Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics

Calamante, Fernando; Yim, Peter J.; Cebral, Juan R.

doi:10.1016/s1053-8119(03)00090-9

Cited by 96 publications

(90 citation statements)

References 33 publications

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“…[4] with the calculated R est (t). For this approach to be successful, it is essential that the deconvolution of the righthand side of Eq.…”

Section: Correction Of Dispersion Errorsmentioning

confidence: 99%

“…However, the vascular operator that properly characterizes the bolus dispersion process is unknown. Different vascular models have been assumed, ranging from simple models (such as a single-exponential VTF(t) (4,5)) to the more complex multipath, multiindicator, four-region organ model (MMID4 model) (7,8). However, the accuracy of the different vascular models remains to be shown, and the use of an incorrect model can potentially lead to an erroneous correction of the dispersion errors.…”

Section: Modeling Of the Vascular Bedmentioning

confidence: 99%

“…Therefore, there is a need to develop methods for the accurate assessment and validation of vascular operators. Furthermore, to the best of our knowledge, there has been no experimental investigation of the distortion effects on the concentration-time curve due to a source of dispersion (4).…”

Section: Modeling Of the Vascular Bedmentioning

confidence: 99%

“…A detailed description of the method and its assumptions can be found in Ref. 4. In brief, it consists of the following steps: 1) From MR anatomical images, a geometric model of the arteries is constructed.…”

Section: Modeling Of the Vascular Bedmentioning

confidence: 99%

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Bolus dispersion issues related to the quantification of perfusion MRI data

Calamante

2005

Magnetic Resonance Imaging

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Quantification of cerebral blood flow (CBF) using dynamicsusceptibility contrast (DSC) MRI relies on the deconvolution of the arterial input function (AIF). The AIF is commonly measured in a major artery (e.g., the middle cerebral artery), and the estimated function is used as a global AIF for the whole slice. However, the presence of bolus delay and dispersion between the artery and the tissue of interest can introduce significant errors in CBF quantification. While several methods have been introduced to minimize or eliminate the effects of bolus delay, the correction of bolus dispersion is more difficult to address because it requires a model for the vascular bed. This article summarizes how this dispersion effect can be incorporated into the model for CBF quantification, and discusses the magnitude of the errors introduced. Furthermore, alternative methods for correcting or minimizing the effects of bolus dispersion in the quantification of CBF are reviewed.

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“…[4] with the calculated R est (t). For this approach to be successful, it is essential that the deconvolution of the righthand side of Eq.…”

Section: Correction Of Dispersion Errorsmentioning

confidence: 99%

Section: Modeling Of the Vascular Bedmentioning

confidence: 99%

Section: Modeling Of the Vascular Bedmentioning

confidence: 99%

Section: Modeling Of the Vascular Bedmentioning

confidence: 99%

See 2 more Smart Citations

Bolus dispersion issues related to the quantification of perfusion MRI data

Calamante

2005

Magnetic Resonance Imaging

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“…The former can be corrected for by using delay-insensitive deconvolution techniques, but dispersion corrupts the perfusion estimates (2,13,14). In addition, local AIF measurements are flow territory specific, which can be especially beneficial when there is a stenosed or occluded large artery, since such large vessel pathologies lead to additional delay and dispersion in the corresponding flow territory (15). The use of a global AIF measured contralaterally from the stenosis or occlusion would create perfusion estimates for the flow territory of the stenosed artery that include the dispersive properties of the stenosis, leading to underestimation of cerebral blood flow (16).…”

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confidence: 99%

Evaluation of signal formation in local arterial input function measurements of dynamic susceptibility contrast MRI

Bleeker

Webb

Walderveen

et al. 2011

Magnetic Resonance in Med

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Correct arterial input function (AIF) measurements in dynamic susceptibility contrast-MRI are crucial for quantification of the hemodynamic parameters. Often a single global AIF is selected near a large brain-feeding artery. Alternatively, local AIF measurements aim for voxel-specific AIFs from smaller arteries. Because local AIFs are measured higher in the arterial-tree, it is assumed that these will reflect the true input of the microvasculature much better. However, do the measured local AIFs reflect the true concentration-time curves of small arteries? To answer this question, in vivo data were used to evaluate local AIF candidates selected based on two different types of angiograms. For interpretation purposes, a 3D numerical model that simulated partial-volume effects in local AIF measurements was created and the simulated local AIFs were compared to the ground truth. The findings are 2-fold. First, the in vivo data showed that the shape-characteristics of local AIFs are similar to the shape-characteristics of gray matter concentration-time curves. Second, these findings are supported by the simulations showing broadening of the measured local AIFs compared to the ground truth. These findings are suggesting that local AIF measurements do not necessarily reflect the true concentration-time curve in small arteries. Magn Reson Med 67:1324-1331,

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Steady flow through a realistic human upper airway geometry

Nithiarasu

Hassan

Morgan

et al. 2008

Numerical Methods in Fluids

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SUMMARYAir flow through a human upper airway (central part) has been carried out using a realistic geometry. In addition to explaining the anatomy, problems and importance of patient-specific study of human upper airways, this article also presents some qualitative and quantitative simulation results. As expected, the shear and pressure forces are large in the oropharynx and laryngopharynx, where the flow passage is narrow. This clearly indicates that these locations should be the focus of any study aimed at understanding the human upper airway collapse in a patient-specific manner.

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Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics

Cited by 96 publications

References 33 publications

Bolus dispersion issues related to the quantification of perfusion MRI data

Bolus dispersion issues related to the quantification of perfusion MRI data

Evaluation of signal formation in local arterial input function measurements of dynamic susceptibility contrast MRI

Steady flow through a realistic human upper airway geometry

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