Comparison of cerebral blood flow measurement with [O-15]-water positron emission tomography and arterial spin labeling magnetic resonance imaging: A systematic review
JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM
2016; 36 (5): 842-861
Chronic kidney disease, cerebral blood flow, and white matter volume in hypertensive adults
2016; 86 (13): 1208-1216
Chronic kidney disease, cerebral blood flow, and white matter volume in hypertensive adults.
2016; 86 (13): 1208-1216
Noninvasive imaging of cerebral blood flow provides critical information to understand normal brain physiology as well as to identify and manage patients with neurological disorders. To date, the reference standard for cerebral blood flow measurements is considered to be positron emission tomography using injection of the [(15)O]-water radiotracer. Although [(15)O]-water has been used to study brain perfusion under normal and pathological conditions, it is not widely used in clinical settings due to the need for an on-site cyclotron, the invasive nature of arterial blood sampling, and experimental complexity. As an alternative, arterial spin labeling is a promising magnetic resonance imaging technique that magnetically labels arterial blood as it flows into the brain to map cerebral blood flow. As arterial spin labeling becomes more widely adopted in research and clinical settings, efforts have sought to standardize the method and validate its cerebral blood flow values against positron emission tomography-based cerebral blood flow measurements. The purpose of this work is to critically review studies that performed both [(15)O]-water positron emission tomography and arterial spin labeling to measure brain perfusion, with the aim of better understanding the accuracy and reproducibility of arterial spin labeling relative to the positron emission tomography reference standard.
View details for DOI 10.1177/0271678X16636393
View details for Web of Science ID 000375261800002
View details for PubMedID 26945019
Arterial cerebral blood volume-weighted functional MRI using pseudocontinuous arterial spin tagging (AVAST).
Magnetic resonance in medicine
2015; 73 (3): 1053-1064
To determine the relation between markers of kidney disease-estimated glomerular filtration rate (eGFR) and urine albumin to creatinine ratio (UACR)-with cerebral blood flow (CBF) and white matter volume (WMV) in hypertensive adults.We used baseline data collected from 665 nondiabetic hypertensive adults aged ≥50 years participating in the Systolic Blood Pressure Intervention Trial (SPRINT). We used arterial spin labeling to measure CBF and structural 3T images to segment tissue into normal and abnormal WMV. We used quantile regression to estimate the association between eGFR and UACR with CBF and abnormal WMV, adjusting for sociodemographic and clinical characteristics.There were 218 participants (33%) with eGFR <60 mL/min/1.73 m(2) and 146 participants (22%) with UACR ≥30 mg/g. Reduced eGFR was independently associated with higher adjusted median CBF, but not with abnormal WMV. Conversely, in adjusted analyses, there was a linear independent association between UACR and larger abnormal WMV, but not with CBF. Compared to participants with neither marker of CKD (eGFR ≥60 mL/min/1.73 m(2) and UACR <30 mg/g), median CBF was 5.03 mL/100 g/min higher (95% confidence interval [CI] 0.78, 9.29) and abnormal WMV was 0.63 cm(3) larger (95% CI 0.08, 1.17) among participants with both markers of CKD (eGFR <60 mL/min/1.73 m(2) and UACR ≥30 mg/g).Among nondiabetic hypertensive adults, reduced eGFR was associated with higher CBF and higher UACR was associated with larger abnormal WMV.
View details for DOI 10.1212/WNL.0000000000002527
View details for PubMedID 26920359
Noncontrast Mapping of Arterial Delay and Functional Connectivity Using Resting-State Functional MRI: A Study in Moyamoya Patients
JOURNAL OF MAGNETIC RESONANCE IMAGING
2015; 41 (2): 424-430
Neurovascular regulation, including responses to neural activation that give rise to the blood oxygenation level-dependent (BOLD) effect, occurs mainly at the arterial and arteriolar level. The purpose of this study is to develop a framework for fast imaging of arterial cerebral blood volume (aCBV) signal suitable for functional imaging studies.A variant of the pseudocontinuous arterial spin tagging technique was developed in order to achieve a contrast that depends on aCBV with little contamination from perfusion signal by taking advantage of the kinetics of the tag through the vasculature. This technique tailors the tagging duration and repetition time for each subject. The proposed technique, called AVAST, is compared empirically with BOLD imaging and standard (perfusion-weighted) arterial spin labeling (ASL) technique, in a motor-visual activation paradigm.The average Z-scores in the activated area obtained over all the subjects were 4.25, 5.52, and 7.87 for standard ASL, AVAST, and BOLD techniques, respectively. The aCBV contrast obtained from AVAST provided 80% higher average signal-to-noise ratio and 95% higher average contrast-to-noise ratio compared with that of the standard ASL measurements.AVAST exhibits improved activation detection sensitivity and temporal resolution over the standard ASL technique, in functional MRI experiments, while preserving its quantitative nature and statistical advantages. AVAST particularly could be useful in clinical studies of pathological conditions, longitudinal studies of cognitive function, and studies requiring sustained periods of the condition. Magn Reson Med 73:1053-1064, 2015. © 2014 Wiley Periodicals, Inc.
