Sampling arterial input function (AIF) from peripheral arteries: Comparison of a temporospatial-feature based method against conventional manual method

It is often difficult to accurately localize small arteries in images of peripheral organs, and even more so with vascular abnormality vasculatures, including collateral arteries, in peripheral artery disease (PAD). This poses a challenge for manually sampling arterial input function (AIF) in quantifying dynamic contrast-enhanced (DCE) MRI data of peripheral organs. In this study, we designed a multi-step screening approach that utilizes both the temporal and spatial information of the dynamic images, and is presumably suitable for localizing small and unpredictable peripheral arteries. In 41 DCE MRI datasets acquired from human calf muscles, the proposed method took