Abstract #0679

Accelerating Acquisition for the Reconstruction of Fiber Orientation Distribution Function Using Convolutional Neural Network

Ting Gong¹, Hongjian He¹, Zhichao Lin², Zhiwei Li², Feng Yu², and Jianhui Zhong^1,3

¹Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China, ²Department of Instrument Science & Technology, Zhejiang University, Hangzhou, China, ³Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

Fiber orientation distribution function (fODF) is one of the key components for establishing brain connectivity maps. However, its reliable reconstruction usually requires a large number of diffusion weighted image (DWI) volumes leading to long acquisition time. Our previous study has shown the potential of multi-layer perceptron in recovering fODF directly from a small number of DWIs. In this study, we proposed a 3-dimentional convolution neural network to take the spatial correlation into consideration, allowing robust fODF reconstruction with up to eleven-fold reduction of number of DWIs. This method offers a new approach for fast fODF reconstruction which could facilitate its clinical applications.

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