Evaluation of four postprocessing methods for determination of cerebral blood volume and mean transit time by dynamic susceptibility contrast imaging

Perkiö, Jussi; Aronen, Hannu J.; Kangasmäki, Aki; Liu, Yawu; Karonen, Jari O.; Savolainen, Sauli; Østergaard, Leif

doi:10.1002/mrm.10126

Cited by 70 publications

(53 citation statements)

References 27 publications

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“…Relative CBV values were obtained in a conventional manner (11) by integrating the ⌬R* 2 time course curves from t ϭ 10 s (the injection time) up to t ϭ 40 s, which covered the first pass of the bolus. Absolute CBV estimates were then derived by assuming that frontal white matter had a blood volume fraction f ϭ 0.02, in consistency with results from positron emission tomography (PET) (18).…”

Section: Discussionmentioning

confidence: 99%

“…Other possible confounding factors include diffusional anisotropy, as occurs in white matter, and contrast-agent leakage in certain types of lesions. However, contrastagent extravasation due to a disruption of the BBB also affects CBV estimates obtained with standard dynamic contrast susceptibility imaging (11), and techniques developed to correct for this effect in CBV estimates could be adapted for MVD estimates as well (23).…”

Section: Discussionmentioning

confidence: 99%

“…The upper bound, N U , depends on the CBV and the mean vessel radius, in addition to Q and D. As with the mean diffusivity, the CBV can be determined with established MRI techniques (11,12). The mean vessel radius, R , is not directly measurable with MRI; however, in order for red blood cells to pass, it can be no less than a few micrometers.…”

Section: Connection Between Q and Mvdmentioning

confidence: 99%

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Microvessel density estimation in the human brain by means of dynamic contrast‐enhanced echo‐planar imaging

Jensen

Inglese

2006

Magnetic Resonance in Med

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Animal studies have shown that in vivo estimates of microvessel density in the brain may be obtained from an MRI-measurable index (Q) provided that a sufficiently high dose of an intravascular paramagnetic contrast agent is employed. Q is determined from the shifts in the transverse relaxation rates induced by the contrast agent, and a high dose is required for the validity of analytic expressions relating Q to the microvessel density. However, the steady-state imaging techniques used in these prior investigations are not appropriate for humans, as the required contrast agent dose is too large. Here results of a pilot study with three subjects are reported. The results suggest that reliable Q measurements can be performed in the human brain at 1.5 T by using an interleaved spin-echo ( The microvessel density (MVD) is a useful prognostic indicator in many tumors (1,2), and is also of interest in the study of ischemia and aging (3-5). It was recently proposed that an MRI-measurable index (referred to as Q) may allow for in vivo quantitative estimates of the MVD (6,7). In particular, a good correspondence between MVD estimates based on Q and histologic values in the mouse brain was demonstrated in a study using superparamagnetic iron oxide particles (7).However, in order to obtain useful MVD values from Q measurements, a high dose of an intravascular paramagnetic or superparamagnetic contrast agent is required. Indeed, for a steady-state experiment, it has been argued that the necessary dose of gadopentetate dimeglumine (Gd-DTPA) at a field level of 1.5 T should be much greater than the standard clinical dose of 0.1 mmol/kg (6). In this article we show that one can overcome this limitation by measuring Q during the first passage of a contrast agent bolus, which makes Q-based MVD estimation feasible for clinical studies.In addition to the need for a high dose, a limitation of the previously proposed method is that it yields, within the model assumptions, a lower bound for the MVD. In regions where the distribution of vessel radii is narrow, this lower bound is expected to be a good MVD predictor (6,7). However, the presence of larger venules and arterioles can broaden the vessel distribution to the extent that the MVD will be significantly underestimated. For this reason, we introduce here a second Q-based quantity that is formally an MVD upper bound. Indeed, our results suggest that this upper bound actually provides a better estimator of the MVD than does the lower bound.Our method utilizes an interleaved spin-echo (SE)/gradient-echo (GE) echo-planar imaging (EPI) sequence and a bolus injection of triple the standard dose of Gd-DTPA administered at a rate of 5 ml/s, in order to achieve a high first-pass contrast agent concentration. Since triple-dose Gd-DTPA injections are safe and well tolerated (8), the method is suitable for human subjects. The Q-values are measured at the concentration peak during the first passage of the bolus through the brain's microvascular network, to ensure sufficient contrast-agent conc...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Connection Between Q and Mvdmentioning

confidence: 99%

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Microvessel density estimation in the human brain by means of dynamic contrast‐enhanced echo‐planar imaging

