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In Magnetic resonance in medicine ; h5-index 66.0

PURPOSE : To develop a deep learning-based reconstruction framework for ultrafast and robust diffusion tensor imaging and fiber tractography.

METHODS : SuperDTI was developed to learn the nonlinear relationship between DWIs and the corresponding diffusion tensor parameter maps. It bypasses the tensor fitting procedure, which is highly susceptible to noises and motions in DWIs. The network was trained and tested using data sets from the Human Connectome Project and patients with ischemic stroke. Results from SuperDTI were compared against widely used methods for tensor parameter estimation and fiber tracking.

RESULTS : Using training and testing data acquired using the same protocol and scanner, SuperDTI was shown to generate fractional anisotropy and mean diffusivity maps, as well as fiber tractography, from as few as six raw DWIs, with a quantification error of less than 5% in all white-matter and gray-matter regions of interest. It was robust to noises and motions in the testing data. Furthermore, the network trained using healthy volunteer data showed no apparent reduction in lesion detectability when directly applied to stroke patient data.

CONCLUSIONS : Our results demonstrate the feasibility of superfast DTI and fiber tractography using deep learning with as few as six DWIs directly, bypassing tensor fitting. Such a significant reduction in scan time may allow the inclusion of DTI into the clinical routine for many potential applications.

Li Hongyu, Liang Zifei, Zhang Chaoyi, Liu Ruiying, Li Jing, Zhang Weihong, Liang Dong, Shen Bowen, Zhang Xiaoliang, Ge Yulin, Zhang Jiangyang, Ying Leslie


convolutional neural network, deep learning, diffusion tensor imaging (DTI), fast DTI, fiber tractography, lesion detectability