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Journal articleTänzer M, Scott AD, Khalique Z, et al., 2025,
Accelerating cDTI with deep learning-based tensor de-noising and breath hold reduction. a step towards improved efficiency and clinical feasibility
, Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647BackgroundCardiac Diffusion Tensor Imaging (cDTI) non-invasively provides unique insights into cardiac microstructure. Current protocols require multiple breath-hold repetitions to achieve adequate signal-to-noise ratio, resulting in lengthy scan times. The aim of this study was to develop a cDTI de-noising method that would enable the reduction of repetitions while preserving image quality.MethodsWe present a novel de-noising framework for cDTI acceleration centred on three fundamental advances: (1) a paradigm shift from image-based to tensor-space de-noising that better preserves structural information, (2) an ensemble of Vision Transformer-based models specifically optimised for tensor processing through adversarial training, and (3) a sophisticated data augmentation strategy that maximises training data utilisation through dynamic repetition selection.ResultsOur approach reduces scan times by a factor of up to 4 while achieving a 20% reduction in cDTI maps errors over existing de-noising methods (Table 1) and preserving anatomical features such as infarct characterisation and transmural cardiomyocyte orientation patterns. Crucially, our proposed method succeeds in clinical cases where other algorithms previously failed.ConclusionsThis demonstrates substantial improvements in cDTI acquisition efficiency, achieving up to 4-fold scan time reduction (3-5 breath-holds) while maintaining diagnostic accuracy across diverse cardiac pathologies.
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Journal articleTeh I, Moulin K, Ferreira PF, et al., 2025,
Multi-center investigation of cardiac diffusion tensor imaging in healthy volunteers by the Society of Cardiovascular Magnetic Resonance Cardiac Diffusion Special Interest Group NETwork (SIGNET)
, Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647BackgroundCardiac diffusion tensor imaging (cDTI) is an emerging technique for microstructural characterization of the heart and has shown clinical potential in a range of cardiomyopathies. However, there is substantial variation reported for in vivo cDTI results across the literature, and sensitivity of cDTI to differences in imaging sites, scanners, acquisition protocols, and post-processing methods remains incompletely understood.MethodsSIGNET is a prospective multi-center, observational study in traveling and non-traveling healthy volunteers. The study was initiated by the executive board of the Society of Cardiovascular Magnetic Resonance (SCMR) Cardiac Diffusion Special Interest Group (SIG) as a follow-up to a previous multi-center study on phantom validation of cardiac DTI and a recently published SCMR consensus statement on cardiac diffusion MRI. The study has been developed by the Project Management Committee in consultation with the SCMR cardiac diffusion SIG, which includes international experts in cardiac diffusion MRI. To date, more than 20 international institutions have engaged with the study, including sites that are new to cardiac DTI, making this the largest collaborative effort in the field.DiscussionSIGNET will provide important information about the key sources of variation in cardiac DTI. This will help rationalize strategies for addressing and minimizing such variation. Harmonization of protocols in this and future studies will underpin efforts to translate cardiac DTI for clinical application.
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Journal articleJin W, Tian X, Wang N, et al., 2025,
Representation-driven sampling and adaptive policy resetting for improving multi-Agent reinforcement learning
, NEURAL NETWORKS, Vol: 192, ISSN: 0893-6080 -
Journal articleHao P, Wang H, Yang G, et al., 2025,
Enhancing Visual Reasoning With LLM-Powered Knowledge Graphs for Visual Question Localized-Answering in Robotic Surgery
, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 29, Pages: 9027-9040, ISSN: 2168-2194- Cite
- Citations: 2
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Journal articleWang H, Chen Y, Chen W, et al., 2025,
Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation With Selective State-Space Model.
