title: Unsupervised inline analysis of cardiac perfusion MRI.
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.
authors.affiliation: Imaging and Visualization, Siemens Corporate Research, Princeton, NJ, USA. hui-xue@siemens.com
authors.forename: Hui
authors.initials: H
authors.lastname: Xue
authors.forename: Sven
authors.initials: S
authors.lastname: Zuehlsdorff
authors.forename: Peter
authors.initials: P
authors.lastname: Kellman
authors.forename: Andrew
authors.initials: A
authors.lastname: Arai
authors.forename: Sonia
authors.initials: S
authors.lastname: Nielles-Vallespin
authors.forename: Christophe
authors.initials: C
authors.lastname: Chefdhotel
authors.forename: Christine H
authors.initials: CH
authors.lastname: Lorenz
authors.forename: Jens
authors.initials: J
authors.lastname: Guehring
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity