ReviewAJNR. American journal of neuroradiology2021
Head CT: Toward Making Full Use of the Information the X-Rays Give.
Review in AJNR. American journal of neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- A randomized multi-window 3D deep learning approach for intracranial hemorrhage detection on non-contrast head CT.Medical & biological engineering & computing · 2026Article
- S2SWCLIP: semantic-optimized prompts with spatial-wavelet synergy for zero-shot anomaly detection.Scientific reports · 2026Article
- Automatic coarse-to-fine AC-PC localization on CT using registration-guided 3D-UNets.Frontiers in neuroinformatics · 2026Article
- Age- and sex-adjusted CT-based reference values for temporal muscle thickness, cross-sectional area and radiodensity.Scientific reports · 2025Article
- Contrast quality control for segmentation task based on deep learning models-Application to stroke lesion in CT imaging.Frontiers in neurology · 2025Article
- Assessment of image quality and establishment of local acceptable quality dose for computed tomography based on patient effective diameter.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Article
- Computational Approaches for Acute Traumatic Brain Injury Image Recognition.Frontiers in neurology · 2022Review
- Medical image fusion quality assessment based on conditional generative adversarial network.Frontiers in neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although clinical head CT images are typically interpreted qualitatively, automated methods applied to routine clinical head CTs enable quantitative assessment of brain volume, brain parenchymal fraction, brain radiodensity, and brain radiomass. These metrics gain clinical meaning when viewed relative to a reference database and expressed as quantile regression values. Quantitative imaging data can aid in objective reporting and in the identification of outliers, with possible diagnostic implications. The comparison to a reference database necessitates standardization of head CT imaging parameters and protocols. Future research is needed to learn the effects of virtual monochromatic imaging on the quantitative characteristics of head CT images.
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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.