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Journal articleLuo Y, Sesia D, Wang F, et al., 2026,
Explicit differentiable slicing and global deformation for cardiac mesh reconstruction.
, Med Image Anal, Vol: 111Three-dimensional (3D) mesh reconstruction of the cardiac anatomy from medical images is useful for shape and motion measurements and biophysics simulations. However, 3D medical images are often acquired as 2D slices that are sparsely sampled (e.g., large slice spacing) and noisy, and 3D mesh reconstruction on such data is a challenging task. Traditional voxel-based approaches utilize non-differentiable pre- and post-processing that compromises fidelity to images, while mesh-level deep learning approaches require large 3D mesh annotations that are difficult to obtain. Differentiable cross-domain supervision from 2D images to 3D meshes is therefore crucial for enabling end-to-end optimization in medical imaging. While there have been attempts to approximate the voxelization and slicing of meshes that are being optimized, there has not yet been a method for directly using 2D slices to supervise 3D mesh reconstruction in a differentiable manner. Here, we propose a novel explicit differentiable voxelization and slicing (DVS) algorithm allowing gradient backpropagation to a 3D mesh from its slices, which facilitates refined mesh optimization directly supervised by the losses defined on 2D images. Further, we propose an innovative framework for extracting patient-specific left ventricle (LV) meshes from medical images by coupling DVS with a graph harmonic deformation (GHD) mesh morphing descriptor of cardiac shape that naturally preserves mesh quality and smoothness during optimization. The proposed framework achieves state-of-the-art performance in cardiac mesh reconstruction tasks from densely sampled (CT) as well as sparsely sampled (MRI stack with few slices) images, outperforming alternatives, including Marching Cubes, statistical shape models, algorithms with vertex-based mesh morphing algorithms and alternative methods for image-supervision of mesh reconstruction. Experimental results demonstrate that our method achieves an overall Dice score of 90% during a sparse
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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.
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Journal articleTänzer M, Lim EJ, Qiu HH, et al., 2026,
Simultaneous multi-slice Cardiac Diffusion Tensor Imaging with variable CAIPIRINHA shifts and artefact-aware AI.
, Med Image Anal, Vol: 112Cardiac Diffusion Tensor Imaging (cDTI) provides unique insights into myocardial microstructure in-vivo but requires averaging multiple repetitions for adequate signal quality, leading to prohibitively long acquisition times. Standard acceleration strategies, such as reducing repetitions and employing simultaneous multi-slice (SMS) imaging, are limited by low signal-to-noise ratio (SNR) and inter-slice leakage artefacts, respectively. We introduce ORCAS, a unified framework that synergistically combines a novel variable CAIPIRINHA acquisition with an artefact-aware AI reconstruction to overcome these challenges. The variable CAIPIRINHA scheme decoheres SMS artefacts across repetitions, while our dual-domain deep learning model simultaneously suppresses these artefacts and combats the low SNR from fewer repetitions. The model is guided by patient-specific single-band auxiliary data to preserve anatomical fidelity. Validated on ex-vivo hearts with and without anomalies, ORCAS achieves an over 18-fold acceleration by combining these strategies, reducing a whole-heart scan from over two hours to under 7 min. This is accomplished while reducing errors in key biomarkers, such as Fractional Anisotropy, by up to 64%. The framework preserves essential microstructural properties and the delineation of abnormalities, representing a significant step towards the clinical translation of whole-heart cDTI.
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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.
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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.
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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
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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 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.
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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.
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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.
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Contact
For enquiries about the MRI Physics Collective, please contact:
Mary Finnegan
Senior MR Physicist at the Imperial College Healthcare NHS Trust
Pete Lally
Assistant Professor in Magnetic Resonance (MR) Physics at Imperial College
Jan Sedlacik
MR Physicist at the Robert Steiner MR Unit, Hammersmith Hospital Campus