Abstract #0720

Deep Learning Based Reconstruction for Multi-shot DWI of the Breast: Comparison of Quantitative ADC and Distortion

Ning Chien¹, Yi-Hsuan Cho¹, Yi-Chen Chen¹, Cheng-Ya Yeh¹, Yeun-Chung Chang², Chia-Wei Lee³, Chien-Yuan Lin³, Patricia Lan⁴, Xinzeng Wang⁵, Arnaud Guidon⁶, and Kao-Lang Liu¹

¹Department of Medical Imaging, National Taiwan University Cancer Center and National Taiwan University College of Medicine, Taipei, Taiwan, ²Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, ³GE Healthcare, Taipei, Taiwan, ⁴GE Healthcare, Menlo Park, CA, United States, ⁵GE Healthcare, Houston, TX, United States, ⁶GE Healthcare, Boston, MA, United States

Synopsis

Keywords: Breast, Machine Learning/Artificial Intelligence, Breast Imaging, Multiplexed Sensitivity Encoding (MUSE), Diffusion Weighted Imaging

Motivation: Diffusion-weighted imaging (DWI) in breast imaging is constrained by image distortion, which can be mitigated through the utilization of multi-shot DWI (MUSE).

Goal(s): We conducted a pilot study to investigate the impact of deep-learning reconstruction (DLRecon) on MUSE image quality.

Approach: Compared with the non-DL MUSE images, the MUSE DLRecon showed higher SNR without affecting the mean ADC value. Moreover, employing a higher shots in MUSE DL with reduced NEX could provide less-distortion DWI.

Results: Our preliminary results suggest the feasibility of MUSE-DWI in breast imaging with a higher number of shots.

Impact: Our results suggest that the DLRecon could be beneficial for the regions prone to distortion and requiring a high density of diffusion direction information, in the complex diffusion modeling, all while maintaining a feasible scan time in breast MUSE imaging.

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