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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