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Time-resolved cardiac interventional cone-beam CT reconstruction from fully truncated projections using the prior image constrained compressed sensing (PICCS) algorithm
- Source :
- Physics in Medicine and Biology. 57:2461-2476
- Publication Year :
- 2012
- Publisher :
- IOP Publishing, 2012.
-
Abstract
- C-arm cone-beam CT could replace preoperative multi-detector CT scans in the cardiac interventional setting. However, cardiac gating results in view angle undersampling and the small size of the detector results in projection data truncation. These problems are incompatible with conventional tomographic reconstruction algorithms. In this paper, the prior image constrained compressed sensing (PICCS) reconstruction method was adapted to solve these issues. The performance of the proposed method was compared to that of FDK, FDK with extrapolated projection data (E-FDK), and total variation-based compressed sensing. A canine projection dataset acquired using a clinical C-arm imaging system supplied realistic cardiac motion and anatomy for this evaluation. Three different levels of truncation were simulated. The relative root mean squared error and the universal image quality index were used to quantify the reconstruction accuracy. Three main conclusions were reached. (1) The adapted version of the PICCS algorithm offered the highest image quality and reconstruction accuracy. (2) No meaningful variation in performance was observed when the amount of truncation was changed. (3) This study showed evidence that accurate interior tomography with an undersampled acquisition is possible for realistic objects if a prior image with minimal artifacts is available.
- Subjects :
- Cone beam computed tomography
Time Factors
Tomographic reconstruction
Radiological and Ultrasound Technology
Computer science
business.industry
Image quality
Heart
Image processing
Cone-Beam Computed Tomography
Article
Dogs
Compressed sensing
Image Processing, Computer-Assisted
Animals
Radiology, Nuclear Medicine and imaging
Computer vision
Tomography
Artificial intelligence
Truncation (statistics)
Projection (set theory)
business
Algorithm
Algorithms
Subjects
Details
- ISSN :
- 13616560 and 00319155
- Volume :
- 57
- Database :
- OpenAIRE
- Journal :
- Physics in Medicine and Biology
- Accession number :
- edsair.doi.dedup.....abf8eda246c94e94a9a23b2f324be1dd