Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleWei X, Liao Y, Zhu J, et al., 2026,
A hybrid CNN–Mamba state space model with pyramid-pooled skip connections for prostate tumor segmentation
, Expert Systems with Applications, Vol: 327, ISSN: 0957-4174Automatic prostate tumor segmentation in multiparametric magnetic resonance imaging (mpMRI) can support targeted biopsy and therapy planning, but remains challenging due to the small, heterogeneous and low-contrast nature of the lesions. CNN-based encoder-decoder models capture local detail well, but struggle with long-range reasoning. Transformers excel at global interactions, but are computationally expensive. Mamba/state-space models enable efficient global modelling, but often lack boundary-sensitive local refinement. We propose ProMamba: a hybrid CNN-Mamba segmentation framework combining local detail extraction with efficient global dependency modelling within a hierarchical U-Net architecture. Its dual-branch Pro-SSM block incorporates a depthwise dilated convolution module (DDCM) to extract multi-scale boundary cues, as well as a Mamba-based visual state-space (VSS) branch to aggregate multi-directional long-range context. The two branches are fused via channel concatenation and shuffling while preserving the 2D convolutional pathway and avoiding spatial flattening. Pyramid-pooled skip connections further reduce the semantic gap between the encoder and decoder and improve small-lesion delineation. Using five-fold cross-validation at the patient level on Prostate158, PI-CAI 2022 and an ethics-approved private cohort of 110 cases, ProMamba achieves Dice scores of 51.39%, 59.46% and 48.49% respectively, outperforming strong CNN, nnU-Net, efficient Transformer/hybrid and Mamba baselines. ProMamba also shows competitive latency and memory usage and generalizes well to PROMISE12 and ACDC, achieving Dice scores of 91.40% and 92.14%, respectively.
-
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 Med, Vol: 96, Pages: 1097-1110PURPOSE: 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 ° or 90 ° pulses in PE direction; and the proposed flip-back sequence with a nonselective 180 ° 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 ) 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 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 Med, Vol: 96, Pages: 1365-1378PURPOSE: 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 ) but lower diastolic scores ( 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 articleDeng F, Chu X, Shi W, et al., 2026,
Radiogenomic modeling of EGFR mutation status in brain metastases from lung adenocarcinoma: a multicenter study with biological interpretability
, npj Digital Medicine, ISSN: 2398-6352 -
Journal articleLee K, Jing P, Zhang Z, et al., 2026,
Seeing through experts' eyes: a foundational vision-language model trained on radiologists' gaze and reasoning
, npj Artificial Intelligence, ISSN: 3005-1460Large-scale vision–language models have shown promise in automating chest X-ray interpretation. However, their clinical utility remains limited by a fundamental gap between model outputs and the reasoning processes of radiologists. Most systems optimize for semantic information without emulating how experts visually examine and interpret medical images. As a result, current models often overlook critical findings, misrepresent anatomical context, or generate reports that diverge from established diagnostic workflows. Radiologists typically follow structured protocols (e.g., the ABCDEF approach that sequentially assesses the airways, breathing, circulation, diaphragm, external and foreign material). This standardized workflow ensures that all clinically relevant regions are examined in a consistent and systematic manner. It reduces the risk of missed findings, supports reliable diagnostic reasoning and facilitates clear communication between radiologists and referring clinicians. Emulating such expert strategies is essential for improving the trustworthiness and interpretability of automated report generation systems. Therefore, we introduce Gaze-X, a vision–language model that leverages radiologists’ eye-tracking data as a behavioral prior to model expert diagnostic reasoning. By incorporating gaze trajectories and fixation patterns into pretraining, Gaze-X learns to follow the spatial and temporal structure of radiologist attention and integratesvisual observations in a clinically meaningful sequence. This approach enables the model to align itsfocus with diagnostically relevant regions and to emulate the interpretive logic underlying expert reports. Using a curated dataset of gaze recordings, comprising over 30,000 key frames across diversedisease categories, from five radiologists interpreting chest X-rays, we demonstrate that Gaze-X produces more accurate, interpretable, and expert-consistent outputs across a range of clinically relevanttasks a
-
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.
