Abstract
In this paper we first discuss the technical challenges preventing an automated analysis of cardiac perfusion MR images and subsequently present a fully unsupervised workflow to address the problems. The proposed solution consists of key-frame detection, consecutive motion compensation, surface coil inhomogeneity correction using proton density images and robust generation of pixel-wise perfusion parameter maps. The entire processing chain has been implemented on clinical MR systems to achieve unsupervised inline analysis of perfusion MRI. Validation results are reported for 260 perfusion time series, demonstrating feasibility of the approach. © 2009 Springer-Verlag.
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CITATION STYLE
Xue, H., Zuehlsdorff, S., Kellman, P., Arai, A., Nielles-Vallespin, S., Chefdhotel, C., … Guehring, J. (2009). Unsupervised inline analysis of cardiac perfusion MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5762 LNCS, pp. 741–749). https://doi.org/10.1007/978-3-642-04271-3_90
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