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Abstract #0856

Towards taking the guesswork (and the errors) out of diffusion tractography

Anastasia Yendiki1, Robert Jones2, Adrian Dalca1,3, Hui Wang1, and Bruce Fischl1
1Radiology, Harvard Medical School & Massachusetts General Hospital, Charlestown, MA, United States, 2Radiology, Massachusetts General Hospital, Charlestown, MA, United States, 3Massachusetts Institute of Technology, Cambridge, MA, United States

Orientation distributions estimated from diffusion MRI have multiple peaks per voxel. Tractography algorithms must choose among them arbitrarily, leading to errors. We propose a novel approach to making this choice in a manner informed by the data. We use post mortem optical and MRI data to train a convolutional neural network that can recognize voxel-wise connection patterns directly from diffusion data, circumventing the conventional paradigm of an orientation distribution. We introduce TRACARIS (TRACt Architectures Recovered from Imaging Signals), a tractography algorithm that uses these network-predicted, local connection patterns. We present preliminary validation results from a post mortem human brain sample.

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