ArticleAJNR. American journal of neuroradiology2020
Aging and the Brain: A Quantitative Study of Clinical CT Images.
Article in AJNR. American journal of neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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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
7 citing papers in PubMed, 12 citations in OpenAlex.
- Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.AJNR. American journal of neuroradiology · 2024Article
- A Comparison of Global Brain Volumetrics Obtained from CT versus MRI Using 2 Publicly Available Software Packages.AJNR. American journal of neuroradiology · 2022Article
- Head CT: Toward Making Full Use of the Information the X-Rays Give.AJNR. American journal of neuroradiology · 2021Review
- The effects of the skull on CT imaging of the brain: a skull and brain phantom study.The British journal of radiology · 2021Article
- Seizures at the onset of aneurysmal SAH: epiphenomenon or valuable predictor?Journal of neurology · 2021Article
- Early Brain Volume Changes After Stroke: Subgroup Analysis From the AXIS-2 Trial.Frontiers in neurology · 2021Article
- Pediatric Head CT: Automated Quantitative Analysis with Quantile Regression.AJNR. American journal of neuroradiology · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeThough CT is a highly calibrated imaging modality, head CT is typically interpreted qualitatively. Our aim was to initiate the establishment of a reference quantitative database for clinical head CT. MATERIALS AND
methodsAn automated segmentation algorithm was developed and applied to 354 clinical head CT scans with radiographically normal findings (ages, 18-101 years; 203 women) to measure brain volume, brain parenchymal fraction, brain radiodensity, and brain parenchymal radiomass. Brain parenchymal fraction was modeled using quantile regression analysis.
resultsBrain parenchymal fraction is highly correlated with age (
conclusionsAn automated segmentation algorithm has been developed and applied to clinical head CT images to initiate the development of a reference database for quantitative brain CT imaging. Such a database can be subject to quantile regression analysis to stratify patient brain CT scans by metrics such as brain parenchymal fraction, radiodensity, and radiomass, to aid in the identification of statistical outliers and lend quantitative assessment to image interpretation.
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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.