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Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT
- Source :
- Physics in Medicine and Biology. 55:6575-6599
- Publication Year :
- 2010
- Publisher :
- IOP Publishing, 2010.
-
Abstract
- Flat-panel-detector X-ray cone-beam computed tomography (CBCT) is used in a rapidly increasing host of imaging applications, including image-guided surgery and radiotherapy. The purpose of the work is to investigate and evaluate image reconstruction from data collected at projection views significantly fewer than what is used in current CBCT imaging. Specifically, we carried out imaging experiments by use of a bench-top CBCT system that was designed to mimic imaging conditions in image-guided surgery and radiotherapy; we applied an image reconstruction algorithm based on constrained total-variation (TV)-minimization to data acquired with sparsely sampled view-angles; and we conducted extensive evaluation of algorithm performance. Results of the evaluation studies demonstrate that, depending upon scanning conditions and imaging tasks, algorithms based on constrained TV-minimization can reconstruct images of potential utility from a small fraction of the data used in typical, current CBCT applications. A practical implication of the study is that the optimization of algorithm design and implementation can be exploited for considerably reducing imaging effort and radiation dose in CBCT.
- Subjects :
- medicine.medical_specialty
Cone beam computed tomography
Computer science
medicine.medical_treatment
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
Computed tomography
Iterative reconstruction
Article
Flat panel detector
Image Processing, Computer-Assisted
medicine
Humans
Radiology, Nuclear Medicine and imaging
Computer vision
Medical physics
Projection (set theory)
Radiological and Ultrasound Technology
medicine.diagnostic_test
Phantoms, Imaging
business.industry
Radiation dose
Cone-Beam Computed Tomography
Radiation therapy
Algorithm design
Artificial intelligence
Minification
Tomography
business
Head
Algorithms
Subjects
Details
- ISSN :
- 13616560 and 00319155
- Volume :
- 55
- Database :
- OpenAIRE
- Journal :
- Physics in Medicine and Biology
- Accession number :
- edsair.doi.dedup.....b9e42f8f1e717fecffc6491796190413