Name
Abstract | ||
|---|---|---|
Static X-ray computed tomography (CT) volumes are often used as anatomic roadmaps during catheter-based cardiac interventions performed under X-ray fluoroscopy guidance. These CT volumes provide a high-resolution depiction of soft-tissue structures, but at only a single point within the cardiac and respiratory cycles. Augmenting these static CT roadmaps with segmented myocardial borders extracted from live ultrasound (US) provides intra-operative access to real-time dynamic information about the cardiac anatomy. In this work, using a customized segmentation method based on a 3D active mesh, endocardial borders of the left ventricle were extracted from US image streams (4D data sets) at a frame rate of approximately 5 frames per second. The coordinate systems for CT and US modalities were registered using rigid body registration based on manually selected landmarks, and the segmented endocardial surfaces were overlaid onto the CT volume. The root-mean squared fiducial registration error was 3.80 mm. The accuracy of the segmentation was quantitatively evaluated in phantom and human volunteer studies via comparison with manual tracings on 9 randomly selected frames using a finite-element model (the US image resolutions of the phantom and volunteer data were 1.3 x 1.1 x 1.3 mm and 0.70 x 0.82 x 0.77 mm, respectively). This comparison yielded 3.70 +/- 2.5 mm (approximately 3 pixels) root-mean squared error (RMSE) in a phantom study and 2.58 +/- 1.58 mm (approximately 3 pixels) RMSE in a clinical study. The combination of static anatomical roadmap volumes and dynamic intra-operative anatomic information will enable better guidance and feedback for image-guided minimally invasive cardiac interventions. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1117/12.711431 | Proceedings of SPIE |
Keywords | Field | DocType |
abdominal procedures,cardiac procedures,multimodality display,registration,segmentation and rendering,ultrasound guidance | Computer vision,Fiducial marker,Data set,Segmentation,Computer science,Imaging phantom,Fluoroscopy,Artificial intelligence,Frame rate,Pixel,Image resolution | Conference |
Volume | ISSN | Citations |
6509 | 0277-786X | 3 |
PageRank | References | Authors |
0.44 | 14 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Duan | 1 | 80 | 7.58 |
| G. Shechter | 2 | 143 | 16.23 |
| Luis F. Gutiérrez | 3 | 15 | 4.10 |
| Douglas Stanton | 4 | 32 | 5.07 |
| Lyubomir Zagorchev | 5 | 66 | 7.98 |
| Andrew F. Laine | 6 | 747 | 83.01 |
| Daniel R. Elgort | 7 | 3 | 0.44 |