  DCE-MRI provides information about vascular permeability and tissue perfusion
through the acquisition of pharmacokinetic parameters. However, traditional
methods for estimating these pharmacokinetic parameters involve fitting tracer
kinetic models, which often suffer from computational complexity and low
accuracy due to noisy arterial input function (AIF) measurements. Although some
deep learning approaches have been proposed to tackle these challenges, most
existing methods rely on supervised learning that requires paired input DCE-MRI
and labeled pharmacokinetic parameter maps. This dependency on labeled data
introduces significant time and resource constraints, as well as potential
noise in the labels, making supervised learning methods often impractical. To
address these limitations, here we present a novel unpaired deep learning
method for estimating both pharmacokinetic parameters and the AIF using a
physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed
based on the underlying physics model, resulting in a simpler architecture with
a single generator and discriminator pair. Crucially, our experimental results
indicate that our method, which does not necessitate separate AIF measurements,
produces more reliable pharmacokinetic parameters than other techniques.
