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Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction

Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR)... Computed tomography (CT) is a useful and widely employed imaging technique, which represents the largest source of population exposure to ionizing radiation in industrialized countries. Adaptive Statistical Iterative Reconstruction (ASIR) is an iterative reconstruction algorithm with the potential to allow reduction of radiation exposure while preserving diagnostic information. The aim of this phantom study was to assess the performance of ASIR, in terms of a number of image quality indices, when different reconstruction blending levels are employed. CT images of the Catphan-504 phantom were reconstructed using conventional filtered back-projection (FBP) and ASIR with reconstruction blending levels of 20, 40, 60, 80, and 100%. Noise, noise power spectrum (NPS), contrast-to-noise ratio (CNR) and modulation transfer function (MTF) were estimated for different scanning parameters and contrast objects. Noise decreased and CNR increased non-linearly up to 50 and 100%, respectively, with increasing blending level of reconstruction. Also, ASIR has proven to modify the NPS curve shape. The MTF of ASIR reconstructed images depended on tube load/contrast and decreased with increasing blending level of reconstruction. In particular, for low radiation exposure and low contrast acquisitions, ASIR showed lower performance than FBP, in terms of spatial resolution for all blending levels of reconstruction. CT image quality varies substantially with the blending level of reconstruction. ASIR has the potential to reduce noise whilst maintaining diagnostic information in low radiation exposure CT imaging. Given the opposite variation of CNR and spatial resolution with the blending level of reconstruction, it is recommended to use an optimal value of this parameter for each specific clinical application. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Australasian Physical & Engineering Sciences in Medicine Springer Journals

Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Australasian College of Physical Scientists and Engineers in Medicine
Subject
Biomedicine; Biomedicine, general; Biological and Medical Physics, Biophysics; Medical and Radiation Physics; Biomedical Engineering
ISSN
0158-9938
eISSN
1879-5447
DOI
10.1007/s13246-018-0645-8
pmid
29737491
Publisher site
See Article on Publisher Site

Abstract

Computed tomography (CT) is a useful and widely employed imaging technique, which represents the largest source of population exposure to ionizing radiation in industrialized countries. Adaptive Statistical Iterative Reconstruction (ASIR) is an iterative reconstruction algorithm with the potential to allow reduction of radiation exposure while preserving diagnostic information. The aim of this phantom study was to assess the performance of ASIR, in terms of a number of image quality indices, when different reconstruction blending levels are employed. CT images of the Catphan-504 phantom were reconstructed using conventional filtered back-projection (FBP) and ASIR with reconstruction blending levels of 20, 40, 60, 80, and 100%. Noise, noise power spectrum (NPS), contrast-to-noise ratio (CNR) and modulation transfer function (MTF) were estimated for different scanning parameters and contrast objects. Noise decreased and CNR increased non-linearly up to 50 and 100%, respectively, with increasing blending level of reconstruction. Also, ASIR has proven to modify the NPS curve shape. The MTF of ASIR reconstructed images depended on tube load/contrast and decreased with increasing blending level of reconstruction. In particular, for low radiation exposure and low contrast acquisitions, ASIR showed lower performance than FBP, in terms of spatial resolution for all blending levels of reconstruction. CT image quality varies substantially with the blending level of reconstruction. ASIR has the potential to reduce noise whilst maintaining diagnostic information in low radiation exposure CT imaging. Given the opposite variation of CNR and spatial resolution with the blending level of reconstruction, it is recommended to use an optimal value of this parameter for each specific clinical application.

