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Abstract #0401

On the use of neural networks to fit high-dimensional microstructure models

João Pedro de Almeida Martins1,2, Markus Nilsson1, Björn Lampinen3, Marco Palombo4, Carl-Fredrik Westin5,6, and Filip Szczepankiewicz1,5,6
1Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden, 2Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway, 3Department of Clinical Sciences, Medical Radiation Physics, Lund University, Lund, Sweden, 4Centre for Medical Image Computing and Dept of Computer Science, University College London, London, United Kingdom, 5Radiology, Brigham and Women’s Hospital, Boston, MA, United States, 6Harvard Medical School, Boston, MA, United States

The application of function fitting neural networks in microstructural MRI has so far been restricted to lower-dimensional biophysical models. Moreover, the data sufficiency requirements of learning-based approaches remain unclear. Here, we use supervised learning to vastly accelerate the fitting of a high-dimensional relaxation-diffusion model of tissue microstructure and develop analysis tools for assessing the accuracy and sensitivity of model fitting networks. The developed learning-based fitting pipelines were tested on relaxation-diffusion data acquired with optimal and sub-optimal protocols. We found no evidence that machine-learning algorithms can correct for a degenerate fitting landscape or replace a careful design of the acquisition protocol.

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