While Positron Emission Tomography (PET) used jointly with Magnetic Resonance (MR) Imaging shows promise in breast imaging, unique constraints require novel solutions to achieve attenuation-corrected images. We propose an algorithm for producing a linear attenuation coefficient map and truncation completion created from MR images using deep learning.
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