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Journal articleMa X, Tao Y, Zhang Z, et al., 2026,
Test-time generative augmentation for medical image segmentation
, MEDICAL IMAGE ANALYSIS, Vol: 109, ISSN: 1361-8415 -
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, 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 articleLi K, Xiao X, Zhong Z, et al., 2026,
Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning
, IEEE Open Journal of Engineering in Medicine and Biology, ISSN: 2644-1276Goal: Protein-ligand binding complexes are ubiquitous and essential to life. Protein-ligand binding affinity prediction (PLA) quantifies the binding strength between ligands and proteins, providing crucial insights for discovering and designing potential candidate ligands. While recent advances have been made in predicting protein-ligand complex structures, existing algorithms for interaction and affinity prediction suffer from a sharp decline in performance when handling ligands bound with novel unseen proteins. Methods: We propose IPBind, a geometric deep learning-based computational method, enabling robust predictions by leveraging interatomic potential between complex’s bound and unbound status. Results: Experimental results on widely used binding affinity prediction benchmarks demonstrate the effectiveness and universality of IPBind. Meanwhile, it provids atom-level insights into prediction. Conclusions: This work highlight the advantage of leveraging machine learning interatomic potential for predicting protein-ligand binding affinity. Index Terms—Deep learning, drug discovery, physics-informed neural networks, protein-ligand binding affinity prediction. Impact Statement–This study extends state-of-the-art deep learning algorithms to applications in protein-ligand binding affinity prediction. This study has implications for enhancing the generalization capability of protein-ligand interactions prediction methods by interatomic potential modeling.
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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 articleKhalique Z, Scott AD, Ferreira PF, et al., 2026,
Diffusion Tensor CMR Assessment of the Microstructural Response to Dobutamine Stress in Health and Comparison With Patients With Recovered Dilated Cardiomyopathy.
, Circ Cardiovasc Imaging, Vol: 19BACKGROUND: Contractile reserve assessment assesses myocardial performance and prognosis. The microstructural mechanisms that facilitate increased cardiac function have not been described, but can be studied using diffusion tensor cardiovascular magnetic resonance. Resting microstructural contractile function is characterized by reorientation of aggregated cardiomyocytes (sheetlets) from wall-parallel in diastole to a more wall-perpendicular configuration in systole, with the diffusion tensor cardiovascular magnetic resonance parameter E2A defining their orientation, and sheetlet mobility defining the angle through which they rotate. We used diffusion tensor cardiovascular magnetic resonance to identify the microstructural response to dobutamine stress in healthy volunteers and then compared with patients with recovered dilated cardiomyopathy (rDCM). METHODS: In this first-of-its-kind prospective observational study, 20 healthy volunteers and 32 patients with rDCM underwent diffusion tensor cardiovascular magnetic resonance at rest, during dobutamine, and on recovery. RESULTS: In healthy volunteers, both diastolic and systolic E2A increased with dobutamine stress (13±3° to 17±5°; P<0.001 and 59±11° to 65±7°; P=0.002). Sheetlet mobility remained unchanged (45±11° to 49±10°; P=0.19), but biphasic mean E2A increased (36±6° to 41±4°; P<0.001). In rDCM, diastolic E2A at rest was higher than in healthy volunteers (20±8° versus 13±3°, P<0.001), and sheetlet mobility was reduced (34±12° versus 45±11°; P<0.001). During dobutamine stress, rDCM diastolic and systolic E2A increased compared with rest (20±8° to 24±10°; P=0.001 and 54±13° to 63±11°; P=0.005). However, sheetlet mobility in patients with rDCM failed to increase with dobutamine to healthy levels (39±13° versus 49±
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Journal articleMa J, Jiang M, Fang X, et al., 2026,
Hybrid aggregation strategy with double inverted residual blocks for lightweight salient object detection
, NEURAL NETWORKS, Vol: 194, ISSN: 0893-6080 -
Journal articleZhang S, Nan Y, Fang Y, et al., 2026,
Dynamical multi-order responses and global semantic-infused adversarial learning: A robust airway segmentation method
, MEDICAL IMAGE ANALYSIS, Vol: 108, ISSN: 1361-8415 -
Journal articleJameel A, Smith J, Akgun S, et al., 2026,
Creation and clinical utility of a 3D atlas-based model for visualising brain nuclei targeted by MR-guided focused ultrasound thalamotomy for tremor.
