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DCE-MRI provides information about vascular permeability and tissue perfusionthrough the acquisition of pharmacokinetic parameters. However, traditionalmethods for estimating these pharmacokinetic parameters involve fitting tracerkinetic models, which often suffer from computational complexity and lowaccuracy due to noisy arterial input function (AIF) measurements. Although somedeep learning approaches have been proposed to tackle these challenges, mostexisting methods rely on supervised learning that requires paired input DCE-MRIand labeled pharmacokinetic parameter maps. This dependency on labeled dataintroduces significant time and resource constraints, as well as potentialnoise in the labels, making supervised learning methods often impractical. Toaddress these limitations, here we present a novel unpaired deep learningmethod for estimating both pharmacokinetic parameters and the AIF using aphysics-driven CycleGAN approach. Our proposed CycleGAN framework is designedbased on the underlying physics model, resulting in a simpler architecture witha single generator and discriminator pair. Crucially, our experimental resultsindicate that our method, which does not necessitate separate AIF measurements,produces more reliable pharmacokinetic parameters than other techniques.

Unpaired Deep Learning for Pharmacokinetic Parameter Estimation from Dynamic Contrast-Enhanced MRI