Abstract #4542

Reconstruction of Tailored Magnetic Resonance Fingerprinting Using Random Forest Approach

Shivaprasad Ashok Chikop¹, Amaresh Shridhar Konar^1,2, Vineet Vinay Bhombore¹, Fabian Balsiger³, Rajagopalan Sundareshan⁴, shaik Imam⁴, Mauricio Antonio Reyes Aguirre³, Ramesh venkatesan⁴, and Sairam Geethanath^1,5

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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