“…12 Owing to the inherent signal-to-noise constraints of computerized tomography and DSC-MRI, however, the use of model-free approaches may be suboptimal because of the unphysiological oscillations in the estimated residue function, 13 which would propagate to its derivative and thereby CTH. While the regularization steps of model-free deconvolution approaches 11,13 may stabilize CBF estimates, [14][15][16] they are not optimized to detect salient features of the residue function. This has led to the development of deconvolution approaches that instead use flexible, yet realistic, parametrized models to describe tissue residue in the deconvolution step.…”
Section: Introductionmentioning
confidence: 99%
“…This has led to the development of deconvolution approaches that instead use flexible, yet realistic, parametrized models to describe tissue residue in the deconvolution step. 16,17 We show that the expectation-maximization (EM) and Levenberg-Marquardt steps of an existing parametric approach 17 can be explicitly calculated to allow simple and efficient computation of CTH and oxygen extraction capacity (OEF max ) in addition to the traditional 'macroscopic' perfusion markers such as CBF and MTT. We assess the performance of the algorithm across a wide range of physiologic hemodynamic conditions, 3 and evaluate its robustness to low signal-to-noise ratio (SNR) as well as bolus delay, and compare with model-free approaches.…”
Section: Introductionmentioning
confidence: 99%
“…[8][9][10] In humans, tracking of the passage of bolus-injected intravascular contrast agent by either computerized tomography or dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) can be used to indirectly quantify the retention of plasma in the cerebral microvasculature. The relative CBF and the tissue residue function, which denotes the proportion of contrast agent that remains in the vasculature at time t after it was injected as a narrow pulse, can be determined by deconvolving the tissue contrast concentration time curve with that of an artery.11 The residue function, in turn, is directly related to the distribution of vascular transit times, h(t), and hence to CTH.12 Owing to the inherent signal-to-noise constraints of computerized tomography and DSC-MRI, however, the use of model-free approaches may be 16,17 We show that the expectation-maximization (EM) and Levenberg-Marquardt steps of an existing parametric approach 17 can be explicitly calculated to allow simple and efficient computation of CTH and oxygen extraction capacity (OEF max ) in addition to the traditional 'macroscopic' perfusion markers such as CBF and MTT. We assess the performance of the algorithm across a wide range of physiologic hemodynamic conditions, 3 and evaluate its robustness to low signal-to-noise ratio (SNR) as well as bolus delay, and compare with model-free approaches.…”
The regional availability of oxygen in brain tissue is traditionally inferred from the magnitude of cerebral blood flow (CBF) and the concentration of oxygen in arterial blood. Measurements of CBF are therefore widely used in the localization of neuronal response to stimulation and in the evaluation of patients suspected of acute ischemic stroke or flow-limiting carotid stenosis. It was recently demonstrated that capillary transit time heterogeneity (CTH) limits maximum oxygen extraction fraction (OEF max ) that can be achieved for a given CBF. Here we present a statistical approach for determining CTH, mean transit time (MTT), and CBF using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). Using numerical simulations, we demonstrate that CTH, MTT, and OEF max can be estimated with low bias and variance across a wide range of microvascular flow patterns, even at modest signal-to-noise ratios. Mean transit time estimated by singular value decomposition (SVD) deconvolution, however, is confounded by CTH. The proposed technique readily identifies malperfused tissue in acute stroke patients and appears to highlight information not detected by the standard SVD technique. We speculate that this technique permits the non-invasive detection of tissue with impaired oxygen delivery in neurologic disorders such as acute ischemic stroke and Alzheimer's disease during routine diagnostic imaging. Keywords: brain ischemia; imaging; kinetic modeling; mathematical modeling; MRI; neuroradiology INTRODUCTION Normal brain function requires a continuous supply of oxygen to meet the metabolic demands of neuroglial activity. The regional availability of oxygen in brain tissue is traditionally inferred from the magnitude of cerebral blood flow (CBF) and the concentration of oxygen in arterial blood. Cerebral blood flow is sensitive to regional levels of neuronal activity-known as neurovascular coupling-and methods to detect changes in CBF therefore provide powerful means of mapping brain function.
