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Journal articleSedlacik J, McGurk KA, Tokarczuk PF, et al., 2026,
Quantitative ventricular trabeculation assessment in cardiac MRI: optimised blood-pool segmentation, box-counting fractal analysis and non-fractal measurements.
, Int J Cardiovasc ImagingQuantitative assessment of the ventricular trabeculation by fractal dimension (FD) involves complex processing steps which may impact the results. We optimised the automated processing workflow for a reliable assessment of the left and right ventricles at end-diastole and end-systole which is suitable for the automated analysis of large-scale cohorts. Ventricular trabeculae and blood were segmented using a level-set method optimised to exclude pixels outside the heart on short-axis cardiac MRI. FD was derived by box-counting the trabeculae/blood boundary while investigating the impact of box size, sampling and rotation. Alternative non-fractal measures - the convexity related boundary length ratio (BLR) and the trabeculated mass ratio (TMR) - were also investigated.FD values with and without optimisation showed a strong linear correlation (R2 = 0.81) and narrow agreement limit (1.96·SD = 0.063) only for the end-diastolic left ventricle. Linear correlation and agreement was good between the optimised FD and BLR values for both ventricles and cardiac phases (R2 = 0.70-0.92, 1.96·SD = 0.037-0.064) but not for TMR (R2 = 0-0.37, 1.96·SD = 0.16-1.4). FD, BLR and TMR differed significantly (p < 0.001) between end-diastole and end-systole with lower FD (-0.07 ± 0.06) but higher BLR (0.31 ± 0.25) and TMR (0.26 ± 0.13) values at end-systole.The previously used fractal analysis is suboptimal except for assessing the end-diastole left ventricle. The optimised fractal analysis is suitable for the left and right ventricle at end-diastole and end-systole. The easy to compute non-fractal BLR gives equivalent information like FD. The volume-based TMR, on the other hand, captures different features of the trabeculation.
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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 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 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 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.
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Journal articleAzma Y, Collins D, Lally P, et al., 2026,
Patient-specific biases in fat fraction estimates of malignant bone marrow due to relaxation times measured with STEAM at 3T
, NMR in Biomedicine, Vol: 39, ISSN: 0952-3480Semi-quantitative fat fraction estimation using 2-point Dixon sequences is widely used in whole body (WB) MR imaging for malignant bone disease but is biased by relaxation times. Understanding this bias requires water- and fat-specific relaxometry data in normal-appearing marrow and lesions. This study measured bone marrow relaxation times in healthy volunteers and WB-MRI patients using MRS at 3T. Five healthy female volunteers (mean age 38.0 ± 2.5 years) and 24 patients with malignant bone disease undergoing clinical WB-MRI (13 male; mean age 67.7 ± 9.7 years; primary cancers: breast = 5, melanoma = 1, multiple myeloma = 8, prostate = 10) underwent variable inversion/echo time STEAM and 3D gradient echo fat-water imaging. MRS water and fat peaks were fitted to determine T1, T2, R2* (from linewidths), and proton density fat fraction (PDFF). Scan-rescan repeatability of MRS parameters was assessed in volunteers. Lesions were classified by disease state according to clinical reports and segmented in Dixon imaging data for comparison of fat fraction estimates with MRS. Repeatability was evaluated using coefficients of variation. Summary statistics (mean, standard deviation and range) were reported; exploratory inferential statistics were also determined with normality (Shapiro-Wilk) and variance (Levene’s) tests before one-way ANOVA and Tukey’s comparisons (p < 0.05). Monte Carlo simulations assessed relaxation bias on PDFF.All quantitative MRS parameters were repeatable (coefficient of variation < 10%). Water T1 and T2 were most sensitive to disease state in patients, ranging from 1121–2206 ms and 15–71 ms respectively, and were demonstrated to substantially affect 2-point Dixon fat fraction estimates with Monte Carlo simulation. Imaging PDFF achieves closer agreement with MRS PDFF than 2-point Dixon methods. These findings remain preliminary due to the small sample size, but they suggest value in future studies with larger
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Journal articleLiao Y, Zheng Y, Zhu J, et al., 2026,
Self-attention-based mixture-of-experts framework for non-invasive prediction of MGMT promoter methylation in glioblastoma using multi-modal MRI
, Displays, Vol: 92, ISSN: 0141-9382Glioblastoma (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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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