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Abstract #0733

Resolving bundle-specific intra-axonal T2 within a voxel using a microstructure-informed approach

Muhamed Barakovic1,2,3, Chantal MW Tax1, Umesh S Rudrapatna1, Jonathan Rafael-Patino2, Cristina Granziera3, Jean-Philippe Thiran2,4, Alessandro Daducci5, Erick J Canales-Rodriguez2,4,6, and Derek K Jones1,7
1Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom, 2Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 3Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 4Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland, 5Department of Computer Science, University of Verona, Verona, Italy, 6FIDMAG Germanes Hospitalaries Research Foundation, Barcelona, Spain, 7Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia

At the typical spatial resolution of MRI, approximately 60-90% of voxels in the human brain contain multiple fibre populations. Quantifying microstructural properties of distinct fibre bundles within a voxel is challenging. While progress has been made for diffusion and T1-relaxation properties, resolving intra-voxel T2 heterogeneity remains an open question. Here we proposed a novel framework, COMMIT-T2, that uses tractography-based spatial regularization. Unlike previously-proposed voxel-based methods, COMMIT-T2 can recover bundle-specific T2 values within a voxel. Adding this new dimension to the microstructural characterisation of white matter pathways improves the power of tractometry to detect subtle differences in tissue properties.

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