ReviewTheranostics2013
Model observers in medical imaging research.
Review in Theranostics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- Error Injection in Task-Based Image Quality Pipelines: What Regression Testing Cannot Catch, and Why Neither Internal Identities nor Closed-Form References Suffice Alone.Journal of imaging · 2026Article
- Context-specific image quality assessment for virtual histologic staining: checklist and guideline.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2025Article
- Automated measurement of detectability index in CT imaging: Development and validation.Journal of applied clinical medical physics · 2025Article
- Approximating the ideal observer for joint signal detection and estimation tasks by the use of Markov-Chain Monte Carlo with generative adversarial networks.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Noise-aware system generative model (NASGM): positron emission tomography (PET) image simulation framework with observer validation studies.Medical physics · 2025Article
- Modeling Radiologists' Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- A deep learning anthropomorphic model observer for a detection task in PET.Medical physics · 2024Article
- Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography.IEEE transactions on computational imaging · 2024Article
- Spatio-Temporal Positron Emission Tomography Reconstruction with Attenuation and Motion Correction.Journal of imaging · 2023Article
- A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods.IEEE transactions on medical imaging · 2022Article
- Impact of deep learning-based image super-resolution on binary signal detection.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Harmonization of in-plane resolution in CT using multiple reconstructions from single acquisitions.Medical physics · 2021Article
- Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks.IEEE transactions on medical imaging · 2021Article
- DeepAMO: a multi-slice, multi-view anthropomorphic model observer for visual detection tasks performed on volume images.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Medical image quality metrics for foveated model observers.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Foveated Model Observers for Visual Search in 3D Medical Images.IEEE transactions on medical imaging · 2021Article
- Virtual clinical trials in medical imaging: a review.Journal of medical imaging (Bellingham, Wash.) · 2020Review
- Article
- Predicting Detection Performance on Security X-Ray Images as a Function of Image Quality.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Model observers play an important role in the optimization and assessment of imaging devices. In this review paper, we first discuss the basic concepts of model observers, which include the mathematical foundations and psychophysical considerations in designing both optimal observers for optimizing imaging systems and anthropomorphic observers for modeling human observers. Second, we survey a few state-of-the-art computational techniques for estimating model observers and the principles of implementing these techniques. Finally, we review a few applications of model observers in medical imaging research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.