Abstract
Robotic radiosurgery uses the kinematic flexibility of a robotic arm to target tumors and lesions from many different directions. This approach allows to focus the dose to the target region while sparing healthy surrounding tissue. However, the flexibility in the placement of treatment beams is also a challenge during treatment planning. So far, a randomized beam generation heuristic has been proven to be most robust in clinical practice. Yet, for prevalent types of cancer similarities in patient anatomy and dose prescription exist. We propose a case-based method to solve the planning problem for a new patient by adapting beam sets from successful previous treatments. Preliminary experimental results indicate that the novel method could lead to faster treatment planning.
| Original language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 106-115 |
| Number of pages | 10 |
| Volume | 5651 LNAI |
| DOIs | |
| State | Published - 2009 |
| Externally published | Yes |
| Event | 12th Conference on Artificial Intelligence in Medicine, AIME 2009 - Verona, Italy Duration: Jul 18 2009 → Jul 22 2009 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5651 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 12th Conference on Artificial Intelligence in Medicine, AIME 2009 |
|---|---|
| Country | Italy |
| City | Verona |
| Period | 7/18/09 → 7/22/09 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Feasibility of case-based beam generation for robotic radiosurgery. / Schlaefer, Alexander; Dieterich, Sonja.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5651 LNAI 2009. p. 106-115 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5651 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Feasibility of case-based beam generation for robotic radiosurgery
AU - Schlaefer, Alexander
AU - Dieterich, Sonja
PY - 2009
Y1 - 2009
N2 - Robotic radiosurgery uses the kinematic flexibility of a robotic arm to target tumors and lesions from many different directions. This approach allows to focus the dose to the target region while sparing healthy surrounding tissue. However, the flexibility in the placement of treatment beams is also a challenge during treatment planning. So far, a randomized beam generation heuristic has been proven to be most robust in clinical practice. Yet, for prevalent types of cancer similarities in patient anatomy and dose prescription exist. We propose a case-based method to solve the planning problem for a new patient by adapting beam sets from successful previous treatments. Preliminary experimental results indicate that the novel method could lead to faster treatment planning.
AB - Robotic radiosurgery uses the kinematic flexibility of a robotic arm to target tumors and lesions from many different directions. This approach allows to focus the dose to the target region while sparing healthy surrounding tissue. However, the flexibility in the placement of treatment beams is also a challenge during treatment planning. So far, a randomized beam generation heuristic has been proven to be most robust in clinical practice. Yet, for prevalent types of cancer similarities in patient anatomy and dose prescription exist. We propose a case-based method to solve the planning problem for a new patient by adapting beam sets from successful previous treatments. Preliminary experimental results indicate that the novel method could lead to faster treatment planning.
UR - http://www.scopus.com/inward/record.url?scp=70350236905&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350236905&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02976-9_15
DO - 10.1007/978-3-642-02976-9_15
M3 - Conference contribution
AN - SCOPUS:70350236905
SN - 3642029752
SN - 9783642029752
VL - 5651 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 106
EP - 115
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -