Abstract #1649

Deep Learning Based Reconstruction for Multi-shot DWI of the Breast: A Preliminary Study

Ning Chien¹, Cheng-Ya Yeh¹, Yi-Chen Chen¹, Yeun-Chung Chang², Chia-Wei Li³, 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, Deep learning reconstruction, Multi-shot DWIDiffusion-weighted imaging (DWI) in the breast is limited by image distortion, which can be improved with multi-shot DWI (MUSE). We conducted a pilot study to investigate the impact of deep-learning reconstruction (DLRecon) on MUSE image quality. Compared with the non-DL MUSE images, the MUSE DLRecon showed higher SNR without altering the mean ADC value. Moreover, the higher shots MUSE DL with reduced NEX could provide less-distortion DWI with comparable SNR and scan time to 2-shot MUSE imaging, which is commonly used in the clinical setting. Preliminary results indicate the feasibility of MUSE-DWI in the breast with higher number of shots.

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