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Abstract #0167

SUper-REsolution TRACTography (SURE-TRACT) pipeline using self-similarity between diffusional and anatomical images

Hong Hsi Lee1,2, Ying Chia Lin1,2, Gregory Lemberskiy1,2, Benjamin Ades-aron1,2, Steven Baete1,2, Fernando E Boada1,2, Els Fieremans1,2, and Dmitry S Novikov1,2

1Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, United States

Here, we propose a model-free, self-similarity based SUper-REsolution TRACTography (SURE-TRACT) pipeline to increase the resolution of diffusion weighted images (DWIs) by translating the high spatial frequency details from the co-registered high-resolution anatomical image of the same subject. The generated high-resolution DWIs enable to identify fiber tracks and estimate biophysical parameters with greater anatomical detail. Validating our pipeline using Human Connectome Project data, we showed that the SURE-TRACT pipeline resolves partial volume effects, and is more flexible to different acquisition protocols than other recent machine-learning based algorithms.

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