Improving dynamic susceptibility contrast MRI measurement of quantitative cerebral blood flow using corrections for partial volume and nonlinear contrast relaxivity: A xenon computed tomographic comparative study

Purpose

To test whether dynamic susceptibility contrast MRI‐based CBF measurements are improved with arterial input function (AIF) partial volume (PV) and nonlinear contrast relaxivity correction, using a gold‐standard CBF method, xenon computed tomography (xeCT).

Materials and Methods

Eighteen patients with cerebrovascular disease underwent xeCT and MRI within 36 h. PV was measured as the ratio of the area under the AIF and the venous output function (VOF) concentration curves. A correction was applied to account for the nonlinear relaxivity of bulk blood (BB). Mean CBF was measured with both techniques and regression analyses both within and between patients were performed.

Results

Mean xeCT CBF was 43.3 ± 13.7 mL/100g/min (mean ± SD). BB correction decreased CBF by a factor of 4.7 ± 0.4, but did not affect precision. The least‐biased CBF measurement was with BB but without PV correction (45.8 ± 17.2 mL/100 g/min, coefficient of variation [COV] = 32%). Precision improved with PV correction, although absolute CBF was mildly underestimated (34.3 ± 10.8 mL/100 g/min, COV = 27%). Between patients correlation was moderate even with both corrections (R = 0.53).

Conclusion

Corrections for AIF PV and nonlinear BB relaxivity improve bolus MRI‐based CBF maps. However, there remain challenges given the moderate between‐patient correlation, which limit diagnostic confidence of such measurements in individual patients. J. Magn. Reson. Imaging 2009;30:743–752. © 2009 Wiley‐Liss, Inc.     

Absolute quantification of cerebral blood flow in normal volunteers: correlation between Xe-133 SPECT and dynamic susceptibility contrast MRI

PURPOSE: To compare absolute cerebral blood flow (CBF) estimates obtained by dynamic susceptibility contrast MRI (DSC-MRI) and Xe-133 SPECT. MATERIALS AND METHODS: CBF was measured in 20 healthy volunteers using DSC-MRI at 3T and Xe-133 SPECT. DSC-MRI was accomplished by gradient-echo EPI and CBF was calculated using a time-shift-insensitive deconvolution algorithm and regional arterial input functions (AIFs). To improve the reproducibility of AIF registration the time integral was rescaled by use of a venous output function. In the Xe-133 SPECT experiment, Xe-133 gas was inhaled over 8 minutes and CBF was calculated using a biexponential analysis. RESULTS: The average whole-brain CBF estimates obtained by DSC-MRI and Xe-133 SPECT were 85 +/- 23 mL/(min 100 g) and 40 +/- 8 mL/(min 100 g), respectively (mean +/- SD, n = 20). The linear CBF relationship between the two modalities showed a correlation coefficient of r = 0.76 and was described by the equation CBF(MRI) = 2.4 . CBF(Xe)-7.9 (CBF in units of mL/(min 100 g)). CONCLUSION: A reasonable positive linear correlation between MRI-based and SPECT-based CBF estimates was observed after AIF time-integral correction. The use of DSC-MRI typically results in overestimated absolute perfusion estimates and the present study indicates that this trend is further enhanced by the use of high magnetic field strength (3T).     

Dynamic susceptibility contrast MRI with localized arterial input functions

Compared to gold‐standard measurements of cerebral perfusion with positron emission tomography using H₂[¹⁵O] tracers, measurements with dynamic susceptibility contrast MR are more accessible, less expensive, and less invasive. However, existing methods for analyzing and interpreting data from dynamic susceptibility contrast MR have characteristic disadvantages that include sensitivity to incorrectly modeled delay and dispersion in a single, global arterial input function. We describe a model of tissue microcirculation derived from tracer kinetics that estimates for each voxel a unique, localized arterial input function. Parameters of the model were estimated using Bayesian probability theory and Markov‐chain Monte Carlo, circumventing difficulties arising from numerical deconvolution. Applying the new method to imaging studies from a cohort of 14 patients with chronic, atherosclerotic, occlusive disease showed strong correlations between perfusion measured by dynamic susceptibility contrast MR with localized arterial input function and perfusion measured by quantitative positron emission tomography with H₂[¹⁵O]. Regression to positron emission tomography measurements enabled conversion of dynamic susceptibility contrast MR to a physiologic scale. Regression analysis for localized arterial input function gave estimates of a scaling factor for quantitation that described perfusion accurately in patients with substantial variability in hemodynamic impairment. Magn Reson Med 63:1305–1314, 2010. © 2010 Wiley‐Liss, Inc.     

