2001
DOI: 10.1002/mrm.1307
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On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI

Abstract: A quantitative analysis was undertaken to calibrate the perfusion quantification technique based on tracking the first pass of a bolus of a blood pool contrast agent. A complete simulation of the bolus passage, of the associated changes in the T 2 and T* 2 signals, and of the data processing was performed using the tracer dilution theory, an analytical theory of the MR signal from living tissues and numerical simulations. Magnetic resonance imaging (MRI) of the first passage of a bolus of a blood pool contrast… Show more

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Cited by 170 publications
(205 citation statements)
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“…Both cerebral blood volume and vessel size index measurements are derived from susceptibility-contrast MRI, but the relationship between brain tumor angiogenesis and susceptibility-induced contrast is not well understood. It has been shown that abnormal tumor vessel morphology can profoundly affect susceptibility-induced contrast (Pathak et al, 2003), and that computational models incorporating the actual vascular structure are required to elucidate this complex relationship (Kiselev, 2001;Pathak et al, 2003Pathak et al, , 2008a. This is now possible with the mMRI data acquired in this work and the recent development of a computational model of MRI contrast known as the finite perturber method (Pathak et al, 2008c).…”
Section: Discussionmentioning
confidence: 88%
“…Both cerebral blood volume and vessel size index measurements are derived from susceptibility-contrast MRI, but the relationship between brain tumor angiogenesis and susceptibility-induced contrast is not well understood. It has been shown that abnormal tumor vessel morphology can profoundly affect susceptibility-induced contrast (Pathak et al, 2003), and that computational models incorporating the actual vascular structure are required to elucidate this complex relationship (Kiselev, 2001;Pathak et al, 2003Pathak et al, , 2008a. This is now possible with the mMRI data acquired in this work and the recent development of a computational model of MRI contrast known as the finite perturber method (Pathak et al, 2008c).…”
Section: Discussionmentioning
confidence: 88%
“…Given a large uncertainty, the maximal vessel size was selected to yield the mean radius ϭ 100 m calculated according to Eq. [10] below. The uniformity of the vessel size distribution follows from the scaling properties of the vascular tree (11).…”
Section: Tissue Modelingmentioning
confidence: 98%
“…The latter limits the size of vessels, which are statistically averaged within a voxel or a small region of interest (ROI) as discussed in Ref. (10). Given a large uncertainty, the maximal vessel size was selected to yield the mean radius ϭ 100 m calculated according to Eq.…”
Section: Tissue Modelingmentioning
confidence: 99%
“…We stress the importance of considering this issue when using a local AIF, where the PVE will be enhanced. However, to reach the goal of absolute CBF quantification further issues must be addressed, including PVE in tissue, variation of local hematocrit with vessel diameter (1), delay and dispersion of the AIF (11), and differences in relaxation between tissue and blood for DSC imaging (21).…”
Section: Figmentioning
confidence: 99%