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Abstract #1914

Vastly accelerated linear least squares fitting with numerical optimization for dual delay compensated quantitative liver perfusion mapping

Ramin Jafari1, Yi Wang1, Martin R. Prince2, and Pascal Spincemaille2

1Cornell University, Ithaca, NY, United States, 2Weill Cornell Medicine, New York, NY, United States

Accurate liver perfusion quantification requires correction for dual arterial and portal venous input delays, but such dual delay correction in current nonlinear perfusion methods is computationally too expensive to apply in perfusion mapping. We realize that the kinetic equation is a linear differential equation that would allow fast linear processing. Accordingly, we propose to use linear least squares (LLS) fitting to this kinetic equation with fast conjugate gradient search for processing dynamic contrast enhanced MRI data. Our proposed LLS vastly (~300 times) accelerate computation in perfusion quantification, enabling for the first time accurate liver perfusion mapping with dual delay corrections.

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