Unsupervised Inline Analysis of Cardiac Perfusion MRI

Authors:
Hui Xue;Sven Zuehlsdorff;Peter Kellman;Andrew Arai;Sonia Nielles-Vallespin;Christophe Chefdhotel;Christine H. Lorenz;Jens Guehring
Affiliations:
Imaging and Visualization, Siemens Corporate Research, Princeton, USA;CMR R&D, Siemens Medical Solutions USA, Inc., Chicago, USA;Laboratory of Cardiac Energetics, National Institutes of Health, Bethesda, USA;Laboratory of Cardiac Energetics, National Institutes of Health, Bethesda, USA;MED MR PLM AW Cardiology, Siemens AG Healthcare Sector, Erlangen, Germany;Imaging and Visualization, Siemens Corporate Research, Princeton, USA;Imaging and Visualization, Siemens Corporate Research, Princeton, USA;Imaging and Visualization, Siemens Corporate Research, Princeton, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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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.