Effects of Bolus Injection Duration on Perfusion Estimates in Dynamic CT and Dynamic Susceptibility Contrast MRI

Overview

Journal MAGMA

Publisher Springer

Date 2022 Sep 17

PMID 36114897

Authors

Jonathan Arvidsson

Goran Starck

Kerstin Lagerstrand

Doerthe Ziegelitz

Oscar Jalnefjord

Affiliations

Soon will be listed here.

Abstract

Estimates of cerebral blood flow (CBF) and tissue mean transit time (MTT) have been shown to differ between dynamic CT perfusion (CTP) and dynamic susceptibility contrast MRI (DSC-MRI). This study investigates whether these discrepancies regarding CBF and MTT between CTP and DSC-MRI can be attributed to the different injection durations of these techniques. Five subjects were scanned using CTP and DSC-MRI. Region-wise estimates of CBF, MTT, and cerebral blood volume (CBV) were derived based on oscillatory index regularized singular value decomposition. A parametric model that reproduced the shape of measured time curves and characteristics of resulting perfusion parameter estimates was developed and used to simulate data with injection durations typical for CTP and DSC-MRI for a clinically relevant set of perfusion scenarios and noise levels. In simulations, estimates of CBF/MTT showed larger negative/positive bias and increasing variability for CTP when compared to DSC-MRI, especially for high CBF levels. While noise also affected estimates, at clinically relevant levels, the injection duration effect was larger. There are several methodological differences between CTP and DSC-MRI. The results of this study suggest that the injection duration is among those that can explain differences in estimates of CBF and MTT between these bolus tracking techniques.

References

Knutsson L, Stahlberg F, Wirestam R . Absolute quantification of perfusion using dynamic susceptibility contrast MRI: pitfalls and possibilities. MAGMA. 2009; 23(1):1-21. DOI: 10.1007/s10334-009-0190-2. View

THOMPSON Jr H, Starmer C, Whalen R, McIntosh H . INDICATOR TRANSIT TIME CONSIDERED AS A GAMMA VARIATE. Circ Res. 1964; 14:502-15. DOI: 10.1161/01.res.14.6.502. View

Mehndiratta A, MacIntosh B, Crane D, Payne S, Chappell M . A control point interpolation method for the non-parametric quantification of cerebral haemodynamics from dynamic susceptibility contrast MRI. Neuroimage. 2012; 64:560-70. DOI: 10.1016/j.neuroimage.2012.08.083. View

Calamante F . Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc. 2013; 74:1-32. DOI: 10.1016/j.pnmrs.2013.04.002. View

Griffith B, Jain R . Perfusion imaging in neuro-oncology: basic techniques and clinical applications. Radiol Clin North Am. 2015; 53(3):497-511. DOI: 10.1016/j.rcl.2015.01.004. View

Ziegelitz D, Arvidsson J, Hellstrom P, Tullberg M, Wikkelso C, Starck G . Pre-and postoperative cerebral blood flow changes in patients with idiopathic normal pressure hydrocephalus measured by computed tomography (CT)-perfusion. J Cereb Blood Flow Metab. 2015; 36(10):1755-1766. PMC: 5076781. DOI: 10.1177/0271678X15608521. View

Boxerman J, Shiroishi M, Ellingson B, Pope W . Dynamic Susceptibility Contrast MR Imaging in Glioma: Review of Current Clinical Practice. Magn Reson Imaging Clin N Am. 2016; 24(4):649-670. DOI: 10.1016/j.mric.2016.06.005. View

Demeestere J, Wouters A, Christensen S, Lemmens R, Lansberg M . Review of Perfusion Imaging in Acute Ischemic Stroke: From Time to Tissue. Stroke. 2020; 51(3):1017-1024. DOI: 10.1161/STROKEAHA.119.028337. View

Pastore M, Calcagni A . Measuring Distribution Similarities Between Samples: A Distribution-Free Overlapping Index. Front Psychol. 2019; 10:1089. PMC: 6558420. DOI: 10.3389/fpsyg.2019.01089. View

10.

Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J, Pujol S . 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012; 30(9):1323-41. PMC: 3466397. DOI: 10.1016/j.mri.2012.05.001. View

11.

Wintermark M, Maeder P, Thiran J, Schnyder P, Meuli R . Quantitative assessment of regional cerebral blood flows by perfusion CT studies at low injection rates: a critical review of the underlying theoretical models. Eur Radiol. 2001; 11(7):1220-30. DOI: 10.1007/s003300000707. View

12.

Ziegelitz D, Starck G, Mikkelsen I, Tullberg M, Edsbagge M, Wikkelso C . Absolute quantification of cerebral blood flow in neurologically normal volunteers: dynamic-susceptibility contrast MRI-perfusion compared with computed tomography (CT)-perfusion. Magn Reson Med. 2009; 62(1):56-65. DOI: 10.1002/mrm.21975. View

13.

Zanderigo F, Bertoldo A, Pillonetto G, Cobelli Ast C . Nonlinear stochastic regularization to characterize tissue residue function in bolus-tracking MRI: assessment and comparison with SVD, block-circulant SVD, and Tikhonov. IEEE Trans Biomed Eng. 2009; 56(5):1287-97. DOI: 10.1109/TBME.2009.2013820. View

14.

Willats L, Calamante F . The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI. NMR Biomed. 2012; 26(8):913-31. DOI: 10.1002/nbm.2833. View

15.

Mouridsen K, Hansen M, Ostergaard L, Jespersen S . Reliable estimation of capillary transit time distributions using DSC-MRI. J Cereb Blood Flow Metab. 2014; 34(9):1511-21. PMC: 4158667. DOI: 10.1038/jcbfm.2014.111. View

16.

Wintermark M, Sincic R, Sridhar D, Chien J . Cerebral perfusion CT: technique and clinical applications. J Neuroradiol. 2008; 35(5):253-60. DOI: 10.1016/j.neurad.2008.03.005. View

17.

van Osch M, Vonken E, Wu O, Viergever M, van der Grond J, Bakker C . Model of the human vasculature for studying the influence of contrast injection speed on cerebral perfusion MRI. Magn Reson Med. 2003; 50(3):614-22. DOI: 10.1002/mrm.10567. View

18.

Wintermark M, Sesay M, Barbier E, Borbely K, Dillon W, Eastwood J . Comparative overview of brain perfusion imaging techniques. Stroke. 2005; 36(9):e83-99. DOI: 10.1161/01.STR.0000177884.72657.8b. View

19.

Kroll K, Wilke N, Jerosch-Herold M, Wang Y, Zhang Y, BACHE R . Modeling regional myocardial flows from residue functions of an intravascular indicator. Am J Physiol. 1996; 271(4 Pt 2):H1643-55. PMC: 3010231. DOI: 10.1152/ajpheart.1996.271.4.H1643. View

20.

Meier P, Zierler K . On the theory of the indicator-dilution method for measurement of blood flow and volume. J Appl Physiol. 1954; 6(12):731-44. DOI: 10.1152/jappl.1954.6.12.731. View