Stamatios N Sotiropoulos¹, Saad Jbabdi¹, An T Vu², Jesper L Andersson¹, Steen Moeller², Christophe Lenglet², Essa Yacoub², Kamil Ugurbil², and Timothy Behrens^1
1FMRIB Centre, University of Oxford, Oxford, United Kingdom, ²Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

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Daniel C. Alexander¹, Darko Zikic², Viktor Wottschel³, Jiaying Zhang¹, Hui Zhang¹, and Antonio Criminisi^2
1Dept. Computer Science, University College London, London, London, United Kingdom, ²Microsoft Research, Cambridge, United Kingdom, ³Institute of Neurology, University College London, London, United Kingdom

Learning the low-level structure of images from high-quality bespoke data sets can substantially improve the content of images reconstructed from more everyday acquisitions. The abstract presents a method that achieves this and demonstrates it using diffusion MRI data from the human connectome project.

Qiuyun Fan¹, Aapo Nummenmaa¹, Thomas Witzel¹, Susie Y. Huang¹, Jonathan R. Polimeni¹, Van J. Wedeen¹, Bruce R. Rosen¹, and Lawrence L. Wald^1
1Massachusetts General Hospital, Charlestown, MA, United States

Mapping the cortico-cortical brain connections relies on the accurate characterization of the axonal structures in both deep white matter and cortical white/gray boundary regions where fibers can sharply curve from the white matter fascicles into the cortical ribbon. Thus, in the region near the cortex, we ideally want a lower b value but high spatial resolution (high-k). In contrast, structures in deep white matter need high b-values to resolve multiple fiber crossings and therefore need to give up some spatial resolution to preserve sensitivity. Thus, deep white matter voxels call for high-b/low-k acquisition. In this work, we acquired b=8000s/mm2 1.9mm isotropic resolution and b=1500s/mm2 1.0mm isotropic resolution diffusion data on the MGH-USC Connectom scanner. We demonstrated that different brain structures require different imaging parameters to be resolved, and diffusion tractography can be improved by fusing the two datasets.

Ricardo Coronado-Leija¹, Alonso Ramirez-Manzanares¹, Jose Luis Marroquin¹, and Rolando Jose Biscay^1
1Computer Science Department, Centro de Investigacion en Matematicas, Guanajuato, Guanajuato, Mexico

We propose a Multi-resolution Discrete Search method to estimate white matter microstructure based on three key ideas: 1) A Multi-resolution Discrete Search to determine the Principal Diffusion Directions; 2) The parameter-free determination of the number of axon bundles using the Bayesian Information Criterion and 3) A Simultaneous Denoising and Fitting procedure to achieve robustness with respect to noise.

Thijs Dhollander¹, David Raffelt¹, Robert Elton Smith¹, and Alan Connelly^1,2
1The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia, ²The Florey Department of Neuroscience, University of Melbourne, Melbourne, Victoria, Australia

Diffusion weighted imaging (DWI) acquisitions typically suffer from a lower spatial resolution, compared to their T1 structural counterparts, but provide unique angular information. Researchers and clinical users may often find themselves switching back and forth between the "traditional" directionally-encoded colour (DEC) FA map and a T1 map to navigate anatomy, or try to overlay them using (partial) transparency. We propose a panchromatic sharpening approach tailored to (FOD-based) DEC and (T1) structural information to create a single fused image. The resulting contrast is striking and allows for easy identification of various anatomical structures beyond the resolution of the DWI data.

Silvia De Santis^1,2, Ben Jeurissen³, Derek K Jones¹, Yaniv Assaf⁴, and Alard Roebroeck^2
1CUBRIC Cardiff University, Cardiff, United Kingdom, ²Maastricht University, Maastricht, Netherlands, ³iMinds-Vision Lab, Dept. of Physics, University of Antwerp, Antwerp, Belgium, ⁴Tel Aviv University, Tel Aviv, Israel

The Inversion Recovery DTI technique was recently introduced to provide fibre-specific estimates of the relaxation time T1 and of the diffusion tensor in areas of crossing fibres, that characterise more than 90% of the human brain. Here, we demonstrate the feasibility of IR-DTI in vivo in the human brain, for the first time, and show that different fibre systems have distinct values of T1, reflecting their different myelination properties.