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Abstract #2110

Acquisition of Ktrans perfusion parameter maps from DCE-MRI using a deep learning approach

Daohui Zeng1,2, Mu Du3, Yubao Liu3, Bingyu Yao1, Junhui Huang1, Long Yang1, Xuanle Li1, Ye Li1,4,5, Dong Liang1,4,5, Xin Liu1,4,5, Hairong Zheng1,4,5, Zhanli Hu1,4,5, and Na Zhang1,4,5
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, China, 3Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China, 4Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China, 5United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China

Synopsis

Keywords: Analysis/Processing, Breast

Motivation: Breast cancer has become the leading cancer worldwide. Hemodynamic features obtained from breast DCE-MRI perfusion maps can accurately quantify tumor pathophysiology. However, traditional estimation of perfusion parameter maps requires significant computational resources and time.

Goal(s): To investigate whether deep learning techniques can synthesize Ktrans perfusion parameter maps from contrast-enhanced MRI.

Approach: A pix2pix-based cGAN architecture was proposed to generate breast Ktrans perfusion maps.

Results: The Ktrans values of the tumor regions in the synthetic and real Ktrans maps show a strong correlation. Two experienced radiologists could not distinguish between real and synthetic Ktrans maps.

Impact: This study presents a novel feasible approach for synthesizing Ktrans perfusion maps, which enables rapid generation of high-quality and low-noise perfusion maps, thereby facilitating more effective application of these maps in clinical practice by physicians.

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Abstract

Keywords