Abstract #3661

3D deep-learning image reconstruction for fast spin-echo triple-echo Dixon images acquired with flexible echo-spacing (FTED-Flex)

Jong Bum Son¹, Huong T. C. Le-Petross², David E. Rauch¹, Zhan Xu¹, Tanya W. Moseley^2,3, Beatriz E. Adrada², and Jingfei Ma¹

¹Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ²Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ³Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: Fat & Fat/Water Separation, Fat, Deep Learning, 3D Convolutional Neural Network

Motivation: The fast spin-echo triple-echo Dixon images acquired with flexible echo-spacing (FTED-Flex) can be used to generate separated water and fat images with enhanced T2-weighted contrast. However, their performance and clinical applications are limited by its long image reconstruction time.

Goal(s): Our goal is to develop a fast and accurate FTED-Flex image reconstruction method.

Approach: The time-consuming phase estimation was replaced by a 3D deep-learning neural network.

Results: The FTED-Flex integrated with a 3D deep-learning network was highly accurate (average Dice coefficient in volume-of-interest=0.989) and reduced the processing time for phase-estimation to a few seconds, compared to tens of minutes by conventional methods.

Impact: The developed FTED-Flex integrated with a 3D deep-learning network is highly accurate and reduces the processing time for phase-estimation to a few seconds, thus it has a great potential to expand clinical applications of FTED-Flex imaging.

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