, Biomed Phys Eng Express, Vol: 12Magnetic resonance guided focused ultrasound (MRgFUS) thalamotomy is an established treatment for tremor. MRgFUS utilises ultrasound to non-invasively thermally ablate or 'lesion' tremorgenic tissue. The success of treatment is contingent on accurate lesioning as assessed by tremor improvement and minimisation of adverse effects. However, coordinate planning and post-procedure lesion visualisation are difficult as the key targets, cannot be seen on standard clinical imaging. Thus, a computational tool is needed to aid target visualisation. A 3D atlas-based model was created using the Schaltenbrand-Wahren atlas. Key nuclei were manually delineated, interpolated and smoothed in 3D Slicer to create the model. Evaluation of targeting approaches across a seven-year period and patient-specific analyses of tremor treatments were performed. The anatomical position of MRgFUS lesions in the model were compared against varying clinical outcomes. The model provides an anatomical visualisation of how the change in targeting approach led to improved tremor suppression and a reduction in adverse effects for patients. This study demonstrates the successful development of a 3D atlas-based computational model of the brain target nuclei in MRgFUS thalamotomy and its clinical utility for tremor treatment analysis.
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Conference paperZhang Z, Lee K, Jing P, et al., 2026,
GEMA-Score: Granular Explainable Multi-Agent Scoring Framework for Radiology Report Evaluation
, Pages: 13025-13033, ISSN: 2159-5399Automatic medical report generation has the potential to support clinical diagnosis, reduce the workload of radiologists, and demonstrate potential for enhancing diagnostic consistency. However, current evaluation metrics often fail to reflect the clinical reliability of generated reports. Overlap-based methods overlook fine-grained details (e.g., location, sever-ity), diagnostic metrics are constrained by fixed vocabularies. Some diagnostic metrics are limited by fixed vocabularies or templates, reducing their ability to capture diverse clinical expressions. LLM-based metrics lack interpretable reasoning, limiting trust in clinical settings. Therefore, we propose a Granular Explainable Multi-Agent Score (GEMA-Score) in this paper, which conducts both objective quantification and subjective evaluation through a large language model-based multi-agent workflow. Our GEMA-Score parses structured reports and employs stable calculations through interactive exchanges of information among agents to assess disease diagnosis, location, severity, and uncertainty. Additionally, an LLM-based scoring agent evaluates completeness, readability, and clinical terminology while providing explanatory feedback. Extensive experiments show that GEMA-Score achieves the highest correlation with human experts on public datasets (Kendall = 0.69 on ReXVal; 0.45 on RadEvalX), demonstrating improved clinical scoring reliability.
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Journal articleKe Z, Xu S, Wang G, et al., 2026,
SAFE-Det: Scale-Adaptive Feature Enhancement for Remote Sensing Small-Object Detection
, IEEE Geoscience and Remote Sensing Letters, Vol: 23, ISSN: 1545-598XDue to the minuscule size of objects and the presence of background noise in remote sensing images, the object detection in such images remains a challenging task. In an effort to enhance the detection accuracy of small objects, this letter proposes a novel scale-adaptive feature enhancement detection (SAFE-Det) framework named SAFE-Det. This framework is specifically designed for small-object detection in remote sensing images. SAFE-Det utilizes the residual connections and a multiscale parallel attention downsample (MPAD) module to dynamically preserve the features of small objects, thereby reducing the information loss. Moreover, it incorporates an adaptive perception core (APC) model that combines a triple-branch pyramid attention (TBPA) and microtarget augmentation (MTA). This module aims to highlight small objects while suppressing the background noise. In addition, SAFE-Det features a specialized small-object detection head that leverages high-resolution shallow features, thus overcoming the limitations of conventional methods. Experimental results on the VisDrone2019 and VEDAI datasets indicate that SAFE-Det achieves an mAP@0.5 of 41.3% and 73.0%, respectively. These results represent respective improvements of 7.9% and 6.7% over baseline models. Codes are available at: https://github.com/xjh1017/Reunion
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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