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Abstract #3997

Application of Random Forest Regression for Fast and Robust MRF Dictionary Matching

Shivaprasad Ashok Chikop1, Vimal Chandran2, Imam Shaik1, Mauricio Antonio Reyes Aguirre2, and Sairam Geethanath1

1Medical Imaging Research Centre, Dayananda Sagar Institutions, Bangalore, India, 2Institute of Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland

Magnetic Resonance Fingerprinting (MRF) provides for simultaneous generation of MR multi-parametric maps from a single acquisition. In this work, a machine learning based regression method that does not require a dictionary has been demonstrated. A leave-one-out evaluation strategy was employed for numerical evaluation of the proposed MRF-RF approach. A comparative study was performed on two previously employed matching methods. Results depict that proposed MRF-RF method produces maps similar to the vector dot product approach, with a 10-fold saving in time. The method can also be extended to other non-linear maps such as B0 inhomogeneity, diffusion maps, and perfusion maps.

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