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Statistical reconstruction for x-ray computed tomography using energy-integrating detectors.
Phys Med Biol. 2007 Apr 21; 52(8):2247-66.PM

Abstract

Statistical image reconstruction (SR) algorithms have the potential to significantly reduce x-ray CT image artefacts because they use a more accurate model than conventional filtered backprojection and can incorporate effects such as noise, incomplete data and nonlinear detector response. Most SR algorithms assume that the CT detectors are photon-counting devices and generate Poisson-distributed signals. However, actual CT detectors integrate energy from the x-ray beam and exhibit compound Poisson-distributed signal statistics. This study presents the first assessment of the impact on image quality of the resultant mismatch between the detector and signal statistics models assumed by the sinogram data model and the reconstruction algorithm. A 2D CT projection simulator was created to generate synthetic polyenergetic transmission data assuming (i) photon-counting with simple Poisson-distributed signals and (ii) energy-weighted detection with compound Poisson-distributed signals. An alternating minimization (AM) algorithm was used to reconstruct images from the data models (i) and (ii) for a typical abdominal scan protocol with incident particle fluence levels ranging from 10(5) to 1.6 x 10(6) photons/detector. The images reconstructed from data models (i) and (ii) were compared by visual inspection and image-quality figures of merit. The reconstructed image quality degraded significantly when the means were mismatched from the assumed model. However, if the signal means are appropriately modified, images from data models (i) and (ii) did not differ significantly even when SNR is very low. While data-mean mismatches characteristic of the difference between particle-fluence and energy-fluence transmission can cause significant streaking and cupping artefacts, the mismatch between the actual and assumed CT detector signal statistics did not significantly degrade image quality once systematic data means mismatches were corrected.

Authors+Show Affiliations

Lasio GM
Division of Medical Physics, Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23298, USA.
Whiting BR
No affiliation info available
Williamson JF
No affiliation info available

MeSH

AlgorithmsData Interpretation, StatisticalEquipment DesignEquipment Failure AnalysisRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedTomography, X-Ray ComputedTransducers

Pub Type(s)

Evaluation Study
Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

17404467

Citation

Lasio, Giovanni M., et al. "Statistical Reconstruction for X-ray Computed Tomography Using Energy-integrating Detectors." Physics in Medicine and Biology, vol. 52, no. 8, 2007, pp. 2247-66.
Lasio GM, Whiting BR, Williamson JF. Statistical reconstruction for x-ray computed tomography using energy-integrating detectors. Phys Med Biol. 2007;52(8):2247-66.
Lasio, G. M., Whiting, B. R., & Williamson, J. F. (2007). Statistical reconstruction for x-ray computed tomography using energy-integrating detectors. Physics in Medicine and Biology, 52(8), 2247-66.
Lasio GM, Whiting BR, Williamson JF. Statistical Reconstruction for X-ray Computed Tomography Using Energy-integrating Detectors. Phys Med Biol. 2007 Apr 21;52(8):2247-66. PubMed PMID: 17404467.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Statistical reconstruction for x-ray computed tomography using energy-integrating detectors. AU - Lasio,Giovanni M, AU - Whiting,Bruce R, AU - Williamson,Jeffrey F, Y1 - 2007/04/02/ PY - 2007/4/4/pubmed PY - 2007/6/20/medline PY - 2007/4/4/entrez SP - 2247 EP - 66 JF - Physics in medicine and biology JO - Phys Med Biol VL - 52 IS - 8 N2 - Statistical image reconstruction (SR) algorithms have the potential to significantly reduce x-ray CT image artefacts because they use a more accurate model than conventional filtered backprojection and can incorporate effects such as noise, incomplete data and nonlinear detector response. Most SR algorithms assume that the CT detectors are photon-counting devices and generate Poisson-distributed signals. However, actual CT detectors integrate energy from the x-ray beam and exhibit compound Poisson-distributed signal statistics. This study presents the first assessment of the impact on image quality of the resultant mismatch between the detector and signal statistics models assumed by the sinogram data model and the reconstruction algorithm. A 2D CT projection simulator was created to generate synthetic polyenergetic transmission data assuming (i) photon-counting with simple Poisson-distributed signals and (ii) energy-weighted detection with compound Poisson-distributed signals. An alternating minimization (AM) algorithm was used to reconstruct images from the data models (i) and (ii) for a typical abdominal scan protocol with incident particle fluence levels ranging from 10(5) to 1.6 x 10(6) photons/detector. The images reconstructed from data models (i) and (ii) were compared by visual inspection and image-quality figures of merit. The reconstructed image quality degraded significantly when the means were mismatched from the assumed model. However, if the signal means are appropriately modified, images from data models (i) and (ii) did not differ significantly even when SNR is very low. While data-mean mismatches characteristic of the difference between particle-fluence and energy-fluence transmission can cause significant streaking and cupping artefacts, the mismatch between the actual and assumed CT detector signal statistics did not significantly degrade image quality once systematic data means mismatches were corrected. SN - 0031-9155 UR - https://www.unboundmedicine.com/medline/citation/17404467/Statistical_reconstruction_for_x_ray_computed_tomography_using_energy_integrating_detectors_ L2 - https://doi.org/10.1088/0031-9155/52/8/014 DB - PRIME DP - Unbound Medicine ER -