Abstract #1858

A Convolutional Neural Network for Accelerating the Computation of the Extended Tofts Model in DCE-MRI

Ke Fang¹, Zejun Wang^2,3, Zhaoqing Li^2,3, Bao Wang⁴, Guangxu Han^2,3, Zhaowei Cheng¹, Zhihong Chen¹, Chuanjin Lan⁵, Yi Zhang⁶, Peng Zhao⁷, Xinyu Jin¹, Yingchao Liu⁸, and Ruiliang Bai^2,3

¹College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, China, ²Department of Physical Medicine and Rehabilitation of The Affiliated Sir Run Run Shaw Hospital AND Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China, ³Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, ⁴Department of Radiology, Qilu Hospital of Shandong University, Jinan, China, ⁵School of Medicine, Shandong University, Jinan, China, ⁶Shandong Medical Imaging Research Institute, Shandong University, Jinan, China, ⁷Department of Neurosurgery, Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China, ⁸Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China

We proposed a customized conventional neural network (CNN) to fasten the computation time of non-linear pharmacokinetic models in DCE-MRI. The results demonstrated that the CNN could shorten the computation time of extended Tofts model of whole-brain data to less than a minute without sacrificing the agreements with conventional non-linear least square (NLLS) fitting. This CNN could serve as an alternative to conventional NLLS fitting for fast assessment of pharmacokinetic parameters in clinical practice.

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