Journal of Cerebral BloodIn disease, measurements of CBF are widely used in the evaluation of patients suspected having acute ischemic stroke or stenoocclusive carotid artery disease. The extent of CBF reduction below the ischemic threshold is thought to predict the development of permanent infarction in acute stroke, 1 while the CBF response to well-defined vasodilator responses is used in the assessment of carotid artery disease severity. 2 The microscopic distribution of CBF in brain tissue is rarely considered in the study of the pathogenesis of brain disorders. We recently demonstrated that the heterogeneity of capillary transit times affect oxygen extraction efficacy in tissue.3 By extending the classic flow-diffusion equation, we showed that the capillary blood mean transit time (MTT) and capillary transit time heterogeneity (CTH) combined determines the maximum oxygen extraction fraction (OEF max ) that can be achieved for a given tissue oxygen tension. In a series of recent papers, we review the putative effects...
“…12 Owing to the inherent signal-to-noise constraints of computerized tomography and DSC-MRI, however, the use of model-free approaches may be suboptimal because of the unphysiological oscillations in the estimated residue function, 13 which would propagate to its derivative and thereby CTH. While the regularization steps of model-free deconvolution approaches 11,13 may stabilize CBF estimates, [14][15][16] they are not optimized to detect salient features of the residue function. This has led to the development of deconvolution approaches that instead use flexible, yet realistic, parametrized models to describe tissue residue in the deconvolution step.…”
Section: Introductionmentioning
confidence: 99%
“…This has led to the development of deconvolution approaches that instead use flexible, yet realistic, parametrized models to describe tissue residue in the deconvolution step. 16,17 We show that the expectation-maximization (EM) and Levenberg-Marquardt steps of an existing parametric approach 17 can be explicitly calculated to allow simple and efficient computation of CTH and oxygen extraction capacity (OEF max ) in addition to the traditional 'macroscopic' perfusion markers such as CBF and MTT. We assess the performance of the algorithm across a wide range of physiologic hemodynamic conditions, 3 and evaluate its robustness to low signal-to-noise ratio (SNR) as well as bolus delay, and compare with model-free approaches.…”
Section: Introductionmentioning
confidence: 99%
“…[8][9][10] In humans, tracking of the passage of bolus-injected intravascular contrast agent by either computerized tomography or dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) can be used to indirectly quantify the retention of plasma in the cerebral microvasculature. The relative CBF and the tissue residue function, which denotes the proportion of contrast agent that remains in the vasculature at time t after it was injected as a narrow pulse, can be determined by deconvolving the tissue contrast concentration time curve with that of an artery.11 The residue function, in turn, is directly related to the distribution of vascular transit times, h(t), and hence to CTH.12 Owing to the inherent signal-to-noise constraints of computerized tomography and DSC-MRI, however, the use of model-free approaches may be 16,17 We show that the expectation-maximization (EM) and Levenberg-Marquardt steps of an existing parametric approach 17 can be explicitly calculated to allow simple and efficient computation of CTH and oxygen extraction capacity (OEF max ) in addition to the traditional 'macroscopic' perfusion markers such as CBF and MTT. We assess the performance of the algorithm across a wide range of physiologic hemodynamic conditions, 3 and evaluate its robustness to low signal-to-noise ratio (SNR) as well as bolus delay, and compare with model-free approaches.…”
The regional availability of oxygen in brain tissue is traditionally inferred from the magnitude of cerebral blood flow (CBF) and the concentration of oxygen in arterial blood. Measurements of CBF are therefore widely used in the localization of neuronal response to stimulation and in the evaluation of patients suspected of acute ischemic stroke or flow-limiting carotid stenosis. It was recently demonstrated that capillary transit time heterogeneity (CTH) limits maximum oxygen extraction fraction (OEF max ) that can be achieved for a given CBF. Here we present a statistical approach for determining CTH, mean transit time (MTT), and CBF using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). Using numerical simulations, we demonstrate that CTH, MTT, and OEF max can be estimated with low bias and variance across a wide range of microvascular flow patterns, even at modest signal-to-noise ratios. Mean transit time estimated by singular value decomposition (SVD) deconvolution, however, is confounded by CTH. The proposed technique readily identifies malperfused tissue in acute stroke patients and appears to highlight information not detected by the standard SVD technique. We speculate that this technique permits the non-invasive detection of tissue with impaired oxygen delivery in neurologic disorders such as acute ischemic stroke and Alzheimer's disease during routine diagnostic imaging. Keywords: brain ischemia; imaging; kinetic modeling; mathematical modeling; MRI; neuroradiology INTRODUCTION Normal brain function requires a continuous supply of oxygen to meet the metabolic demands of neuroglial activity. The regional availability of oxygen in brain tissue is traditionally inferred from the magnitude of cerebral blood flow (CBF) and the concentration of oxygen in arterial blood. Cerebral blood flow is sensitive to regional levels of neuronal activity-known as neurovascular coupling-and methods to detect changes in CBF therefore provide powerful means of mapping brain function.
