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Abstract #4542

Reconstruction of Tailored Magnetic Resonance Fingerprinting Using Random Forest Approach

Shivaprasad Ashok Chikop1, Amaresh Shridhar Konar1,2, Vineet Vinay Bhombore1, Fabian Balsiger3, Rajagopalan Sundareshan4, shaik Imam4, Mauricio Antonio Reyes Aguirre3, Ramesh venkatesan4, and Sairam Geethanath1,5

1Dayananda Sagar Institutions, Bangalore, India, 2Memorial Sloan Kettering Cancer Center, New York, NY, United States, 3Institute of Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland, 4Wipro-GE, bangalore, India, 5Magnetic Resonance Research Program, Columbia University, New York, NY, United States

Magnetic Resonance Fingerprinting is a new acquisition/reconstruction technique to obtain multi-parametric map. Tailored MRF has demonstrated the quantification of longer T2 components contrary to classical MRF. The supervised learning based approach model in the study does not require construction of the dictionary. Leave out one approach has been utilized as the approach for modeling the random forest approach. The dictionary approach is heavy on the computation that limits the MRF to get into the clinic.

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