Literature DB >> 30995611

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

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

Abstract

The purpose of this study was to determine the correlation between human observer performance for localization of small low contrast lesions within uniform water background versus an anatomical liver background, under the conditions of varying dose, lesion size, and reconstruction algorithm. Liver lesions (5 mm, 7 mm, and 9 mm, contrast:  -21 HU) were digitally inserted into CT projection data of ten normal patients in vessel-free liver regions. Noise was inserted into the projection data to create three image sets: full dose and simulated half and quarter doses. Images were reconstructed with a standard filtered back projection (FBP) and an iterative reconstruction (IR) algorithm. Lesion and noise insertion procedures were repeated with water phantom data. Two-dimensional regions of interest (66 lesion-present, 34 lesion-absent) were selected, randomized, and independently reviewed by three medical physicists to identify the most likely location of the lesion and provide a confidence score. Locations and confidence scores were assessed using the area under the localization receiver operating characteristic curve (AzLROC). We examined the correlation between human performance for the liver and uniform water backgrounds as dose, lesion size, and reconstruction algorithm varied. As lesion size or dose increased, reader localization performance improved. For full dose IR images, the AzLROC for 5, 7, and 9 mm lesions were 0.53, 0.91, and 0.97 (liver) and 0.51, 0.96, and 0.99 (uniform water), respectively. Similar trends were seen with other parameters. Performance values for liver and uniform backgrounds were highly correlated for both reconstruction algorithms, with a Spearman correlation of ρ  =  0.97, and an average difference in AzLROC of 0.05  ±  0.04. For the task of localizing low contrast liver lesions, human observer performance was highly correlated between anatomical and uniform backgrounds, suggesting that lesion localization studies emulating a clinical test of liver lesion detection can be performed using a uniform background.

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Year:  2019        PMID: 30995611      PMCID: PMC6598706          DOI: 10.1088/1361-6560/ab1a45

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


  21 in total

1.  Objective assessment of low contrast detectability in computed tomography with Channelized Hotelling Observer.

Authors:  Damien Racine; Alexandre H Ba; Julien G Ott; François O Bochud; Francis R Verdun
Journal:  Phys Med       Date:  2015-10-26       Impact factor: 2.685

2.  Validation of a Projection-domain Insertion of Liver Lesions into CT Images.

Authors:  Baiyu Chen; Chi Ma; Shuai Leng; Jeff L Fidler; Shannon P Sheedy; Cynthia H McCollough; Joel G Fletcher; Lifeng Yu
Journal:  Acad Radiol       Date:  2016-07-16       Impact factor: 3.173

3.  Radiation dose reduction with Sinogram Affirmed Iterative Reconstruction technique for abdominal computed tomography.

Authors:  Mannudeep K Kalra; Mischa Woisetschläger; Nils Dahlström; Sarabjeet Singh; Maria Lindblom; Garry Choy; Petter Quick; Bernhard Schmidt; Martin Sedlmair; Michael A Blake; Anders Persson
Journal:  J Comput Assist Tomogr       Date:  2012 May-Jun       Impact factor: 1.826

4.  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

5.  Contrast-to-noise ratio and low-contrast object resolution on full- and low-dose MDCT: SAFIRE versus filtered back projection in a low-contrast object phantom and in the liver.

Authors:  Mark E Baker; Frank Dong; Andrew Primak; Nancy A Obuchowski; David Einstein; Namita Gandhi; Brian R Herts; Andrei Purysko; Erick Remer; Neil Vachhani; Neil Vachani
Journal:  AJR Am J Roentgenol       Date:  2012-07       Impact factor: 3.959

6.  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

7.  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

8.  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

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

10.  Low-dose CT for the detection and classification of metastatic liver lesions: Results of the 2016 Low Dose CT Grand Challenge.

Authors:  Cynthia H McCollough; Adam C Bartley; Rickey E Carter; Baiyu Chen; Tammy A Drees; Phillip Edwards; David R Holmes; Alice E Huang; Farhana Khan; Shuai Leng; Kyle L McMillan; Gregory J Michalak; Kristina M Nunez; Lifeng Yu; Joel G Fletcher
Journal:  Med Phys       Date:  2017-10       Impact factor: 4.071

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  6 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.  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.

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

3.  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

4.  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

5.  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

6.  Comparison of low-contrast detectability between uniform and anatomically realistic phantoms-influences on CT image quality assessment.

Authors:  Juliane Conzelmann; Ulrich Genske; Arthur Emig; Michael Scheel; Bernd Hamm; Paul Jahnke
Journal:  Eur Radiol       Date:  2021-09-02       Impact factor: 5.315

  6 in total

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