Unsupervised PK Model-free BBB Leakage Detection in DCE-MRI using Generative Adversarial Networks

Motivation: Arterial input function (AIF) in DCE-MRI is often degraded due to noise, motion, and partial volume. This may lower the overall reliability of the resulting pharmacokinetic (PK) parameters. Goal(s): Our goal was to develop a robust, fast method for detecting blood-brain barrier (BBB) leakage signals without PK models. Approach: We employed a fast anomaly detection using generative adversarial networks (f-AnoGAN) for unsupervised detection of the leakage signals. Results: The results were highly correlated with the traditional Ktrans maps, and more robust against reduced temporal data points, which may be used for shorter scan time and/or higher spatial resolution. Impact: Our proposed method may allow fast and robust detection of BBB leakage signals in the case where the scan time is highly limited, and consequently, the traditional approach with PK models may not be suitable.