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Abstract #2790

Deep Learning Reconstruction for Tailored Magnetic Resonance Fingerprinting

Amaresha Shridhar Konar1, Vineet Vinay Bhombore1, Imam Ahmed Shaik1, Seema Bhat1, Rajagopalan Sundaresan2, Sachin Jambawalikar3, Ramesh Venkatesan2, and Sairam Geethanath1,3

1MIRC, Dayananda Sagar Institutions, Bangalore, India, 2MRI, GE Healthcare, Bangalore, India, 3Radiology, Columbia University, New York, NY, United States

Magnetic Resonance Fingerprinting (MRF) is an accelerated acquisition and reconstruction method employed to generate multiple parametric maps. Tailored MRF (TMRF) coupled with deep learning based reconstruction has been proposed to overcome the shortcoming of T2 under estimation and the need for dictionaries respectively. A generalized approach with training of natural images and a specific approach with training of brain data are detailed in this work. Both approaches are demonstrated, compared and quantified.

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