ArticleMagnetic resonance imaging2014
A comparison of individual and population-derived vascular input functions for quantitative DCE-MRI in rats.
Article in Magnetic resonance imaging, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Dynamic image-informed selection of biomechanical tumor growth models.Biomechanics and modeling in mechanobiology · 2026Article
- Investigating the Limits of Predictability of Magnetic Resonance Imaging-Based Mathematical Models of Tumor Growth.Cancers · 2025Article
- Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma.Scientific reports · 2023Article
- HACE1-mediated NRF2 activation causes enhanced malignant phenotypes and decreased radiosensitivity of glioma cells.Signal transduction and targeted therapy · 2021Article
- Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy.Cancers · 2021Article
- Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling.Radiation oncology (London, England) · 2020Article
- Calibrating a Predictive Model of Tumor Growth and Angiogenesis with Quantitative MRI.Annals of biomedical engineering · 2019Article
- Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements.Tomography (Ann Arbor, Mich.) · 2019Article
- Dynamic contrast-enhanced MRI of the patellar bone: How to quantify perfusion.Journal of magnetic resonance imaging : JMRI · 2018Article
- A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations.Scientific reports · 2017Article
- Validation of a T1 and T2* leakage correction method based on multiecho dynamic susceptibility contrast MRI using MION as a reference standard.Magnetic resonance in medicine · 2016Article
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4 authors.
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Abstract
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) can quantitatively and qualitatively assess physiological characteristics of tissue. Quantitative DCE-MRI requires an estimate of the time rate of change of the concentration of the contrast agent in the blood plasma, the vascular input function (VIF). Measuring the VIF in small animals is notoriously difficult as it requires high temporal resolution images limiting the achievable number of slices, field-of-view, spatial resolution, and signal-to-noise. Alternatively, a population-averaged VIF could be used to mitigate the acquisition demands in studies aimed to investigate, for example, tumor vascular characteristics. Thus, the overall goal of this manuscript is to determine how the kinetic parameters estimated by a population based VIF differ from those estimated by an individual VIF. Eight rats bearing gliomas were imaged before, during, and after an injection of Gd-DTPA. K(trans), ve, and vp were extracted from signal-time curves of tumor tissue using both individual and population-averaged VIFs. Extended model voxel estimates of K(trans) and ve in all animals had concordance correlation coefficients (CCC) ranging from 0.69 to 0.98 and Pearson correlation coefficients (PCC) ranging from 0.70 to 0.99. Additionally, standard model estimates resulted in CCCs ranging from 0.81 to 0.99 and PCCs ranging from 0.98 to 1.00, supporting the use of a population based VIF if an individual VIF is not available.
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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.