Name
Abstract | ||
|---|---|---|
Acquisition geometries for tomographic reconstruction are usually densely sampled in order to keep the underlying linear system used in iterative reconstruction as well-posed as possible. While this objective is easily enforced in imaging systems with gantries, this issue is more critical for intra-operative setups using freehand-guided data sensing. This paper investigates an incremental method to monitor the numerical condition of the system based on the singular value decomposition of the system matrix, and presents an approach to find optimal detector positions via a randomized optimization scheme. The feasibility of this approach is demonstrated using simulations of an intra-operative functional imaging setup and actual robot-controlled phantom experiments. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33454-2_6 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Iterative reconstruction,Singular value decomposition,Computer vision,Tomographic reconstruction,Linear system,System matrix,Computer science,Imaging phantom,Artificial intelligence,Detector | Conference | 7512 |
Issue | ISSN | Citations |
Pt 3 | 0302-9743 | 3 |
PageRank | References | Authors |
0.63 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jakob Vogel | 1 | 76 | 8.85 |
| Tobias Reichl | 2 | 103 | 9.78 |
| José Gardiazabal | 3 | 50 | 6.08 |
| Nassir Navab | 4 | 6594 | 578.60 |
| Tobias Lasser | 5 | 97 | 16.81 |