Abstract #1333

A Model-driven Deep Learning Method Based on Sparse Coding to Accelerate IVIM Imaging in Fetal Brain

Tianshu Zheng¹, Cong Sun², Guangbin Wang², Weihao Zheng¹, Wen Shi¹, Yi Sun³, Yi Zhang¹, Chuyang Ye⁴, and Dan Wu¹

¹Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China,, Zhengjiang University, Hangzhou, China, ²Department of Radiology, Shandong Medical Imaging Research Institute, Cheeloo College of Medicine, Shandong University, 324, Jingwu Road, Jinan, Shandong, 250021, People's Republic of China, Shandong University, Jinan, China, ³Department of Radiology 2MR Collaboration, Siemens Healthcare China, Shanghai, China, Siemens Healthcare China, Shanghai, China, ⁴chool of Information and Electronics, Beijing Institute of Technology, Beijing Institute of Technology, Beijing, China

Intravoxel incoherent motion (IVIM) can be used to assess microcirculation in the brain, however, conventional IVIM requires long acquisition to obtain multiple b-values, which is challenging for fetal brain MRI due to excessive motion. Q-space learning helps to accelerate the acquisition but it is hard to be interpreted. In this study, we proposed a sparsity coding deep neural network (SC-DNN), which is a model-driven network based on sparse representation and unfold the parameter optimization process. Compared to conventional IVIM fitting, SC-DNN took only 50% of the data to reach the comparable accuracy for parameter estimation, which outperformed the multilayer perceptron.

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