Journal

Australasian Physical & Engineering Sciences in Medicine – Springer Journals

Published: May 8, 2018

References

  • AAPM/RSNA physics tutorial for residents: topics in CT. Radiation dose in CT
    McNitt-Gray, MF
  • Iterative reconstruction techniques for computed tomography part 1: technical principles
    Willemink, MJ; Jong, PA; Leiner, T
  • Iterative reconstruction methods in X-ray CT
    Beister, M; Kolditz, D; Kalender, WA
  • ASIR™: a new reconstruction technique to lower dose without compromise. ASIR™ whitepaper
    Argaud, C
  • Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms
    Richard, S; Husarik, DB; Yadava, G; Murphy, SN; Samei, E
  • Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology
    Samei, E; Richard, S
  • Iterative reconstruction methods in two different MDCT scanners: physical metrics and 4-alternative forced-choice detectability experiments—a phantom approach
    Miéville, FA; Gudinchet, F; Brunelle, F; Bochud, FO; Verdun, FR
  • A qualitative and quantitative analysis of radiation dose and image quality of computed tomography images using Adaptive Statistical Iterative Reconstruction
    Hussain, FA; Mail, N; Shamy, AM; Alghamdi, S; Saoudi, A
  • A study of the image quality of computed tomography adaptive statistical iterative reconstructed brain images using subjective and objective methods
    Mangat, J; Morgan, J; Benson, E; Bath, M; Lewis, M; Reilly, A
  • Quantitative analysis of the effect of iterative reconstruction using a phantom: determining the appropriate blending percentage
    Kim, HG; Chung, YE; Lee, YH
  • Catphan ® 504 Manual
  • A framework for noise-power spectrum analysis of multidimensional images
    Siewerdsen, JH; Cunningham, IA; Jaffray, DA
  • Image quality in CT: from physical measurements to model observers
    Verdun, FR; Racine, D; Ott, JG
  • A simple approach to measure computed tomography (CT) modulation transfer function (MTF) and noise-power spectrum (NPS) using the American College of Radiology (ACR) accreditation phantom
    Friedman, SN; Fung, GSK; Siewerdsen, JH; Tsui, BMW
  • Modulation transfer function measurement of CT images by use of a circular edge method with a logistic curve-fitting technique
    Takenaga, T; Katsuragawa, S; Goto, M; Hatemura, M; Uchiyama, Y; Shiraishi, J
  • SNR and noise measurements for medical imaging: I. A practical approach based on statistical decision theory
    Tapiovaara, MJ; Wagner, RF
  • Investigation into image quality and dose for different patient geometries with multiple cone-beam CT systems
    Gardner, SJ; Studenski, MT; Giaddui, T; Cui, Y; Galvin, J; Yu, Y; Xiao, Y
  • Dual energy CT with photon counting and dual source systems: comparative evaluation
    Atak, H; Shikhaliev, S
  • Model-based iterative reconstruction: effect on patient radiation dose and image quality in pediatric body CT
    Smith, EA; Dillman, JR; Goodsitt, MM; Christodoulou, EG; Keshavarzi, N; Strouse, PJ
  • Characterization of Adaptive Statistical Iterative Reconstruction algorithm for dose reduction in CT: a pediatric oncology perspective
    Brady, SL; Yee, BS; Kaufman, RA
  • Degradation of CT low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels
    McCollough, CH; Yu, L; Kofler, JM
  • The effect of Adaptive Statistical Iterative Reconstruction on the assessment of diagnostic image quality and visualisation of anatomical structures in pediatric cerebral CT examinations
    Larsson, J; Bath, M; Ledenius, K; Thilander-Klang, A
  • Assessment of clinical image quality in pediatric abdominal CT examinations: dependency of the level of Adaptive Statistical Iterative Reconstruction (ASIR) and the type of convolution kernel
    Larsson, J; Bath, M; Ledenius, K; Caisander, H; Thilander-Klang, A
  • Adaptive statistical iterative reconstruction technique for pulmonary CT: image quality of the cadaveric lung on standard- and reduced-dose CT
    Yanagawa, M; Honda, O; Yoshida, S
  • Dose reduction in 64-row whole-body CT in multiple trauma: an optimized CT protocol with iterative image reconstruction on a gemstone-based scintillator
    Geyer, LL; Körner, M; Harrieder, A; Mueck, FG; Deak, Z; Wirth, S; Linsenmaier, U
  • Adaptive Statistical Iterative Reconstruction: assessment of image noise and image quality in coronary CT angiography
    Leipsic, J; LaBounty, TM; Heilbron, B; Min, JK; Mancini, J; Lin, FY; Taylor, C; Dunning, A; Earls, JP
  • The use of Adaptive Statistical Iterative Reconstruction (ASIR) technique in evaluation of patients with cervical spine trauma: impact on radiation dose reduction and image quality
    Patro, SN; Chakraborty, S; Sheikh, A
  • State of the art: iterative CT reconstruction techniques
    Geyer, LL; Schoepf, UJ; Meinel, FG
  • Performance evaluation of iterative reconstruction algorithms for achieving CT radiation dose reduction—a phantom study
    Dodge, CT; Tamm, EP; Cody, DD; Liu, X; Jensen, CT; Wei, W; Kundra, V; Rong, XJ
  • A noise power spectrum study of a new model-based iterative reconstruction system: Veo 3.0
    Li, G; Liu, X; Dodge, CT; Jensen, CT; Rong, XJ
  • How to measure CT image quality: Variations in CT-numbers, uniformity and low contrast resolution for a CT quality assurance phantom
    Gulliksrud, K; Stokke, C; Martinsen, ACT
  • Quantification of head and body CTDI VOL of dual-energy X-ray CT with fast-kVp switching
    Li, B; Yadava, Y; Hsieh, J
  • Characterization of a commercial hybrid iterative and model-based reconstruction algorithm in radiation oncology
    Price, RG; Vance, S; Cattaneo, R; Schultz, L; Elshaikh, MA; Chetty, IJ; Glide-Hurst, CK
  • Objective characterization of GE discovery CT750 HD scanner: gemstone spectral imaging mode
    Zhang, D; Li, X; Liu, B
  • Material-specific transfer function model and SNR in CT
    Brunner, CC; Kyprianou, IS
  • Interphantom and interscanner variations for Hounsfield units—establishment of reference values for HU in a commercial QA phantom
    Sande, EPS; Martinsen, ACT; Hole, EO; Olerud, HM
  • Image texture and radiation dose properties in CT
    Mozejko, D; Andersen, HK; Pedersen, M; Waaler, D; Martinsen, ACT