ArticleRadiological physics and technology2021
Apparent diffusion coefficient measurement using thin-slice diffusion-weighted magnetic resonance imaging: assessment of measurement errors and repeatability.
Article in Radiological physics and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Inter-software agreement in abdominal computed diffusion-weighted imaging: apparent diffusion coefficient and image quality assessment.Abdominal radiology (New York) · 2026Article
- Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom.Radiological physics and technology · 2024Article
- Construction of a Phantom for Image Quality Evaluation in PET/MRI System.Annals of nuclear cardiology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigated the measurement error and repeatability of the apparent diffusion coefficient (ADC) obtained using thin-slice imaging. Diffusion-weighted images of an ice-water phantom were acquired using 1.5-T and 3.0-T scanners with 1-, 3-, and 5-mm thickness. ADC maps were generated at b = 0 and 1000 mm
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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.