Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04271-3_90 | MICCAI |
Keywords | Field | DocType |
unsupervised inline analysis,perfusion time series,perfusion mri,pixel-wise perfusion parameter map,clinical mr system,consecutive motion compensation,inline analysis,cardiac perfusion mr image,validation result,cardiac perfusion mri,unsupervised workflow,automated analysis,time series | Computer vision,Proton density,Computer science,Motion compensation,Electromagnetic coil,Artificial intelligence,Perfusion magnetic resonance imaging,Bias field,Perfusion | Conference |
Volume | Issue | ISSN |
12 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
8 | 0.62 | 9 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Xue | 1 | 11 | 1.41 |
| Sven Zuehlsdorff | 2 | 17 | 2.11 |
| Peter Kellman | 3 | 17 | 2.52 |
| Andrew Arai | 4 | 8 | 0.62 |
| Sonia Nielles-Vallespin | 5 | 8 | 0.62 |
| Christophe Chefdhotel | 6 | 34 | 4.17 |
| Christine H. Lorenz | 7 | 36 | 5.14 |
| Jens Guehring | 8 | 102 | 9.88 |