ArticleJournal of medical imaging (Bellingham, Wash.)2016
Correlation between human detection accuracy and observer model-based image quality metrics in computed tomography.
Article in Journal of medical imaging (Bellingham, Wash.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 57 citations in OpenAlex.
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- Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms.Bioengineering (Basel, Switzerland) · 2024Article
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- Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.Journal of digital imaging · 2023Article
- Quality Assessment of Computed Tomography Images using a Channelized Hoteling Observer: Optimization of Protocols in Clinical Practice.Advanced biomedical research · 2023Article
- Iterative reconstruction with multifrequency signal recognition technology to improve low-contrast detectability: A phantom study.Acta radiologica open · 2022Article
- Clinical acceptance of deep learning reconstruction for abdominal CT imaging: objective and subjective image quality and low-contrast detectability assessment.European radiology · 2022Article
- Performance of Spectral Photon-Counting Coronary CT Angiography and Comparison with Energy-Integrating-Detector CT: Objective Assessment with Model Observer.Diagnostics (Basel, Switzerland) · 2021Article
- Development, validation, and relevance of in vivo low-contrast task transfer function to estimate detectability in clinical CT images.Medical physics · 2021Article
- Virtual clinical trials in medical imaging: a review.Journal of medical imaging (Bellingham, Wash.) · 2020Review
- A Universal Protocol for Abdominal CT Examinations Performed on a Photon-Counting Detector CT System: A Feasibility Study.Investigative radiology · 2020Article
- A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.Medical physics · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to compare computed tomography (CT) low-contrast detectability from human readers with observer model-based surrogates of image quality. A phantom with a range of low-contrast signals (five contrasts, three sizes) was imaged on a state-of-the-art CT scanner (Siemens' force). Images were reconstructed using filtered back projection and advanced modeled iterative reconstruction and were assessed by 11 readers using a two alternative forced choice method. Concurrently, contrast-to-noise ratio (CNR), area-weighted CNR (CNRA), and observer model-based metrics were estimated, including nonprewhitening (NPW) matched filter, NPW with eye filter (NPWE), NPW with internal noise, NPW with an eye filter and internal noise (NPWEi), channelized Hotelling observer (CHO), and CHO with internal noise (CHOi). The correlation coefficients (Pearson and Spearman), linear discriminator error, [Formula: see text], and magnitude of confidence intervals, [Formula: see text], were used to determine correlation, proper characterization of the reconstruction algorithms, and model precision, respectively. Pearson (Spearman) correlation was 0.36 (0.33), 0.83 (0.84), 0.84 (0.86), 0.86 (0.88), 0.86 (0.91), 0.88 (0.90), 0.85 (0.89), and 0.87 (0.84), [Formula: see text] was 0.25, 0.15, 0.2, 0.25, 0.3, 0.25, 0.4, and 0.45, and [Formula: see text] was [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for CNR, CNRA, NPW, NPWE, NPWi, NPWEi, CHO, and CHOi, respectively.
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