Autoencoder-based Quality Assessment for Synthetic DiﬀusionMRI Data
Leon Weninger, Maxim Drobjazko, Chuh-Hyoun Na, Kerstin Jütten, Dorit Merhof
Rheinisch-Westfälische Technische Hochschule Aachen, Lehrstuhl für Bildverarbeitung
Diffusion MRI makes it possible to assess brain microstructure in-vivo. Recently, a variety of deep learning methods have been proposed that enhance the quality and utility of these acquisitions. For deep learning methods, a large amount of training data is necessary, but difficult to obtain. As a solution, different approaches to synthetic data creation have been published, but it is unclear which approach produces data that best matches the in-vivo characteristics. Here, a methodology to assess the quality of synthetic diffusion data which is based on de- noising autoencoders is proposed. For this, the reconstruction errors of autoencoders trained only on synthetic data were evaluated. The more the synthetic data resembles the real data, the lower the reconstruction error. Using this method, we evaluated which of four different synthetic data simulation techniques produced data that best resembled the in- vivo data. We find that modeling diffusion MRI data with patient- and scanner specific values leads to significantly better reconstruction results than using default diffusivity values, suggesting possible benefits of precision medicine approaches in diffusion MRI analysis.