scholarly journals Suppression of Cone-Beam Artefacts with Direct Iterative Reconstruction Computed Tomography Trajectories (DIRECTT)

2021 ◽  
Vol 7 (8) ◽  
pp. 147
Author(s):  
Sotirios Magkos ◽  
Andreas Kupsch ◽  
Giovanni Bruno
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Iterative Reconstruction ◽  
Cone Beam ◽  
Back Projection ◽  
Projection Algorithms ◽  
Filtered Back Projection ◽  
Tomography Data

The reconstruction of cone-beam computed tomography data using filtered back-projection algorithms unavoidably results in severe artefacts. We describe how the Direct Iterative Reconstruction of Computed Tomography Trajectories (DIRECTT) algorithm can be combined with a model of the artefacts for the reconstruction of such data. The implementation of DIRECTT results in reconstructed volumes of superior quality compared to the conventional algorithms.

scholarly journals Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT

2019 ◽  
Vol 18 ◽  
pp. 153303381882305 ◽  
Author(s):  
Weihua Mao ◽  
Chang Liu ◽  
Stephen J. Gardner ◽  
Farzan Siddiqui ◽  
Karen C. Snyder ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Quality Assurance ◽  
Image Quality ◽  
Cone Beam Computed Tomography ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Tomography Reconstruction ◽  
Computed Tomography Reconstruction

Purpose: We have quantitatively evaluated the image quality of a new commercially available iterative cone-beam computed tomography reconstruction algorithm over standard cone-beam computed tomography image reconstruction results. Methods: This iterative cone-beam computed tomography reconstruction pipeline uses a finite element solver (AcurosCTS)-based scatter correction and a statistical (iterative) reconstruction in addition to a standard kernel-based correction followed by filtered back-projection-based Feldkamp-Davis-Kress cone-beam computed tomography reconstruction. Standard full-fan half-rotation Head, half-fan full-rotation Head, and standard Pelvis cone-beam computed tomography protocols have been investigated to scan a quality assurance phantom via the following image quality metrics: uniformity, HU constancy, spatial resolution, low contrast detection, noise level, and contrast-to-noise ratio. An anthropomorphic head phantom was scanned for verification of noise reduction. Clinical patient image data sets for 5 head/neck patients and 5 prostate patients were qualitatively evaluated. Results: Quality assurance phantom study results showed that relative to filtered back-projection-based cone-beam computed tomography, noise was reduced from 28.8 ± 0.3 HU to a range between 18.3 ± 0.2 and 5.9 ± 0.2 HU for Full-Fan Head scans, from 14.4 ± 0.2 HU to a range between 12.8 ± 0.3 and 5.2 ± 0.3 HU for Half-Fan Head scans, and from 6.2 ± 0.1 HU to a range between 3.8 ± 0.1 and 2.0 ± 0.2 HU for Pelvis scans, with the iterative cone-beam computed tomography algorithm. Spatial resolution was marginally improved while results for uniformity and HU constancy were similar. For the head phantom study, noise was reduced from 43.6 HU to a range between 24.8 and 13.0 HU for a Full-Fan Head and from 35.1 HU to a range between 22.9 and 14.0 HU for a Half-Fan Head scan. The patient data study showed that artifacts due to photon starvation and streak artifacts were all reduced, and image noise in specified target regions were reduced to 62% ± 15% for 10 patients. Conclusion: Noise and contrast-to-noise ratio image quality characteristics were significantly improved using the iterative cone-beam computed tomography reconstruction algorithm relative to the filtered back-projection-based cone-beam computed tomography method. These improvements will enhance the accuracy of cone-beam computed tomography-based image-guided applications.

scholarly journals Cone Beam Computed Tomography Data Acquisition during VMAT Delivery with Subsequent Respiratory Phase Sorting Based on Projection Image Cross-correlation

2011 ◽  
Vol 52 (1) ◽  
pp. 112-113 ◽  
Author(s):  
Keiichi NAKAGAWA ◽  
Satoshi KIDA ◽  
Akihiro HAGA ◽  
Yoshitaka MASUTANI ◽  
Hideomi YAMASHITA ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Data Acquisition ◽  
Cone Beam Computed Tomography ◽  
Cross Correlation ◽  
Cone Beam ◽  
Respiratory Phase ◽  
Projection Image ◽  
Tomography Data
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