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Abstract #4805

Prediction of new diffusion MRI data is feasible using robust machine learning algorithms for multi-shell HARDI in a clinical setting

Cayden Murray1, Olayinka Oladosu1, and Yunyan Zhang 2,3
1Neuroscience, University of Calgary, Calgary, AB, Canada, 2Radiology, University of Calgary, Calgary, AB, Canada, 3Clinical Neurosciences, University of Calgary, Calgary, AB, Canada


High Angular Resolution Diffusion Imaging (HARDI) is a promising method for the analysis of microstructural changes. However, HARDI acquisition is time-consuming and therefore impractical in clinical settings. We developed 2 neural networks for predicting non-acquired diffusion datasets based on diffusion MRI: Multi-layer Perceptron (MLP) and Convolutional Neural Network (CNN). Through systemic training and evaluation with healthy public data and local MS patient MRI, we found that both the MLP and CNN models could predict high b-value from low b-value data that allowed the assessment of Neurite Orientation Dispersion and Density Imaging (NODDI) outcomes. Neural networks can make NODDI clinically viable.

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