Name
Abstract | ||
|---|---|---|
Magnetic tissue tagging is becoming a very viable non-invasive tool in the assessment of the myocardium. Landmarks are introduced on the reconstructed MR heart image, which are otherwise missing in the cardiac anatomy. Quantification of the myocardial function relies on the accuracy of the tag detection and tracking algorithms. Perfecting the tagging process simplifies the detection and tracking procedure and results in a more reliable assessment of the myocardium. This study investigates and attempts to control the factors that govern the persistence, contrast-to-noise ratio and morphological properties of magnetic tissue tagging by optimizing an imaging sequence with a perfected group of tags. To accomplish this goal, an algorithm has been developed to optimize the RF excitation pulse associated with the tagging sequence. This algorithm takes into account the time duration of the RF pulse, the number of tags and the width of each tag, as well as the center-to-center distance between the tags. The flip angle on the tagged region, the gradient properties of the tag profile line, the contrast-to-noise properties and the persistence of the tag lines over the consecutive frames are also examined |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/CBMS.1998.701326 | Lubbock, TX |
Keywords | Field | DocType |
biomedical NMR,cardiology,image reconstruction,image sequences,medical image processing,muscle,optimisation,RF excitation pulse optimization,algorithm accuracy,center-to-center distance,consecutive frames,contrast-to-noise ratio,echo-planar imaging,flip angle,imaging sequence optimization,magnetic tissue tagging sequence,morphological properties,myocardial function quantification,perfected tag group,pulse time duration,tag detection algorithm,tag line persistence,tag profile line gradient properties,tag tracking algorithm,tag width | Iterative reconstruction,Computer vision,Medical imaging,Echo-planar imaging,Computer science,Flip angle,Radio frequency,Pulse (signal processing),Artificial intelligence,Image resolution,Contrast-to-noise ratio | Conference |
ISSN | ISBN | Citations |
1063-7125 | 0-8186-8564-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faruq A. Al-Omari | 1 | 42 | 5.07 |
| Michael Chwialkowski | 2 | 0 | 0.34 |
| Michael Clarke | 3 | 0 | 0.34 |
| Ronald Peshock | 4 | 1 | 0.87 |