimal artifacts. This study aimed to optimize reduced FOV and fat suppression methods for interleaved multislice cDTI to improve signal-to-noise ratio (SNR) and minimize artifacts. METHODS: Two-slice motion compensated spin echo datasets from 20 healthy volunteers were acquired. Four reduced PE FOV sequences were evaluated: 2DRF pulse; applying either 180 ° $$ {180}^{{}^{\circ}} $$ or 90 ° $$ {90}^{{}^{\circ}} $$ pulses in PE direction; and the proposed flip-back sequence with a nonselective 180 ° $$ {180}^{{}^{\circ}} $$ pulse after readout to restore inverted magnetization. Four fat suppression techniques were implemented: no fat suppression (standard); fat saturation; binomial water excitation and spectral attenuated inversion recovery (SPAIR). RESULTS: The proposed flip-back sequence with SPAIR achieved the highest median SNR, and its SNR values are significantly higher ( p < 0.01 $$ p<0.01 $$ ) than 2DRF with SPAIR as current state-of-the-art. SPAIR and water excitation demonstrated comparable performance when combined with the flip-back sequence, and both yielded superior image quality than with no suppression or fat saturation. SPAIR showed robust fat suppression across most subjects, whilst water excitation exhibited advantages in some subjects with a high body mass index. CONCLUSION: The proposed flip-back sequence with SPAIR enables efficient interleaved multislice imaging with reduced PE FOV and effective fat suppression, facilitating clinical translation of in vivo cDTI.
-
Journal articleZhang Z, Jing P, Wang Z, et al., 2026,
Cyclic Self-Supervised Diffusion for Ultra Low-field to High-field MRI Synthesis.
, IEEE Trans Med Imaging, Vol: PPSynthesizing high-quality images from low-field MRI holds significant potential. Low-field MRI is cheaper, more accessible, and safer, but suffers from low resolution and poor signal-to-noise ratio. This synthesis process can reduce reliance on costly acquisitions and expand data availability. However, synthesizing high-field MRI still suffers from a clinical fidelity gap. There is a need to preserve anatomical fidelity, enhance fine-grained structural details, and bridge domain gaps in image contrast. To address these issues, we propose a cyclic self-supervised diffusion (CSS-Diff) framework for high-field MRI synthesis from real low-field MRI data. Our core idea is to reformulate diffusion-based synthesis under a cycle-consistent constraint. It enforces anatomical preservation throughout the generative process rather than just relying on paired pixel-level supervision. The CSS-Diff framework further incorporates two novel processes. The slice-wise gap perception network aligns inter-slice inconsistencies via contrastive learning. The local structure correction network enhances local feature restoration through self-reconstruction of masked and perturbed patches. Extensive experiments on cross-field synthesis tasks demonstrate the effectiveness of our method, achieving state-of-the-art performance (e.g., 31.80 ± 2.70 dB in PSNR, 0.943± 0.102 in SSIM, and 0.0864 ± 0.0689 in LPIPS). Beyond pixel-wise fidelity, our method also preserves fine-grained anatomical structures compared with the original low-field MRI (e.g., left cerebral white matter error drops from 12.1% to 2.1%, cortex from 4.2% to 3.7%). To conclude, our CSS-Diff can synthesize images that are both quantitatively reliable and anatomically consistent. The code is available at: https://github.com/ayanglab/CSS-Diff.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.