Literature DB >> 25019428

Methods for clinical evaluation of noise reduction techniques in abdominopelvic CT.

Eric C Ehman1, Lifeng Yu, Armando Manduca, Amy K Hara, Maria M Shiung, Dayna Jondal, David S Lake, Robert G Paden, Daniel J Blezek, Michael R Bruesewitz, Cynthia H McCollough, David M Hough, Joel G Fletcher.   

Abstract

Most noise reduction methods involve nonlinear processes, and objective evaluation of image quality can be challenging, since image noise cannot be fully characterized on the sole basis of the noise level at computed tomography (CT). Noise spatial correlation (or noise texture) is closely related to the detection and characterization of low-contrast objects and may be quantified by analyzing the noise power spectrum. High-contrast spatial resolution can be measured using the modulation transfer function and section sensitivity profile and is generally unaffected by noise reduction. Detectability of low-contrast lesions can be evaluated subjectively at varying dose levels using phantoms containing low-contrast objects. Clinical applications with inherent high-contrast abnormalities (eg, CT for renal calculi, CT enterography) permit larger dose reductions with denoising techniques. In low-contrast tasks such as detection of metastases in solid organs, dose reduction is substantially more limited by loss of lesion conspicuity due to loss of low-contrast spatial resolution and coarsening of noise texture. Existing noise reduction strategies for dose reduction have a substantial impact on lowering the radiation dose at CT. To preserve the diagnostic benefit of CT examination, thoughtful utilization of these strategies must be based on the inherent lesion-to-background contrast and the anatomy of interest. The authors provide an overview of existing noise reduction strategies for low-dose abdominopelvic CT, including analytic reconstruction, image and projection space denoising, and iterative reconstruction; review qualitative and quantitative tools for evaluating these strategies; and discuss the strengths and limitations of individual noise reduction methods. ©RSNA, 2014.

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Year:  2014        PMID: 25019428     DOI: 10.1148/rg.344135128

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  34 in total

1.  Observer performance for adaptive, image-based denoising and filtered back projection compared to scanner-based iterative reconstruction for lower dose CT enterography.

Authors:  Joel G Fletcher; Amy K Hara; Jeff L Fidler; Alvin C Silva; John M Barlow; Rickey E Carter; Adam Bartley; Maria Shiung; David R Holmes; Nicolas K Weber; David H Bruining; Lifeng Yu; Cynthia H McCollough
Journal:  Abdom Imaging       Date:  2015-06

2.  Technical Note: Measuring contrast- and noise-dependent spatial resolution of an iterative reconstruction method in CT using ensemble averaging.

Authors:  Lifeng Yu; Thomas J Vrieze; Shuai Leng; Joel G Fletcher; Cynthia H McCollough
Journal:  Med Phys       Date:  2015-05       Impact factor: 4.071

3.  Degradation of CT Low-Contrast Spatial Resolution Due to the Use of Iterative Reconstruction and Reduced Dose Levels.

Authors:  Cynthia H McCollough; Lifeng Yu; James M Kofler; Shuai Leng; Yi Zhang; Zhoubo Li; Rickey E Carter
Journal:  Radiology       Date:  2015-03-26       Impact factor: 11.105

4.  Low-Dose CT for Craniosynostosis: Preserving Diagnostic Benefit with Substantial Radiation Dose Reduction.

Authors:  J C Montoya; L J Eckel; D R DeLone; A L Kotsenas; F E Diehn; L Yu; A C Bartley; R E Carter; C H McCollough; J G Fletcher
Journal:  AJNR Am J Neuroradiol       Date:  2017-02-09       Impact factor: 3.825

5.  Computed Tomography Image Quality Evaluation of a New Iterative Reconstruction Algorithm in the Abdomen (Adaptive Statistical Iterative Reconstruction-V) a Comparison With Model-Based Iterative Reconstruction, Adaptive Statistical Iterative Reconstruction, and Filtered Back Projection Reconstructions.

Authors:  Martin H Goodenberger; Nicolaus A Wagner-Bartak; Shiva Gupta; Xinming Liu; Ramon Q Yap; Jia Sun; Eric P Tamm; Corey T Jensen
Journal:  J Comput Assist Tomogr       Date:  2018 Mar/Apr       Impact factor: 1.826

6.  Dose reduction potential of iterative reconstruction algorithms in neck CTA-a simulation study.

Authors:  Stephan Ellmann; Ferdinand Kammerer; Thomas Allmendinger; Michael Brand; Rolf Janka; Matthias Hammon; Michael M Lell; Michael Uder; Manuel Kramer
Journal:  Dentomaxillofac Radiol       Date:  2016-08-19       Impact factor: 2.419

7.  Impact of number of repeated scans on model observer performance for a low-contrast detection task in computed tomography.

Authors:  Chi Ma; Lifeng Yu; Baiyu Chen; Christopher Favazza; Shuai Leng; Cynthia McCollough
Journal:  J Med Imaging (Bellingham)       Date:  2016-05-26

8.  Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer.

Authors:  Maryam Gholizadeh-Ansari; Javad Alirezaie; Paul Babyn
Journal:  J Digit Imaging       Date:  2020-04       Impact factor: 4.056

9.  Optimizing CT technique to reduce radiation dose: effect of changes in kVp, iterative reconstruction, and noise index on dose and noise in a human cadaver.

Authors:  Kevin J Chang; Scott Collins; Baojun Li; William W Mayo-Smith
Journal:  Radiol Phys Technol       Date:  2016-10-03

10.  Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT.

Authors:  Corey T Jensen; Nicolaus A Wagner-Bartak; Lan N Vu; Xinming Liu; Bharat Raval; David Martinez; Wei Wei; Yuan Cheng; Ehsan Samei; Shiva Gupta
Journal:  Radiology       Date:  2018-11-27       Impact factor: 11.105

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