ArticleMagnetic resonance imaging2018
Modification of population based arterial input function to incorporate individual variation.
Article in Magnetic resonance imaging, 2018. 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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7 citing papers in PubMed, 10 citations in OpenAlex.
- Deep learning-based super-resolution and denoising algorithm improves reliability of dynamic contrast-enhanced MRI in diffuse glioma.Scientific reports · 2024Article
- Patient-specific arterial input function for accurate perfusion assessment in intraoperative fluorescence imaging.Journal of biomedical optics · 2024Article
- Quantitative renal magnetic resonance imaging: magnetic resonance urography.Pediatric radiology · 2022Review
- Effects of artery input function on dynamic contrast-enhanced MRI for determining grades of gliomas.The British journal of radiology · 2021Article
- Portable Perfusion Phantom Offers Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Accurate Prostate Cancer Grade Stratification: A Pilot Study.Academic radiology · 2021Article
- Accurate Therapeutic Response Assessment of Pancreatic Ductal Adenocarcinoma Using Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging With a Point-of-Care Perfusion Phantom: A Pilot Study.Investigative radiology · 2019Article
- Variability in Quantitative DCE-MRI: Sources and Solutions.Journal of nature and science · 2018Article
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1 author at 1 institution in 1 country.
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Abstract
This technical note describes how to modify a population-based arterial input function to incorporate variation among the individuals. In DCE-MRI, an arterial input function (AIF) is often distorted by pulsated inflow effect and noise. A population-based AIF (pAIF) has high signal-to-noise ratio (SNR), but cannot incorporate the individual variation. AIF variation is mainly induced by variation in cardiac output and blood volume of the individuals, which can be detected by the full width at half maximum (FWHM) during the first passage and the amplitude of AIF, respectively. Thus pAIF scaled in time and amplitude fitting to the individual AIF may serve as a high SNR AIF incorporating the individual variation. The proposed method was validated using DCE-MRI images of 18 prostate cancer patients. Root mean square error (RMSE) of pAIF from individual AIFs was 0.88±0.48mM (mean±SD), but it was reduced to 0.25±0.11mM after pAIF modification using the proposed method (p<0.0001).
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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.