Unsupervised inline analysis of cardiac perfusion MRI.
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.
Xue
Hui
H
Imaging and Visualization, Siemens Corporate Research, Princeton, NJ, USA. hui-xue@siemens.com
Zuehlsdorff
Sven
S
Kellman
Peter
P
Arai
Andrew
A
Nielles-Vallespin
Sonia
S
Chefdhotel
Christophe
C
Lorenz
Christine H
CH
Guehring
Jens
J
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