Literature DB >> 28983493

Use of a channelized Hotelling observer to assess CT image quality and optimize dose reduction for iteratively reconstructed images.

Christopher P Favazza1, Andrea Ferrero1, Lifeng Yu1, Shuai Leng1, Kyle L McMillan1, Cynthia H McCollough1.   

Abstract

The use of iterative reconstruction (IR) algorithms in CT generally decreases image noise and enables dose reduction. However, the amount of dose reduction possible using IR without sacrificing diagnostic performance is difficult to assess with conventional image quality metrics. Through this investigation, achievable dose reduction using a commercially available IR algorithm without loss of low contrast spatial resolution was determined with a channelized Hotelling observer (CHO) model and used to optimize a clinical abdomen/pelvis exam protocol. A phantom containing 21 low contrast disks-three different contrast levels and seven different diameters-was imaged at different dose levels. Images were created with filtered backprojection (FBP) and IR. The CHO was tasked with detecting the low contrast disks. CHO performance indicated dose could be reduced by 22% to 25% without compromising low contrast detectability (as compared to full-dose FBP images) whereas 50% or more dose reduction significantly reduced detection performance. Importantly, default settings for the scanner and protocol investigated reduced dose by upward of 75%. Subsequently, CHO-based protocol changes to the default protocol yielded images of higher quality and doses more consistent with values from a larger, dose-optimized scanner fleet. CHO assessment provided objective data to successfully optimize a clinical CT acquisition protocol.

Entities:  

Keywords:  channelized Hotelling observer; computed tomography; iterative reconstruction; low contrast resolution; radiation dose

Year:  2017        PMID: 28983493      PMCID: PMC5624940          DOI: 10.1117/1.JMI.4.3.031213

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  24 in total

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2.  Evaluation of the impact of tube current modulation on lesion detectability using model observers.

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Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008

3.  Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms.

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Journal:  Med Phys       Date:  2016-12       Impact factor: 4.071

4.  Correlation between human detection accuracy and observer model-based image quality metrics in computed tomography.

Authors:  Justin Solomon; Ehsan Samei
Journal:  J Med Imaging (Bellingham)       Date:  2016-09-22

5.  Pilot study of detection, radiologist confidence and image quality with sinogram-affirmed iterative reconstruction at half-routine dose level.

Authors:  Joel G Fletcher; William R Krueger; David M Hough; James E Huprich; Jeff L Fidler; Jia Wang; Maria M Shiung; W Scott Harmsen; Katharine L Grant; Cynthia H McCollough
Journal:  J Comput Assist Tomogr       Date:  2013 Mar-Apr       Impact factor: 1.826

6.  Assessment of low contrast detection in CT using model observers: Developing a clinically-relevant tool for characterising adaptive statistical and model-based iterative reconstruction.

Authors:  Julien G Ott; Alexandre Ba; Damien Racine; Anais Viry; François O Bochud; Francis R Verdun
Journal:  Z Med Phys       Date:  2016-05-04       Impact factor: 4.820

7.  Observer Performance in the Detection and Classification of Malignant Hepatic Nodules and Masses with CT Image-Space Denoising and Iterative Reconstruction.

Authors:  Joel G Fletcher; Lifeng Yu; Zhoubo Li; Armando Manduca; Daniel J Blezek; David M Hough; Sudhakar K Venkatesh; Gregory C Brickner; Joseph C Cernigliaro; Amy K Hara; Jeff L Fidler; David S Lake; Maria Shiung; David Lewis; Shuai Leng; Kurt E Augustine; Rickey E Carter; David R Holmes; Cynthia H McCollough
Journal:  Radiology       Date:  2015-05-26       Impact factor: 11.105

8.  Effect of Radiation Dose Reduction and Reconstruction Algorithm on Image Noise, Contrast, Resolution, and Detectability of Subtle Hypoattenuating Liver Lesions at Multidetector CT: Filtered Back Projection versus a Commercial Model-based Iterative Reconstruction Algorithm.

Authors:  Justin Solomon; Daniele Marin; Kingshuk Roy Choudhury; Bhavik Patel; Ehsan Samei
Journal:  Radiology       Date:  2017-02-07       Impact factor: 11.105

9.  Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain.

Authors:  Shuai Leng; Lifeng Yu; Yi Zhang; Rickey Carter; Alicia Y Toledano; Cynthia H McCollough
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

Review 10.  Image quality in CT: From physical measurements to model observers.

Authors:  F R Verdun; D Racine; J G Ott; M J Tapiovaara; P Toroi; F O Bochud; W J H Veldkamp; A Schegerer; R W Bouwman; I Hernandez Giron; N W Marshall; S Edyvean
Journal:  Phys Med       Date:  2015-10-12       Impact factor: 2.685

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  4 in total

1.  A Web-Based Software Platform for Efficient and Quantitative CT Image Quality Assessment and Protocol Optimization.

Authors:  Mingdong Fan; Theodore Thayib; Liqiang Ren; Scott Hsieh; Cynthia McCollough; David Holmes; Lifeng Yu
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2021-02-15

2.  Efficient Evaluation of Low-contrast Detectability of Deep-CNN-based CT Reconstruction Using Channelized Hotelling Observer on the ACR Accreditation Phantom.

Authors:  Mingdong Fan; Zhongxing Zhou; Thomas Vrieze; Jia Wang; Cynthia McCollough; Lifeng Yu
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2022-04-04

Review 3.  Virtual clinical trials in medical imaging: a review.

Authors:  Ehsan Abadi; William P Segars; Benjamin M W Tsui; Paul E Kinahan; Nick Bottenus; Alejandro F Frangi; Andrew Maidment; Joseph Lo; Ehsan Samei
Journal:  J Med Imaging (Bellingham)       Date:  2020-04-11

4.  Low-dose CT image and projection dataset.

Authors:  Taylor R Moen; Baiyu Chen; David R Holmes; Xinhui Duan; Zhicong Yu; Lifeng Yu; Shuai Leng; Joel G Fletcher; Cynthia H McCollough
Journal:  Med Phys       Date:  2020-12-16       Impact factor: 4.071

  4 in total

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