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Journal articleJiang C, Xing X, Nan Y, et al., 2025,
A lung structure and function information-guided residual diffusion model for predicting idiopathic pulmonary fibrosis progression
, MEDICAL IMAGE ANALYSIS, Vol: 103, ISSN: 1361-8415 -
Journal articleSelvaggi P, Osugo M, Zahid U, et al., 2025,
Antipsychotics cause reversible structural brain changes within one week
, NEUROPSYCHOPHARMACOLOGY, Vol: 50, Pages: 1275-1283, ISSN: 0893-133X- Cite
- Citations: 1
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Journal articleNing J, Marshall D, Gao Y, et al., 2025,
Unpaired translation of chest X-ray images for lung opacity diagnosis via adaptive activation masks and cross-domain alignment
, PATTERN RECOGNITION LETTERS, Vol: 193, Pages: 21-28, ISSN: 0167-8655 -
Journal articleWang Z, Yu X, Wang C, et al., 2025,
One for multiple: Physics-informed synthetic data boosts generalizable deep learning for fast MRI reconstruction
, MEDICAL IMAGE ANALYSIS, Vol: 103, ISSN: 1361-8415- Cite
- Citations: 1
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Journal articleArmour C, Gopalan D, Statton B, et al., 2025,
Patient-specific modelling of pulmonary arterial hypertension: wall shear stress correlates with disease severity
, Frontiers in Bioengineering and Biotechnology, ISSN: 2296-4185Introduction: Pulmonary arterial hypertension (PAH) requires an invasive right heart catheter (RHC) procedure for diagnosis. Patients can present with initial symptoms and interact with healthcare institutes for up to three years before referral for diagnosis. Thus, there is a great need to develop noninvasive tools, to better screen patients and improve early diagnosis rates. Methods: seven patients diagnosed and treated for PAH were included in this study. Patient-specific computational fluid dynamics (CFD) models were built for all patients, with all model parameters tuned using non-invasive imaging data, including CT, cardiac MR, echocardiogram, and 4D-flow MRI scanscrucially, a 3D inlet velocity profile was derived from 4D-flow MRI. Results: CFD models were quantitatively and qualitatively well matched with in-vivo 4D-flow hemodynamics. A linear correlation of R 2 = 0.84 was found between CFD derived time-averaged wall shear stress (TAWSS) and RHC measured mean pulmonary pressure (key diagnostic value): low TAWSS correlated with high pressure. Conclusions: This study highlights TAWSS as a potential computational biomarker for PAH. The clinical use of TAWSS to diagnose and stratify PAH patients has the potential to greatly improve patient outcomes. Further work is ongoing to validate these findings in larger cohorts.
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Journal articleSu Y, Gao L, Plaza A, et al., 2025,
SRViT: Self-Supervised Relation-Aware Vision Transformer for Hyperspectral Unmixing
, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, ISSN: 2162-237X- Cite
- Citations: 5
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Journal articleDallArmellina E, Ennis DB, Axel L, et al., 2025,
Cardiac diffusion-weighted and tensor imaging: a Society for Cardiovascular Magnetic Resonance (SCMR) special interest group consensus statement
, Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647Thanks to recent developments in Cardiovascular magnetic resonance (CMR), cardiac diffusion-weighted magnetic resonance is fast emerging in a range of clinical applications. Cardiac diffusion-weighted imaging (cDWI) and diffusion tensor imaging (cDTI) now enable investigators and clinicians to assess and quantify the 3D microstructure of the heart. Free-contrast DWI is uniquely sensitized to the presence and displacement of water molecules within the myocardial tissue, including the intra-cellular, extra-cellular and intra-vascular spaces. CMR can determine changes in microstructure by quantifying: a) mean diffusivity (MD) –measuring the magnitude of diffusion; b) fractional anisotropy (FA) – specifying the directionality of diffusion; c) helix angle (HA) and transverse angle (TA) –indicating the orientation of the cardiomyocytes; d) E2A and E2A mobility – measuring the alignment and systolic-diastolic mobility of the sheetlets, respectively.This document provides recommendations for both clinical and research cDWI and cDTI, based on published evidence when available and expert consensus when not. It introduces the cardiac microstructure focusing on the cardiomyocytes and their role in cardiac physiology and pathophysiology. It highlights methods, observations and recommendations in terminology, acquisition schemes, post-processing pipelines, data analysis and interpretation of the different biomarkers. Despite the ongoing challenges discussed in the document and the need for ongoing technical improvements, it is clear that cDTI is indeed feasible, can be accurately and reproducibly performed and, most importantly, can provide unique insights into myocardial pathophysiology.
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Journal articleMunoz C, Lim E, Ferreira PF, et al., 2025,
Simultaneous non-contrast assessment of cardiac microstructure and perfusion in vivo in the human heart
, Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647BackgroundIntravoxel incoherent motion (IVIM) imaging can provide information on cardiac microstructure and microvascular perfusion from a single examination. However, the spin-echo based approaches typically used for cardiac IVIM suffer from low sensitivity to changes in perfusion.ObjectivesTo develop a stimulated-echo (STEAM)-based method for IVIM and diffusion tensor cardiovascular magnetic resonance to simultaneously provide biomarkers of microstructure and perfusion in vivo in the human heart.MethodsHere we introduce a novel STEAM-IVIM sequence incorporating phase cycling to obtain true non-diffusion weighted images (b=0 s/mm2). STEAM-IVIM imaging was performed at 20 b-values (0 to 1000 s/mm2) to enable accurate estimation of the IVIM parameters, and with six diffusion encoding directions to enable reconstruction of the diffusion tensor. 20 healthy subjects (8 female, median age 31 years) were imaged on a clinical 3 T system with STEAM-IVIM. A simulation study was performed to investigate the optimal fitting algorithms for the IVIM parameters, which was subsequently used to create pixel-wise IVIM parameter maps for the in vivo acquisitions.ResultsGood image quality across the myocardium was obtained for all b-values. Mean(±SD) IVIM parameter estimates were: diffusivity D=0.83±0.07×10-3 mm2/s, perfusion coefficient D*=19.08±6.48×10-3 mm2/s, perfusion fraction f=19.72±4.11%, and mean diffusion tensor parameters were: mean diffusivity=0.88±0.06×10-3 mm2/s, fractional anisotropy=0.45±0.04, absolute E2 angle=55.29±6.38º, helix angle gradient=-0.68±0.18º/%.ConclusionPhase-cycled STEAM-IVIM enables fitting of cardiac diffusion tensor and perfusion parameters in healthy subjects and shows promise for the simultaneous detection of microstructural aberration and perfusion abnormalities in the presence of cardiac disease without the need for exogenous contrast agents.
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Journal articleYang G, Zhang J, Papanastasiou G, et al., 2025,
Editorial Emerging Horizons: The Rise of Large Language Models and Cross-Modal Generative AI
, IEEE TRANSACTIONS ON BIG DATA, Vol: 11, Pages: 896-897, ISSN: 2332-7790 -
Journal articleInglese M, Conti A, Toschi N, 2025,
Radiomics across modalities: a comprehensive review of neurodegenerative diseases
, CLINICAL RADIOLOGY, Vol: 85, ISSN: 0009-9260- Cite
- Citations: 2
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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