» Articles » PMID: 19351382

Average Arterial Input Function for Quantitative Dynamic Contrast Enhanced Magnetic Resonance Imaging of Neck Nodal Metastases

Overview
Journal BMC Med Phys
Publisher Biomed Central
Specialty Nuclear Medicine
Date 2009 Apr 9
PMID 19351382
Citations 23
Authors
Affiliations
Soon will be listed here.
Abstract

Background: The present study determines the feasibility of generating an average arterial input function (Avg-AIF) from a limited population of patients with neck nodal metastases to be used for pharmacokinetic modeling of dynamic contrast-enhanced MRI (DCE-MRI) data in clinical trials of larger populations.

Methods: Twenty patients (mean age 50 years [range 27-77 years]) with neck nodal metastases underwent pretreatment DCE-MRI studies with a temporal resolution of 3.75 to 7.5 sec on a 1.5T clinical MRI scanner. Eleven individual AIFs (Ind-AIFs) met the criteria of expected enhancement pattern and were used to generate Avg-AIF. Tofts model was used to calculate pharmacokinetic DCE-MRI parameters. Bland-Altman plots and paired Student t-tests were used to describe significant differences between the pharmacokinetic parameters obtained from individual and average AIFs.

Results: Ind-AIFs obtained from eleven patients were used to calculate the Avg-AIF. No overall significant difference (bias) was observed for the transfer constant (Ktrans) measured with Ind-AIFs compared to Avg-AIF (p = 0.20 for region-of-interest (ROI) analysis and p = 0.18 for histogram median analysis). Similarly, no overall significant difference was observed for interstitial fluid space volume fraction (ve) measured with Ind-AIFs compared to Avg-AIF (p = 0.48 for ROI analysis and p = 0.93 for histogram median analysis). However, the Bland-Altman plot suggests that as Ktrans increases, the Ind-AIF estimates tend to become proportionally higher than the Avg-AIF estimates.

Conclusion: We found no statistically significant overall bias in Ktrans or ve estimates derived from Avg-AIF, generated from a limited population, as compared with Ind-AIFs.However, further study is needed to determine whether calibration is needed across the range of Ktrans. The Avg-AIF obtained from a limited population may be used for pharmacokinetic modeling of DCE-MRI data in larger population studies with neck nodal metastases. Further validation of the Avg-AIF approach with a larger population and in multiple regions is desirable.

Citing Articles

Patient-specific arterial input function for accurate perfusion assessment in intraoperative fluorescence imaging.

Tang Y, Jiang S, Sottosanti J, Usherwood T, Cao X, Bateman L J Biomed Opt. 2024; 29(Suppl 3):S33306.

PMID: 39247899 PMC: 11379448. DOI: 10.1117/1.JBO.29.S3.S33306.


Quantitative dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer: A systematic comparison of different modelling approaches.

Kastad Hoiskar M, Saether O, Alsaker M, Redalen K, Winter R Phys Imaging Radiat Oncol. 2024; 29:100548.

PMID: 38380153 PMC: 10876686. DOI: 10.1016/j.phro.2024.100548.


A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology.

LoCastro E, Paudyal R, Konar A, LaViolette P, Akin O, Hatzoglou V Tomography. 2023; 9(6):2052-2066.

PMID: 37987347 PMC: 10661267. DOI: 10.3390/tomography9060161.


Bridging the macro to micro resolution gap with angiographic optical coherence tomography and dynamic contrast enhanced MRI.

Zabel W, Allam N, Foltz W, Flueraru C, Taylor E, Vitkin I Sci Rep. 2022; 12(1):3159.

PMID: 35210476 PMC: 8873467. DOI: 10.1038/s41598-022-07000-1.


Application of Community Detection Algorithm to Investigate the Correlation between Imaging Biomarkers of Tumor Metabolism, Hypoxia, Cellularity, and Perfusion for Precision Radiotherapy in Head and Neck Squamous Cell Carcinomas.

Paudyal R, Grkovski M, Oh J, Schoder H, Aramburu Nunez D, Hatzoglou V Cancers (Basel). 2021; 13(15).

PMID: 34359810 PMC: 8345739. DOI: 10.3390/cancers13153908.


References
1.
Yankeelov T, Luci J, Lepage M, Li R, DeBusk L, Lin P . Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. Magn Reson Imaging. 2005; 23(4):519-29. DOI: 10.1016/j.mri.2005.02.013. View

2.
Dyke J, Panicek D, Healey J, Meyers P, Huvos A, Schwartz L . Osteogenic and Ewing sarcomas: estimation of necrotic fraction during induction chemotherapy with dynamic contrast-enhanced MR imaging. Radiology. 2003; 228(1):271-8. DOI: 10.1148/radiol.2281011651. View

3.
Kim S, Quon H, Loevner L, Rosen M, Dougherty L, Kilger A . Transcytolemmal water exchange in pharmacokinetic analysis of dynamic contrast-enhanced MRI data in squamous cell carcinoma of the head and neck. J Magn Reson Imaging. 2007; 26(6):1607-17. DOI: 10.1002/jmri.21207. View

4.
Hoskin P, Saunders M, Goodchild K, Powell M, Taylor N, Baddeley H . Dynamic contrast enhanced magnetic resonance scanning as a predictor of response to accelerated radiotherapy for advanced head and neck cancer. Br J Radiol. 2000; 72(863):1093-8. DOI: 10.1259/bjr.72.863.10700827. View

5.
Yanagi Y, Asaumi J, Konouchi H, Hisatomi M, Matsuzaki H, Murakami J . Enhancement effects of test injection with a small amount of MR contrast medium in the oral and maxillofacial region. Eur J Radiol. 2006; 59(3):367-70. DOI: 10.1016/j.ejrad.2006.03.017. View