Literature DB >> 24989388

Assessing image quality and dose reduction of a new x-ray computed tomography iterative reconstruction algorithm using model observers.

Hsin-Wu Tseng1, Jiahua Fan2, Matthew A Kupinski1, Paavana Sainath2, Jiang Hsieh2.   

Abstract

PURPOSE: A number of different techniques have been developed to reduce radiation dose in x-ray computed tomography (CT) imaging. In this paper, the authors will compare task-based measures of image quality of CT images reconstructed by two algorithms: conventional filtered back projection (FBP), and a new iterative reconstruction algorithm (IR).
METHODS: To assess image quality, the authors used the performance of a channelized Hotelling observer acting on reconstructed image slices. The selected channels are dense difference Gaussian channels (DDOG).A body phantom and a head phantom were imaged 50 times at different dose levels to obtain the data needed to assess image quality. The phantoms consisted of uniform backgrounds with low contrast signals embedded at various locations. The tasks the observer model performed included (1) detection of a signal of known location and shape, and (2) detection and localization of a signal of known shape. The employed DDOG channels are based on the response of the human visual system. Performance was assessed using the areas under ROC curves and areas under localization ROC curves.
RESULTS: For signal known exactly (SKE) and location unknown/signal shape known tasks with circular signals of different sizes and contrasts, the authors' task-based measures showed that a FBP equivalent image quality can be achieved at lower dose levels using the IR algorithm. For the SKE case, the range of dose reduction is 50%-67% (head phantom) and 68%-82% (body phantom). For the study of location unknown/signal shape known, the dose reduction range can be reached at 67%-75% for head phantom and 67%-77% for body phantom case. These results suggest that the IR images at lower dose settings can reach the same image quality when compared to full dose conventional FBP images.
CONCLUSIONS: The work presented provides an objective way to quantitatively assess the image quality of a newly introduced CT IR algorithm. The performance of the model observers using the IR images was always higher than that seen using the FBP images in the authors' SKE and SKE location unknown detection tasks. To achieve a FBP-equivalent image quality in CT systems, the authors can lower the radiation dose by using this IR image reconstruction algorithm. Further studies are warranted using clinical data and human observer to validate these results for more complicated and realistic tasks.

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Year:  2014        PMID: 24989388     DOI: 10.1118/1.4881143

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  15 in total

1.  Anthropomorphic model observer performance in three-dimensional detection task for low-contrast computed tomography.

Authors:  Alexandre Ba; Miguel P Eckstein; Damien Racine; Julien G Ott; Francis Verdun; Sabine Kobbe-Schmidt; François O Bochud
Journal:  J Med Imaging (Bellingham)       Date:  2015-12-29

2.  Channelized Hotelling observer correlation with human observers for low-contrast detection in liver CT images.

Authors:  Alexandre Ba; Craig K Abbey; Damien Racine; Anaïs Viry; Francis R Verdun; Sabine Schmidt; François O Bochud
Journal:  J Med Imaging (Bellingham)       Date:  2019-05-20

3.  Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages.

Authors:  André Euler; Bram Stieltjes; Zsolt Szucs-Farkas; Reto Eichenberger; Clemens Reisinger; Anna Hirschmann; Caroline Zaehringer; Achim Kircher; Matthias Streif; Sabine Bucher; David Buergler; Luigia D'Errico; Sebastién Kopp; Markus Wilhelm; Sebastian T Schindera
Journal:  Eur Radiol       Date:  2017-04-03       Impact factor: 5.315

4.  The role of extra-foveal processing in 3D imaging.

Authors:  Miguel P Eckstein; Miguel A Lago; Craig K Abbey
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-03-10

5.  Design of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems.

Authors:  Hsin-Wu Tseng; Jiahua Fan; Matthew A Kupinski
Journal:  J Med Imaging (Bellingham)       Date:  2016-07-28

6.  Numerical observer for atherosclerotic plaque classification in spectral computed tomography.

Authors:  Auranuch Lorsakul; Georges El Fakhri; William Worstell; Jinsong Ouyang; Yothin Rakvongthai; Andrew F Laine; Quanzheng Li
Journal:  J Med Imaging (Bellingham)       Date:  2016-07-12

7.  Evaluation of low-contrast detectability for iterative reconstruction in pediatric abdominal computed tomography: a phantom study.

Authors:  Nicholas Rubert; Richard Southard; Susan M Hamman; Ryan Robison
Journal:  Pediatr Radiol       Date:  2019-11-09

8.  Rapid measurement of the low contrast detectability of CT scanners.

Authors:  Akinyinka Omigbodun; J Y Vaishnav; Scott S Hsieh
Journal:  Med Phys       Date:  2021-01-13       Impact factor: 4.071

9.  Modified ideal observer model (MIOM) for high-contrast and high-spatial resolution CT imaging tasks.

Authors:  Juan P Cruz-Bastida; Daniel Gomez-Cardona; John Garrett; Timothy Szczykutowicz; Guang-Hong Chen; Ke Li
Journal:  Med Phys       Date:  2017-07-18       Impact factor: 4.071

10.  Can conclusions drawn from phantom-based image noise assessments be generalized to in vivo studies for the nonlinear model-based iterative reconstruction method?

Authors:  Daniel Gomez-Cardona; Ke Li; Jiang Hsieh; Meghan G Lubner; Perry J Pickhardt; Guang-Hong Chen
Journal:  Med Phys       Date:  2016-02       Impact factor: 4.071

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