Name
Abstract | ||
|---|---|---|
In clinical MRI examinations. the geometry of diagnostic scans is defined in an initial planning phase. The operator plans the scan volumes (off-centre, angulation, field-of-view) with respect to patient anatomy in scout' images. Often multiple plans are required within a single examination, distracting attention from the patient waiting in the scanner. A novel and robust method is described for automated planning of neurological MRI scans, capable of handling strong shape deviations from healthy anatomy. The expert knowledge required to position scan geometries is learned from previous example plans, allowing site-specific styles to be readily taken into account. The proposed method first fits an anatomical model to the scout data, and then new scan geometries are positioned with respect to extracted landmarks. The accuracy of landmark extraction was measured to be comparable to the inter-observer variability, and automated plans are shown to be highly consistent with those created by expert operators using clinical data. The results of the presented evaluation demonstrate the robustness and applicability of the proposed approach, which has the potential to significantly improve clinical workflow. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1117/12.652160 | Proceedings of SPIE |
Keywords | Field | DocType |
MRI,image acquisition,scan planning,shape modeling,robust learning | Computer vision,Computer science,Robust learning,Scanner,Artificial intelligence,Landmark,Magnetic resonance imaging | Conference |
Volume | ISSN | Citations |
6144 | 0277-786X | 4 |
PageRank | References | Authors |
1.25 | 2 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stewart Young | 1 | 30 | 6.74 |
| Daniel Bystrov | 2 | 62 | 8.62 |
| Thomas Netsch | 3 | 93 | 16.47 |
| rene c bergmans | 4 | 4 | 1.25 |
| Arianne Van Muiswinkel | 5 | 40 | 6.88 |
| fredy visser | 6 | 4 | 1.25 |
| rudolf sprigorum | 7 | 4 | 1.25 |
| jurgen gieseke | 8 | 4 | 1.25 |