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Journal articleZhou T, Li M, Ruan S, et al., 2026, , Information Fusion, Vol: 129, ISSN: 1566-2535
Accurate 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 articleHasan MK, Yang G, Yap CH, 2026, , Med Image Anal, Vol: 110
Cardiac 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 articleLuo Y, Ferreira PF, Wen K, et al., 2026, , Magn Reson Med
PURPOSE: 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.
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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, ISSN: 2644-1276 -
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: 2644-1276Abstract—Stain 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 andH&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.
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Journal articleLiao Y, Zheng Y, Zhu J, et al., 2026, , Displays, Vol: 92, ISSN: 0141-9382
Glioblastoma (GBM) is an aggressive brain tumor associated with poor prognosis and limited treatment options. The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a critical biomarker for predicting the efficacy of temozolomide chemotherapy in GBM patients. However, current methods for determining MGMT promoter methylation, including invasive and costly techniques, hinder their widespread clinical application. In this study, we propose a novel non-invasive deep learning framework based on a Mixture-of-Experts (MoE) architecture for predicting MGMT promoter methylation status using multi-modal magnetic resonance imaging (MRI) data. Our MoE model incorporates modality-specific expert networks built on the ResNet18 architecture, with a self-attention-based gating mechanism that dynamically selects and integrates the most relevant features across MRI modalities (T1-weighted, contrast-enhanced T1, T2-weighted, and fluid-attenuated inversion recovery). We evaluate the proposed framework on the BraTS2021 and TCGA-GBM datasets, showing superior performance compared to conventional deep learning models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Furthermore, Grad-CAM visualizations provide enhanced interpretability by highlighting biologically relevant regions in the tumor and peritumoral areas that influence model predictions. The proposed framework represents a promising tool for integrating imaging biomarkers into precision oncology workflows, offering a scalable, cost-effective, and interpretable solution for non-invasive MGMT methylation prediction in GBM.
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Journal articleSveinsson B, Vangel M, Rowe OE, et al., 2026, , Muscle Nerve
INTRODUCTION/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 articleZhang C, Wu Y, Boyer-Chammard J, et al., 2026, , IEEE Trans Biomed Eng, Vol: PP
Spreading depolarizations (SDs) are key drivers of secondary brain injury, yet existing bedside monitoring methods that use electrocorticography (ECoG) analyze electrodes and frequency bands separately, thereby obscuring the joint spatiotemporal patterns of SDs. Therefore, this paper introduces a multi-scale signal-image fusion framework that for the first time enables SDmonitoring as a joint multi-modal multi-band spectral image-based analysis. The ECoG signal is converted into a persistent spectral de-weighted spectrogram (PSd-Spec) and joined with multi-band features, through Transformer-CNN jointly empowered blocks: Multi-Channel and Band Transformer Block (MCBTB) and Multi-Scale Adaptive Fusion (MSAF). The network extracts short- and long-range dynamics in a multi-scale time window, while an attention-driven channel weighting module adaptively models the spatial propagation of the electrode strips. On 500h of neuro-ICU recordings, the proposed approach achieved 92.6% accuracy, 84.9% sensitivity. Relative to the best single-modality base line, performance improved by at least 18%, and SD onset was identified on average of 8 min before expert observation. The results suggest that multi-scale fusion of spectral images with ECoG signals yields a clinically actionable early-warning approach and extends quantitative imaging methods to intracranial electrophysiology.
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Journal articleCheng CW, Huang J, Zhang Y, et al., 2026, , Journal of Computational Physics, Vol: 548, ISSN: 0021-9991
Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have emerged as the preferred architecture for PDEs due to their ability to capture intricate dependencies. However, they struggle with representing continuous dynamics and long-range interactions. To overcome these limitations, we introduce the Mamba Neural Operator (MNO), a novel framework that enhances neural operator-based techniques for solving PDEs. MNO establishes a formal theoretical connection between structured state-space models (SSMs) and neural operators, offering a unified structure that can adapt to diverse architectures, including Transformer-based models. By leveraging the structured design of SSMs, MNO captures long-range dependencies and continuous dynamics more effectively than traditional Transformers. Through extensive analysis, we show that MNO significantly boosts the expressive power and accuracy of neural operators, making it not just a complement but a superior framework for PDE-related tasks, bridging the gap between efficient representation and accurate solution approximation.
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Journal articleJing P, Lee K, Zhang Z, et al., 2026, , MEDICAL IMAGE ANALYSIS, Vol: 109, ISSN: 1361-8415
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Contact
For enquiries about the MRI Physics Collective, please contact:
Mary Finnegan
Senior MR Physicist at the 51³Ô¹ÏÍø College Healthcare NHS Trust
Pete Lally
Assistant Professor in Magnetic Resonance (MR) Physics at 51³Ô¹ÏÍø College
Jan Sedlacik
MR Physicist at the Robert Steiner MR Unit, Hammersmith Hospital Campus