Phase‐based arterial input function measurements for dynamic susceptibility contrast MRI

In dynamic susceptibility contrast perfusion MRI, arterial input function (AIF) measurements using the phase of the MR signal are traditionally performed inside an artery. However, phase‐based AIF selection is also feasible in tissue surrounding an artery such as the middle cerebral artery, which runs approximately perpendicular to B₀ since contrast agents also induce local field changes in tissue surrounding the artery. The aim of this study was to investigate whether phase‐based AIF selection is better performed in tissue just outside the middle cerebral artery than inside the artery. Additionally, phase‐based AIF selection was compared to magnitude‐based AIF selection. Both issues were studied theoretically and using numerical simulations, producing results that were validated using phantom experiments. Finally, an in vivo experiment was performed to illustrate the feasibility of phase‐based AIF selection. Three main findings are presented: first, phase‐based AIF selections are better made in tissue outside the middle cerebral artery, rather than within the middle cerebral artery, since in the latter approach partial‐volume effects affect the shape of the estimated AIF. Second, optimal locations for phase‐based AIF selection are similar for different clinical dynamic susceptibility contrast MRI sequences. Third, phase‐based AIF selection allows more locations in tissue to be chosen that show the correct AIF than does magnitude‐based AIF selection. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.     

Robust dynamic susceptibility contrast MR perfusion using 4D nonlinear noise filters

PURPOSE: To investigate if 4D (simultaneous space and time) nonlinear filtering techniques can produce more robust cerebral blood flow (CBF) estimates by reducing noise in acquired dynamic susceptibility contrast (DSC) MR perfusion data. MATERIALS AND METHODS: A digital anthropomorphic brain perfusion phantom was constructed to analyze filter performance by: 1) deriving anthropomorphic tissue volume fractions from a human subject and 2) simulating DSC-MR perfusion signals for voxels with mixed tissue for various signal-to-noise ratios (SNRs). DSC-MR data for 11 acute ischemic stroke patients were also acquired at 3T. CBF maps cross-calibrated so that normal white matter CBF was 22 mL/minute/100 g were produced from DSC-MR data without filtering and from 4D-Gaussian and 4D-bilateral noise-filtered DSC-MR data. RESULTS: The nonlinear 4D-bilateral filter yielded the lowest CBF root-mean square error (RMSE) in the phantom experiments with noise (average RMSE across all tissues regions for no filtering, 4D-Gaussian, and 4D-bilateral was 5.3 mL/minute/100 g, 6.2 mL/minute/100 g, and 4.0 mL/minute/100 g, respectively) and had the best image quality in both the phantom and patient data. CONCLUSION: Nonlinear 4D noise filters are better suited to the 4D nature of DSC-MR data. Linear spatial filters are not appropriate and can produce larger CBF errors than without filtering.     

Mr contrast due to intravascular magnetic susceptibility perturbations

A particularly powerful paradigm for functional MR imaging of microvascular hemodynamics incorporates paramagnetic materials that create significant image contrast. These include exogenous (lanthanide chelates) and endogenous (de-oxygenated hemoglobin) agents for mapping cerebral blood volume and neuronal activity, respectively. Accurate interpretation of these maps requires an understanding of the bio-physics of susceptibility-based image contrast. The authors developed a novel Monte Carlo model with which the authors quantified the relationship between microscopic tissue parameters, NMR imaging parameters, and susceptibility contrast in vivo. The authors found vascular permeability to water and the flow of erythrocytes to be relatively unimportant contributors to susceptibility-induced ΔR2. However, pulse sequence, echo time, and concentration of contrast agent have profound effects on the vessel size dependence of ΔR2. For a model vasculature containing both capillaries and venules, the authors predicted a linear volume fraction dependence for physiological volume changes based on recruitment and dilation, and a concentration dependence that is nonlinear and pulse sequence dependent. Using the model, the authors demonstrated that spin echo functional images have greater microvascular sensitivity than gradient echo images, and that the specifics of the volume fraction and concentration dependence of transverse relaxivity change should allow for robust mapping of relative blood volume. The authors also demonstrated excellent agreement between the predictions of their model and experimental data obtained from the serial injection of superparamagnetic contrast agent in a rat model.     

On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI.

