Bayesian optimization of IMPULSED diffusion MRI protocols produces designs with superior cell classification accuracy and lower parameter estimation error than heuristic baselines in simulations and canine tumor scans at 3T.
Quantitative parameters are obtained by fitting biophysical models to the acquired signals, providing imaging biomarkers about cell microenvironment(3-6)
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Optimizing IMPULSED Acquisition Protocols for Clinical 3T Scanners Through Bayesian Experimental Design
Bayesian optimization of IMPULSED diffusion MRI protocols produces designs with superior cell classification accuracy and lower parameter estimation error than heuristic baselines in simulations and canine tumor scans at 3T.