Jensen

Inglese

2006

Magnetic Resonance in Med

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“…Assuming uniform arterial concentration profiles in all arterial inputs, relative CBV measurements are determined by simply integrating the area under the concentration time curve (11)(12)(13), occasionally by the use of a gamma variate function to correct for tracer recirculation (19). In a recent report, Perkio et al (20) concluded that numerically integrating the area of the tissue curve (over the full time range for which it was imaged) and integrating the area of the deconvolved tissue impulse response function (see below) represent the most accurate methods of determining relative CBV.…”

Section: Cbv Measurementsmentioning

confidence: 99%

Principles of cerebral perfusion imaging by bolus tracking

Østergaard

2005

Magnetic Resonance Imaging

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248

215

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The principles of cerebral perfusion imaging by the method of dynamic susceptibility contrast magnetic resonance imaging (DSC‐MRI) (bolus tracking) are described. The MRI signals underlying DSC‐MRI are discussed. Tracer kinetics procedures are defined to calculate images of cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT). Two general categories of numerical procedures are reviewed for deriving CBF from the residue function. Procedures that involve deconvolution, such as Fourier deconvolution or singular value decomposition (SVD), are classified as model‐independent methods because they do not require a model of the microvascular hemodynamics. Those methods in principle also yield a measure of the tissue impulse response function and the residue function, from which microvascular hemodynamics can be characterized. The second category of methods is the model‐dependent methods, which use models of tracer transport and retention in the microvasculature. These methods do not yield independent measures of the residue function and may introduce bias when the physiology does not follow the model. Statistical methods are sometimes used, which involve treating the residue function as a deconvolution kernel and optimizing (fitting) the kernel from the experimental data using procedures such as maximum likelihood. Finally, other hemodynamic indices that can be measured from DSC‐MRI data are described. J. Magn. Reson. Imaging 2005. © 2005 Wiley‐Liss, Inc.

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“…For CBF and MTT measurement, the AIF must be deconvolved from the tissue signal curve (2,4,5) to yield the tissue residue function. The CBV is typically calculated as the ratio of the area under the tissue signal curve divided by the area under the AIF (2,6), or as the area under the residue function (7). Random and systematic errors in the AIF can be the largest sources of error in the derived measurements of CBV, CBF, and MTT (e.g., see Refs.…”

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confidence: 99%

Arterial input functions for dynamic susceptibility contrast MRI: Requirements and signal options

Conturo

Akbudak

Kotys

et al. 2005

Magnetic Resonance Imaging

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Cerebral perfusion imaging using dynamic susceptibility contrast (DSC) has been the subject of considerable research and shows promise for basic science and clinical use. In DSC, the MRI signals in brain tissue and feeding arteries are monitored dynamically in response to a bolus injection of paramagnetic agents, such as gadolinium (Gd) chelates. DSC has the potential to allow quantitative imaging of parameters such as cerebral blood flow (CBF) with a high signal‐to‐noise ratio (SNR) in a short scan time; however, quantitation depends critically on accurate and precise measurement of the arterial input function (AIF). We discuss many requirements and factors that make it difficult to measure the AIF. The AIF signal should be linear with respect to Gd concentration, convertible to the same concentration scale as the tissue signal, and independent of hematocrit. Complicated relationships between signal and concentration can violate these requirements. The additional requirements of a high SNR and high spatial/temporal resolution are technically challenging. AIF measurements can also be affected by signal saturation and aliasing, as well as dispersion/delay between the AIF sampling site and the tissue. We present new in vivo preliminary results for magnitude‐based (ΔR2*) and phase‐based (Δϕ) AIF measurements that show a linearity advantage of phase, and a disparity in the scaling of Δϕ AIFs, ΔR2* AIFs, and ΔR2* tissue curves. Finally, we discuss issues related to the choice of AIF signal for quantitative perfusion imaging. J. Magn. Reson. Imaging 2005. © 2005 Wiley‐Liss, Inc.

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Evaluation of four postprocessing methods for determination of cerebral blood volume and mean transit time by dynamic susceptibility contrast imaging

Cited by 70 publications

References 27 publications

Microvessel density estimation in the human brain by means of dynamic contrast‐enhanced echo‐planar imaging

Microvessel density estimation in the human brain by means of dynamic contrast‐enhanced echo‐planar imaging

Principles of cerebral perfusion imaging by bolus tracking

Arterial input functions for dynamic susceptibility contrast MRI: Requirements and signal options

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