Data-driven selection of motion correction techniques in breast DCE-MRI

It is well known that some sort of motion correction technique (MCT) should be performed before DCE-MRI data analysis in order to reduce movement artefacts. However, it is not clear if a single MCT can produce optimum results for every single examination, since for example different movements can occur. In this paper we investigated the possibility of choosing the best MCT per each specific patient, before performing further data analysis (e.g. tumour segmentation). In particular, our aim is the proposal of some physiological model-based quality indexes (QIs) for ranking different MCT on a patient basis. Moreover, for practical feasibility, we investigated the performance of our proposal when only a small fraction of the available data was used. We performed tests on a dataset of patients with breast tumour. Specifically, for each patient we compared the “reference ranking” of different MCT obtained by using the results of tumour segmentation with the rankings produced with each QI. Our results indicate that the ranking obtained by using the QI based on the Extended Tofts-Kermode model (with the Parker arterial input function) are in accordance with the “reference ranking”. Moreover, computational load can be significantly reduced without affecting the overall performance by using only 5% of the available data.