-
Journal articleWang T, Zhang Z, Zhou Y, et al., 2026,
From noisy labels to intrinsic structure: A geometric-structural dual-guided framework for noise-robust medical image segmentation.
, Med Image Anal, Vol: 112The effectiveness of convolutional neural networks in medical image segmentation relies on large-scale, high-quality annotations, which are costly and time-consuming to obtain. Even expert-labeled datasets inevitably contain noise arising from subjectivity and coarse delineations, which disrupt feature learning and adversely impact model performance. To address these challenges, this study proposes a Geometric-Structural Dual-Guided Network (GSD-Net), which integrates geometric and structural cues to improve robustness against noisy annotations. It incorporates a Geometric Distance-Aware module that dynamically adjusts pixel-level weights using geometric features, thereby strengthening supervision in reliable regions while suppressing noise. A Structure-Guided Label Refinement module further refines labels with structural priors, and a Knowledge Transfer module enriches supervision and improves sensitivity to local details. To comprehensively assess its effectiveness, we evaluated GSD-Net on six publicly available datasets: four containing three types of simulated label noise, and two with multi-expert annotations that reflect real-world subjectivity and labeling inconsistencies. Experimental results demonstrate that GSD-Net achieves state-of-the-art performance under noisy annotations, achieving improvements of 1.58% on Kvasir, 22.76% on Shenzhen, 8.87% on BU_SUC, and 1.77% on BraTS2020 under SR simulated noise. The code of this study is available at https://github.com/ortonwang/GSD-Net.
-
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
-
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 articlePaul S, Munoz C, Ferreira P, et al., 2026,
Motion compensated spin echo cardiac diffusion tensor imaging in multiple cardiac phases using an ultrahigh gradient strength scanner
, Journal of Cardiovascular Magnetic Resonance, Vol: 28, ISSN: 1097-6647BackgroundCardiac diffusion tensor imaging (cDTI) has traditionally relied on inefficient stimulated echo techniques to robustly assess microstructural changes over the cardiac cycle. Ultrahigh gradient strength systems (>80mT/m) allow shorter motion compensated diffusion encoding. This study compares the ability of high and ultrahigh strength gradient systems to provide systolic and diastolic motion compensated spin echo (MCSE) cDTI.MethodsSecond order MCSE sequences were developed for a research-only Siemens 3T Connectom (300mT/m maximum gradient amplitude per axis) and breath hold cDTI was acquired at peak systole and end diastole. Acquisitions used the maximum achievable gradient strength (GUH, 116mT/m) and also limited to typical high gradient strengths (GH, 66mT/m based on 80mT/m maximum allowable), giving TE=48ms and 58ms respectively. Data were acquired at 2.8x2.8x8mm3, b=500s/mm2 (8 averages) and b=150s/mm2 (2 averages) in 6 encoding directions.Results22 healthy subjects were recruited. 20/21 and 21/22 systolic acquisitions at GUH and GH respectively met the >50% criteria of the circumferential myocardium showing the expected transmural variation in helix angle. For GUH and GH (16/20) 80% and (16/22) 73% of diastolic acquisitions were successful respectively. SNR was increased using GUH compared to GH (median [IQR]: 112.9 [3.8] vs. 9.6 [2.9], p=0.0002 diastole, 15.6 [5.9] vs. 12.5 [6.7], p=0.006 systole). Using GUH fractional anisotropy was lower in systole (0.349 [0.040] vs. 0.373 [0.019], p=0.002) and GUH transmural helix angle gradient (HAG) was steeper in diastole (-0.70 [0.17] vs. -0.55 [0.12] ˚/%, p=0.04). At both GUH and GH, sheetlet angle (|E2A|) was higher in systole than in diastole (30.7 [7.3] vs. 21.3 [6.7]˚ p=10-4 and 32.6 [10.9] vs. 26.0 [7.4]˚, p=0.03 respectively). Differences in HAG between phases were only apparent with GH (-0.88 [0.23] vs. -0.55 [0.15], p=10-4) and differences in the mean diffusivity only with GUH (1.64 [0.11] vs
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.
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