ArticleMagnetic resonance imaging2023
The arterial input function: Spatial dependence within the imaging volume and its influence on 3D quantitative dynamic contrast-enhanced MRI for head and neck cancer.
Article in Magnetic resonance imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06108089 (Novel Hypoxia Imaging for Head and Neck Cancer), which is not on this map. Cited by 1 paper.
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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.
Novel Hypoxia Imaging for Head and Neck Cancer: Imaging Phenotype for Personalized Treatment
Who cites it
1 citing paper in PubMed.
- Streamlined calculation of kidney function using dynamic contrast-enhanced MRI with population-based arterial input function and a whole-kidney model.European radiology experimental · 2026Article
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6 authors.
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Abstract
purposeTo evaluate the dependence of the arterial input function (AIF) on the imaging z-axis and its effect on 3D DCE MRI pharmacokinetic parameters as mediated by the SPGR signal equation and Extended Tofts-Kermode model. THEORY: For SPGR-based 3D DCE MRI acquisition of the head and neck, inflow effects within vessels violate the assumptions underlying the SPGR signal model. Errors in the SPGR-based AIF estimate propagate through the Extended Tofts-Kermode model to affect the output pharmacokinetic parameters. MATERIALS AND
methods3D DCE-MRI data were acquired for six newly diagnosed HNC patients in a prospective single arm cohort study. AIF were selected within the carotid arteries at each z-axis location. A region of interest (ROI) was placed in normal paravertebral muscle and the Extended Tofts-Kermode model solved for each pixel within the ROI for each AIF. Results were compared to those obtained with a published population average AIF.
resultsDue to inflow effect, the AIF showed extreme variation in their temporal shapes. K
conclusionInflow effects may introduce an unknown bias to SPGR-based 3D DCE pharmacokinetic parameters. Variation in the computed parameters depends on the selected AIF location. In the context of high flow, measurements may be limited to relative rather than absolute quantitative parameters.
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