A frequency-domain machine learning method is presented that significantly reduces the bias and variance in dual-calibrated estimation of oxygen extraction fraction, as demonstrated with simulation and in-vivo imaging. In addition, the method substantially reduces the processing time compared to previous robust analysis methods.
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