Journal of Cerebral BloodIn disease, measurements of CBF are widely used in the evaluation of patients suspected having acute ischemic stroke or stenoocclusive carotid artery disease. The extent of CBF reduction below the ischemic threshold is thought to predict the development of permanent infarction in acute stroke, 1 while the CBF response to well-defined vasodilator responses is used in the assessment of carotid artery disease severity. 2 The microscopic distribution of CBF in brain tissue is rarely considered in the study of the pathogenesis of brain disorders. We recently demonstrated that the heterogeneity of capillary transit times affect oxygen extraction efficacy in tissue.3 By extending the classic flow-diffusion equation, we showed that the capillary blood mean transit time (MTT) and capillary transit time heterogeneity (CTH) combined determines the maximum oxygen extraction fraction (OEF max ) that can be achieved for a given tissue oxygen tension. In a series of recent papers, we review the putative effects...
“…This method has been shown to improve the accuracy of the estimated CBF when compared with the actual CBF. Figure 2 shows a sample CBF image, plotted in arbitrary units, of a healthy subject and an ischaemic stroke patient obtained from Mehndiratta et al [6]. Figure 2.CBF map, using arbitrary units, of a healthy subject ( a ) and an ischaemic stroke patient ( b ) taken from Mehndiratta et al [6].…”
The microvasculature plays a vital part in the cardiovascular system. Any impairment to its function can lead to significant pathophysiological effects, particularly in organs such as the brain where there is a very tight coupling between structure and function. However, it is extremely difficult to quantify the health of the microvasculature in vivo, other than by assessing perfusion, using techniques such as arterial spin labelling. Recent work has suggested that the flow distribution within a voxel could also be a valuable measure. This can also be measured clinically, but as yet has not been related to the properties of the microvasculature due to the difficulties in modelling and characterizing these strongly inter-connected networks. In this paper, we present a new technique for characterizing an existing physiologically accurate model of the cerebral microvasculature in terms of its residue function. A new analytical mathematical framework for calculation of the residue function, based on the mass transport equation, of any arbitrary network is presented together with results from simulations. We then present a method for characterizing this function, which can be directly related to clinical data, and show how the resulting parameters are affected under conditions of both reduced perfusion and reduced network density. It is found that the residue function parameters are affected in different ways by these two effects, opening up the possibility of using such parameters, when acquired from clinical data, to infer information about both the network properties and the perfusion distribution. These results open up the possibility of obtaining valuable clinical information about the health of the microvasculature in vivo, providing additional tools to clinicians working in cerebrovascular diseases, such as stroke and dementia.
“…11,17 That is, current deconvolution methods cannot capture the rapid kinetics of intravascular tracers with sufficient accuracy for CBF quantification. 28 Improved deconvolution algorithms have been proposed, 11,29,30 but must be validated before clinical use of CBF maps. The MTT dependence of these algorithms requires validation in humans.…”
In the bolus tracking technique with computed tomography (CT) or magnetic resonance imaging, cerebral blood flow (CBF) is computed from deconvolution analysis, but its accuracy is unclear. To evaluate the reliability of CT perfusion (CTP)-derived CBF, we examined 27 patients with symptomatic or asymptomatic unilateral cerebrovascular steno-occlusive disease. Results from three deconvolution algorithms, standard singular value decomposition (sSVD), delay-corrected SVD (dSVD), and block-circulant SVD (cSVD), were compared with (15)O positron emission tomography (PET) as a reference standard. To investigate CBF errors associated with the deconvolution analysis, differences in lesion-to-normal CBF ratios between PET and CTP were correlated with prolongation of arterial-tissue delay (ATD) and mean transit time (MTT) in the lesion hemisphere. Computed tomography perfusion results strongly depended on the deconvolution algorithms used. Standard singular value decomposition showed ATD-dependent underestimation of CBF ratio, whereas cSVD showed overestimation of the CBF ratio when MTT was severely prolonged in the lesions. The computer simulations reproduced the trend observed in patients. Deconvolution by dSVD can provide lesion-to-normal CBF ratios less dependent on ATD and MTT, but requires accurate ATD maps in advance. A practical and accurate method for CTP is required to assess CBF in patients with MTT-prolonged regions.
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