, IEEE Trans Med Imaging, Vol: 44, Pages: 4811-4825Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-resolution views of the retina with typically spanning 200 degrees. Accurate segmentation of vessels in UWF-SLO images is essential for detecting and diagnosing fundus disease. Recent studies highlight that Mamba's selective State Space Model (SSM) excels in modeling long-range dependencies with linear computational complexity, making it highly suitable for preserving the continuity of elongated vessel structures, especially for high-resolution UWF images. Inspired by this, we propose the Serpentine Mamba (Serp-Mamba) network to address this challenging task. Specifically, we recognize the intricate, varied, and delicate nature of the tubular structure of vessels. Furthermore, the high-resolution of UWF-SLO images exacerbates the imbalance between the vessel and background categories. Based on the above observations, we first devise a Serpentine Interwoven Adaptive (SIA) scan mechanism, which scans UWF-SLO images along curved vessel structures in a snake-like crawling manner. This approach, consistent with vascular texture transformations, ensures the effective and continuous capture of curved vascular structure features. Second, we propose an Ambiguity-Driven Dual Recalibration (ADDR) module to address the category imbalance problem intensified by high-resolution images. Our ADDR module delineates pixels by two learnable thresholds and refines ambiguous pixels through a dual-driven strategy, thereby accurately distinguishing vessels and background regions. Experiment results on three datasets demonstrate the superior performance of our Serp-Mamba on high-resolution vessel segmentation. We also conduct a series of ablation studies to verify the impact of our designs. Our code will be released upon publication (https://github.com/whq-xxh/Serp-Mamba).
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Journal articleWang Z, Xiao M, Zhou Y, et al., 2025,
Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 72, Pages: 3642-3654, ISSN: 0018-9294- Cite
- Citations: 4
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Journal articleConnor S, Lally P, Pai I, et al., 2025,
7-Tesla sodium magnetic resonance imaging of the inner ears in unilateral Ménière’s disease and endolymphatic hydrops: an exploratory study
, BMC Medical Imaging, Vol: 25, ISSN: 1471-2342BackgroundWhilst delayed post-gadolinium MRI has led to a shift in the diagnostic paradigm of Meniere’s Disease (MD), there remains a strong desire to develop a non-contrast enhanced MRI technique to detect and monitor MD. The endolymphatic space (ES) undergoes hydropic expansion in Ménière’s Disease (MD) and the concentration of sodium ions in the endolymph is at least 10 times lower than that in the perilymph. It was hypothesised that the lower sodium (23Na) concentration in the endolymph relative to the surrounding perilymph would result in a differential reduction in 23Na-MRI signal in inner ears with endolymphatic hydrops (EH). This proof of principle study explored the feasibility of 7-Tesla (7T) 23Na-MRI to lateralise EH ears in unilateral MD.MethodsIn this prospective study, 7T 23Na-MRI was performed in participants with both unilateral definite MD and severe vestibulo-cochlear EH on a delayed post-gadolinium real inversion recovery sequence. Two blinded independent observers qualitatively graded the visibility and anatomical compatibility of inner ear 23Na MRI signal intensity (NaSI), before and after registering to 3D T2-weighted (T2w) MRI and determined the certainty of EH laterality. The internal auditory meatus (IAM), cochlea and vestibule were segmented using 3D Slicer and NaSI was quantified. Inner ear median NaSI were scaled to the adjacent IAM median NaSI and compared between the two ears.ResultsIn 4 unilateral MD participants (mean age 60.3 years, 2 men), both observers correctly predicted EH laterality in 1/4 before and 3/4 participants after fusion to 3D T2w MRI. There was no incorrect lateralisation of EH by either observer, either before or after registration and fusion. In the 3 participants correctly lateralised, quantitative analysis revealed the median inner ear NaSI scaled to the ipsilateral IAM was 1.2–2.8 times higher in the normal cochlea and 1.9–2.9 times higher in the vestibule, compared to
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Journal articleAi R, Mao L, Jin X, et al., 2025,
NAD<SUP>+</SUP> reverses Alzheimer's neurological deficits via regulating differential alternative RNA splicing of <i>EVA1C</i>
, SCIENCE ADVANCES, Vol: 11 -
Journal articleWang J, Ruan D, Li Y, et al., 2025,
Dynamic mask stitching-guided region consistency for semi-supervised 3D medical image segmentation
, EXPERT SYSTEMS WITH APPLICATIONS, Vol: 292, ISSN: 0957-4174 -
Journal articlePan Q, Li Z, Qiao W, et al., 2025,
AMVLM: Alignment-Multiplicity Aware Vision-Language Model for Semi-Supervised Medical Image Segmentation
, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 44, Pages: 4307-4322, ISSN: 0278-0062
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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