Automatic Detection of Arterial Voxels in Dynamic Contrast-Enhanced MR Images of the Brain

Arterial input function (AIF) is important for the determination of cerebral blood flow and the analysis of related disease. Detection of artery voxels in dynamic contrast-enhanced (DCE) MRI is the key challenge in estimating the AIF. In the presence of tumour tissue, automatic detection of arteries becomes as even more challenging task. In this paper we propose a supervised machine-learning based method for the detection of artery voxels in DCE-MRI of the brain. The method utilises a set of kinetic and local structural features with a logistic regression classifier in order to detect arterial voxels in the image. The performance of the method is evaluated on 11 DCE-MRI datasets, of patients with diagnosed brain cancer, in terms of area under the ROC curve and in terms of correlation with an ideal AIF. The results of the evaluation suggest that the proposed method has the potential to be used as a tool for accurate estimation of AIF in DCE-MRI of the brain.