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 T2 and T2* 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. The noise was excluded in the simulation in order to analyze the ultimate accuracy of the method. It is demonstrated that the relationship between the contrast agent concentration and the associated changes in the transverse relaxation rate shows essentially different forms in studied tissue and in the reference artery. This effect results in systematic deviations of the measured blood flow, blood volume, and the residue function obtained with conventional processing from their true values. The error depends on the microvascular composition, the properties of the contrast agent, and the weights of the various compartments in the total signal. The results show that dynamic susceptibility contrast MRI can reach the goal of absolute perfusion quantification only with additional input from measurements of the microvascular architecture. Alternatively, the method can be used to provide such information if the perfusion is quantified by another modality.     

Transient crossed cerebellar diaschisis secondary to cerebral hyperperfusion following carotid endarterectomy

We report a case of transient crossed cerebellar diaschisis secondary to cerebral hyperperfusion following carotid endarterectomy. The appearance of crossed cerebellar diaschisis under the presence of cerebral hyperperfusion may suggest the development of hyperperfusion syndrome.     

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

Four different postprocessing methods to determine cerebral blood volume (CBV) and contrast agent mean transit time (MTT) by dynamic susceptibility contrast (DSC) MRI were compared. CBV was determined by two different methods that integrate tracer concentration-time curves numerically and by two other methods that take recirculation into account. For the two methods that use numerical integration, one method cuts the integration after the first pass while the other method integrates over the whole time series. For the two methods that account for recirculation, one method uses a gamma-variate fit, whereas the other method utilizes tissue impulse response. All four methods determine MTT as the ratio of CBV and cerebral blood flow (CBF). In each case, CBF was obtained as the height of the impulse response obtained by deconvolving the tissue concentration-time curves with a noninvasively determined arterial input function. Monte Carlo simulations were performed to determine the reliability of the methods and the validity of the simulations was supported by observation of similar trends in 13 acute stroke patients. The method of determining CBV and subsequently MTT was found to affect the measured value especially in areas where MTT is prolonged, but had no apparent effect on the visually determined hypoperfusion volumes.     

Determination of arterial input function using fuzzy clustering for quantification of cerebral blood flow with dynamic susceptibility contrast-enhanced MR imaging

An accurate determination of the arterial input function (AIF) is necessary for quantification of cerebral blood flow (CBF) using dynamic susceptibility contrast-enhanced magnetic resonance imaging. In this study, we developed a method for obtaining the AIF automatically using fuzzy c-means (FCM) clustering. The validity of this approach was investigated with computer simulations. We found that this method can automatically extract the AIF, even under very noisy conditions, e.g., when the signal-to-noise ratio is 2. The simulation results also indicated that when using a manual drawing of a region of interest (ROI) (manual ROI method), the contamination of surrounding pixels (background) into ROI caused considerable overestimation of CBF. We applied this method to six subjects and compared it with the manual ROI method. The CBF values, calculated using the AIF obtained using the manual ROI method [CBF(manual)], were significantly higher than those obtained with FCM clustering [CBF(fuzzy)]. This may have been due to the contamination of non-arterial pixels into the manually drawn ROI, as suggested by simulation results. The ratio of CBF(manual) to CBF(fuzzy) ranged from 0.99-1.83 [1.31 +/- 0.26 (mean +/- SD)]. In conclusion, our FCM clustering method appears promising for determination of AIF because it allows automatic, rapid and accurate extraction of arterial pixels. J. Magn. Reson. Imaging 2001;13:797-806.     

The impact of partial-volume effects in dynamic susceptibility contrast magnetic resonance perfusion imaging

PURPOSE: To demonstrate the degree of the cerebral blood flow (CBF) estimation bias that could arise from distortion of the arterial input function (AIF) as a result of partial-volume effects (PVEs) in dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI). MATERIALS AND METHODS: A model of the volume fraction an artery occupies in a voxel was devised, and a mathematical relationship between the amount of PVE and the measured baseline MR signal intensity was derived. Based on this model, simulation studies were performed to assess the impact of PVE on CBF. Furthermore, the effectiveness of linear PVE compensation approaches on the concentration function was investigated. RESULTS: Simulation results showed a nonlinear relationship between PVE and the resulting CBF measurement error. In addition to AIF underestimation, PVE also causes distortions of AIF frequency characteristics, leading to CBF errors varying with mean transit time (MTT). An uncorrected AIF measured at a voxel with a partial-volume fraction of 相似文献