RADAR-WMH: Relaxometry And Diffusion Analysis beyond Radiologically defined WMH
Roduit, V.; Carneiro, F.; Lutti, A.; Vollenweider, P.; Marques-Vidal, P.; Vaucher, J.; Preisig, M.; Thiran, J.-P.; Bussy, A.; Draganski, B.
Show abstract
Background: White matter hyperintensities (WMH) represent the most visible manifestation of cerebral small vessel disease and of white matter pathology more broadly, yet empirical evidence points to a brain tissue injury extending beyond radiologically detectable lesions on fluid-attenuated inversion recovery (FLAIR) MRI. We present RADAR-WMH (Relaxometry And Diffusion Analysis for Radiological WMH), a multicontrast MRI machine learning framework that characterises white matter pathology through tissue microstructural information rather than lesion contrast alone. Methods: RADAR-WMH was trained on quantitative relaxometry and diffusion-weighted MRI acquired in community-dwelling participants (mean age 59.6 years [SD 22.4], 60.8% women, n=148) using a LightGBM classifier integrating local, textural, and anatomical features at the voxel level. Biological validity was assessed through longitudinal analyses and associations with age, cardio-vascular risk, and cognitive performance in independent cohorts. Results: RADAR-WMH achieved segmentation performance comparable to state-of-the-art FLAIR-based approaches without requiring FLAIR or T1-weighted data. Mean diffusivity was the most influential feature for lesion classification. Beyond FLAIR-defined WMH, RADAR-WMH identified tissue pathology extending outside lesion borders characterised by myelin loss, axonal injury, and increased extracellular water. These microstructural signatures persisted over follow-up and showed stronger association with age, systolic blood pressure, and cognitive performance than corresponding tissue properties restricted to FLAIR-defined WMH extent. Conclusions: RADAR-WMH reveals a significant burden of biologically meaningful white matter injury that remains invisible to FLAIR-defined WMH segmentation. By capturing microstructural pathology linked to vascular risk, cognitive decline, and lesion evolution, RADAR-WMH may provide more sensitive markers of cerebral small vessel disease than FLAIR-visible WMH alone.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- More than the sum of its parts: disrupted core-periphery of multiplex networks in multiple sclerosis 94%
- WMH-DualTasker: A weakly-supervised deep learning model for automated white matter hyperintensities segmentation and visual rating prediction 94%
- Delineating In-Vivo T1-Weighted Intensity Profiles Within the Human Insula Cortex Using 7-Tesla MRI 94%
Similar papers in this journal
- Magnetisation transfer, diffusion and g-ratio measures of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: a longitudinal microstructural MRI study 94%
- Fluid and White Matter Suppression Contrasts MRI Improves Deep Learning Detection of Multiple Sclerosis Cortical Lesions 93%
- Contribution of white matter hyperintensities to ventricular enlargement in older adults 93%
Similar papers in this journal
- Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration 95%
- White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life 94%
- Detection of Nanoliter-Scale Cortical Perivascular Spaces Using Heavily T2-weighted MRI at 7T 94%
Similar papers in this journal
- In vivo myelin imaging and tissue microstructure in white matter hyperintensities and perilesional white matter 95%
- Assessment of white matter hyperintensity severity using multimodal MRI in Alzheimer's Disease 94%
- Spinal cord MRI and MRS Detect Early-stage Alterations and Disease Progression in Friedreich Ataxia 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.