ArticlePhysics in medicine and biology2019
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.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
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- Automated Web-based Software for CT Quality Control Testing of Low-contrast Detectability using Model Observers.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Accurate and efficient measurement of channelized Hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom.Medical physics · 2023Article
- Evaluation of Low-contrast Detectability of Photon-Counting-Detector CT Using Channelized Hotelling Observer and an ACR Accreditation Phantom.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Low-dose CT image and projection dataset.Medical physics · 2021Article
- A Web-Based Software Platform for Efficient and Quantitative CT Image Quality Assessment and Protocol Optimization.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Observer Performance for Detection of Pulmonary Nodules at Chest CT over a Large Range of Radiation Dose Levels.Radiology · 2020Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
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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