Back to Search
Start Over
Divided-volume matching technique for volume displacement estimation of patient positioning in radiation therapy
- Source :
- Physica Medica. 62:1-12
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Purpose We propose the Divided-Volume Matching (DVM) technique to visualize and estimate three-dimensional (3D) displacements of internal structures to enable more accurate patient positioning for radiation therapy. Methods A CT volume is divided into a volume of interest (VOI) and a base volume (BV); 2D-3D matching is achieved using digital radiography (DR) images and digitally reconstructed radiographs (DRRs), where the DRRs are iteratively generated by changing the 3D positions and rotation angles of the separate volumes independently to identify the best match with the DR images. We demonstrate this technique with two phantom and two clinical cases. Results 3D displacements of the VOIs could be estimated independently and simultaneously with those of the BVs, with accuracies comparable to those of the conventional 2D-3D matching. The proposed technique yielded more suitable matching results when internal displacements occurred in the regions of interest (ROIs). The best matches were found when the ROI was confined to the focused structure, initial displacement values were coarsely adjusted, one volume was matched while the other was fixed, or any combination thereof. Conclusions The proposed technique can be used effectively for independent displacement estimations of VOIs and BVs for patient positioning in radiation therapy.
- Subjects :
- Matching (statistics)
Computer science
medicine.medical_treatment
Biophysics
General Physics and Astronomy
Patient positioning
Patient Positioning
Imaging phantom
Displacement (vector)
030218 nuclear medicine & medical imaging
03 medical and health sciences
Imaging, Three-Dimensional
0302 clinical medicine
Neoplasms
medicine
Humans
Radiology, Nuclear Medicine and imaging
Computer vision
Digital radiography
Phantoms, Imaging
business.industry
Radiotherapy Planning, Computer-Assisted
General Medicine
Radiation therapy
030220 oncology & carcinogenesis
Artificial intelligence
Tomography, X-Ray Computed
business
Rotation (mathematics)
Volume (compression)
Subjects
Details
- ISSN :
- 11201797
- Volume :
- 62
- Database :
- OpenAIRE
- Journal :
- Physica Medica
- Accession number :
- edsair.doi.dedup.....aa125cea5ea39cb7b334890e551d4940
- Full Text :
- https://doi.org/10.1016/j.ejmp.2019.04.028