Self-contained comprehensive quantification of dynamic contrast-enhanced MRI using physics+kinetics-based network learning (PKNet)

This work develops a deep learning technique that is trained according to physics and kinetics for self-contained comprehensive quantification of dynamic contrast-enhanced MRI. In addition to perfusion parameters, patient-specific parameters that affect the quantification are estimated, including bolus arrival time, T1, steady state magnetization, and AIF. The DCE-MRI quantification network was tested on a patient with cervical cancer and demonstrated high concordance between two scans separated by 24 hours. Physics plus kinetics informed network learning (PKNet) enabled the quantification of multiple parameters which has the potential to increase reproducibility of quantitative DCE-MRI, a long-desired goal.