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Researchers developed a new framework for diffusion MRI that can characterize brain cellular architecture in under 10 minutes of scan time, combining optimized imaging sequences with advanced computational inference methods. The approach was validated through computer simulations and real brain scans from both humans and rodents, producing robust and reproducible measurements of brain microstructure. The resulting metrics correlate with spatial patterns of cell-specific gene expression across the brain, demonstrating biological validity.
Why it matters
This framework significantly reduces the time and complexity barriers that have prevented advanced diffusion MRI from being used in clinical settings. By making detailed brain microstructure imaging practical for routine use, it could enable better diagnosis, patient stratification, and monitoring of neurological and psychiatric disorders.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Diffusion-weighted MRI, beyond the commonly used diffusion tensor framework, offers a unique window into tissue microstructure in vivo, yet its clinical adoption has remained limited. Major barriers include the complexity of diffusion MRI sequence design, lengthy acquisition protocols, and the challenges associated with robust estimation of high-dimensional microstructural model parameters. Here, we address these limitations by combining optimised diffusion encoding with state-of-the-art simulation-based inference, establishing a clinically feasible framework for multi-compartment diffusion modelling. We validate the approach through i) in-depth in silico experiments and ii) in vivo studies made up of both human and rodent data. The resulting microstructural metrics are robust, reproducible across healthy individuals and show significant spatial associations with brain-wide expression patterns of cell-specific genes. Requiring less than 10 minutes of acquisition time, this framework substantially lowers the barriers to advanced microstructural imaging, a prerequisite step toward its eventual evaluation for the diagnosis, stratification, and monitoring of brain disorders.