Literature DB >> 30995626

Localization of liver lesions in abdominal CT imaging: II. Mathematical model observer performance correlates with human observer performance for localization of liver lesions in abdominal CT imaging.

Samantha K N Dilger1, Shuai Leng, Baiyu Chen, Rickey E Carter, Chris P Favazza, Joel G Fletcher, Cynthia H McCollough, Lifeng Yu.   

Abstract

Determination of the effect of protocol modifications on diagnostic performance in CT with human observers is extremely time-consuming, limiting the applicability of such methods in routine clinical practice. In this work, we sought to determine whether a channelized Hotelling observer (CHO) could predict human observer performance for the task of liver lesion localization as background, reconstruction algorithm, dose, and lesion size were varied. Liver lesions (5 mm, 7 mm, and 9 mm) were digitally inserted into the CT projection data of patients with normal livers and water phantoms. The projection data were reconstructed with filtered back projection (FBP) and iterative reconstruction (IR) algorithms for three dose levels: full dose (liver CTDIvol  =  10.5  ±  8.5 mGy, water phantom CTDIvol  =  9.6  ±  0.1 mGy) and simulated half and quarter doses. For each of 36 datasets (3 dose levels  ×  2 reconstruction algorithms  ×  2 backgrounds  ×  3 sizes), 66 signal-present and 34 signal-absent 2D images were extracted from the reconstructed volumes. Three medical physicists independently reviewed each dataset and noted the lesion location and a confidence score for each image. A CHO with Gabor channels was calculated to estimate the performance for each of the 36 localization tasks. The CHO performances, quantified using localization receiver operating characteristic (LROC) analysis, were compared to the human observer performances. Performance values between human and model observers were highly correlated for equivalent parameters (same lesion size, dose, background, and reconstruction), with a Spearman's correlation coefficient of 0.93 (95% CI: 0.82-0.98). CHO performance values for the uniform background were strongly correlated (ρ  =  0.94, CI: 0.80-1.0) with the human observer performance values for the liver background. Performance values between human observers and CHO were highly correlated as dose, reconstruction type and object size were varied for the task of localization of patient liver lesions in both uniform and liver backgrounds.

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Year:  2019        PMID: 30995626      PMCID: PMC6598689          DOI: 10.1088/1361-6560/ab1a62

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  17 in total

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Authors:  Miguel Eckstein; Jay Bartroff; Craig Abbey; James Whiting; Francois Bochud
Journal:  Opt Express       Date:  2003-03-10       Impact factor: 3.894

2.  Medical radiation exposure in the U.S. in 2006: preliminary results.

Authors:  Fred A Mettler; Bruce R Thomadsen; Mythreyi Bhargavan; Debbie B Gilley; Joel E Gray; Jill A Lipoti; John McCrohan; Terry T Yoshizumi; Mahadevappa Mahesh
Journal:  Health Phys       Date:  2008-11       Impact factor: 1.316

3.  Task-based image quality evaluation of iterative reconstruction methods for low dose CT using computer simulations.

Authors:  Jingyan Xu; Matthew K Fuld; George S K Fung; Benjamin M W Tsui
Journal:  Phys Med Biol       Date:  2015-03-17       Impact factor: 3.609

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

Authors:  Justin Solomon; Alexandre Ba; François Bochud; Ehsan Samei
Journal:  Med Phys       Date:  2016-12       Impact factor: 4.071

5.  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

6.  Model observers for assessment of image quality.

Authors:  H H Barrett; J Yao; J P Rolland; K J Myers
Journal:  Proc Natl Acad Sci U S A       Date:  1993-11-01       Impact factor: 11.205

7.  Localization of liver lesions in abdominal CT imaging: I. Correlation of human observer performance between anatomical and uniform backgrounds.

Authors:  Samantha K N Dilger; Lifeng Yu; Baiyu Chen; Chris P Favazza; Rickey E Carter; Joel G Fletcher; Cynthia H McCollough; Shuai Leng
Journal:  Phys Med Biol       Date:  2019-05-10       Impact factor: 3.609

8.  Correlation between human and model observer performance for discrimination task in CT.

Authors:  Yi Zhang; Shuai Leng; Lifeng Yu; Rickey E Carter; Cynthia H McCollough
Journal:  Phys Med Biol       Date:  2014-05-30       Impact factor: 3.609

9.  Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms.

Authors:  Lifeng Yu; Shuai Leng; Lingyun Chen; James M Kofler; Rickey E Carter; Cynthia H McCollough
Journal:  Med Phys       Date:  2013-04       Impact factor: 4.071

10.  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

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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.  Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography.

Authors:  Hao Gong; Joel G Fletcher; Jay P Heiken; Michael L Wells; Shuai Leng; Cynthia H McCollough; Lifeng Yu
Journal:  Med Phys       Date:  2021-12-01       Impact factor: 4.506

3.  Observer Performance for Detection of Pulmonary Nodules at Chest CT over a Large Range of Radiation Dose Levels.

Authors:  Joel G Fletcher; David L Levin; Anne-Marie G Sykes; Rebecca M Lindell; Darin B White; Ronald S Kuzo; Vighnesh Suresh; Lifeng Yu; Shuai Leng; David R Holmes; Akitoshi Inoue; Matthew P Johnson; Rickey E Carter; Cynthia H McCollough
Journal:  Radiology       Date:  2020-09-29       Impact factor: 11.105

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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