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Abstract #3925

MRI protocol recommendation using deep metric learning

Mohamad Abdi1, Yu Zhao1, Sepehr Farhand1, Ke Zeng1, Mahesh Ranganath1, Yoshihisa Shinagawa1, and Gerardo Valadez Hermosillo1
1Siemens Healthineers, Malvern, PA, United States


MRI requires careful design of imaging protocols and parameters to optimally assess a particular region of the body and/or pathological process. Selection of acquisition parameters is a challenging task because (a) the relationship between the acquisition parameters and the image features is typically non-trivial, and (b) not all users have the leverage to optimize their imaging protocols. To help users overcome these challenges and elevate the user experience, a deep metric learning tool was developed as a recommendation system for automatic candidate generation of imaging protocols. The feasibility of the model is evaluated using 3-dimensional brain MR images.

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