View details for DOI 10.1002/mrm.25220
View details for PubMedID 24753198
Spontaneous BOLD Signal Fluctuations in Young Healthy Subjects and Elderly Patients with Chronic Kidney Disease
2014; 9 (3)
Spontaneous BOLD signal fluctuations in young healthy subjects and elderly patients with chronic kidney disease.
2014; 9 (3)
To investigate if delays in resting-state spontaneous fluctuations of the BOLD (sfBOLD) signal can be used to create maps similar to time-to-maximum of the residue function (Tmax) in Moyamoya patients and to determine whether sfBOLD delays affect the results of brain connectivity mapping.Ten patients were scanned at 3 Tesla using a gradient-echo echo planar imaging sequence for sfBOLD imaging. Cross correlation analysis was performed between each brain voxel signal and a reference signal comprised of either the superior sagittal sinus (SSS) or whole brain (WB) average time course. sfBOLD delay maps were created based on the time shift necessary to maximize the correlation coefficient, and compared with dynamic susceptibility contrast Tmax maps. Standard and time-shifted resting-state BOLD connectivity analyses of the default mode network were compared.Good linear correlations were found between sfBOLD delays and Tmax using the SSS as reference (r(2) = 0.8, slope = 1.4, intercept = -4.6) or WB (r(2) = 0.7, slope = 0.8, intercept = -3.2). New nodes of connectivity were found in delayed regions when accounting for delays in the analysis.Resting-state sfBOLD imaging can create delay maps similar to Tmax maps without the use of contrast agents in Moyamoya patients. Accounting for these delays may affect the results of functional connectivity maps.J. Magn. Reson. Imaging 2014. © 2014 Wiley Periodicals, Inc.
View details for DOI 10.1002/jmri.24558
View details for Web of Science ID 000348850600018
B0 field inhomogeneity considerations in pseudo-continuous arterial spin labeling (pCASL): effects on tagging efficiency and correction strategy
NMR IN BIOMEDICINE
2011; 24 (10): 1202-1209
Spontaneous fluctuations in blood oxygenation level-dependent (BOLD) images are the basis of resting-state fMRI and frequently used for functional connectivity studies. However, there may be intrinsic information in the amplitudes of these fluctuations. We investigated the possibility of using the amplitude of spontaneous BOLD signal fluctuations as a biomarker for cerebral vasomotor reactivity. We compared the coefficient of variation (CV) of the time series (defined as the temporal standard deviation of the time series divided by the mean signal intensity) in two populations: 1) Ten young healthy adults and 2) Ten hypertensive elderly subjects with chronic kidney disease (CKD). We found a statistically significant increase (P<0.01) in the CV values for the CKD patients compared with the young healthy adults in both gray matter (GM) and white matter (WM). The difference was independent of the exact segmentation method, became more significant after correcting for physiological signals using RETROICOR, and mainly arose from very low frequency components of the BOLD signal fluctuation (f<0.025 Hz). Furthermore, there was a strong relationship between WM and GM signal fluctuation CV's (R2 = 0.87) in individuals, with a ratio of about 1∶3. These results suggest that amplitude of the spontaneous BOLD signal fluctuations may be used to assess the cerebrovascular reactivity mechanisms and provide valuable information about variations with age and different disease states.
View details for DOI 10.1371/journal.pone.0092539
View details for PubMedID 24651703
Real-Time Functional MRI Using Pseudo-Continuous Arterial Spin Labeling
MAGNETIC RESONANCE IN MEDICINE
2011; 65 (6): 1570-1577
Pseudo-continuous arterial spin labeling (pCASL) is a very powerful technique to measure cerebral perfusion, which circumvents the problems affecting other continuous arterial spin labeling schemes, such as magnetization transfer and duty cycle. However, some variability in the tagging efficiency of the pCASL technique has been reported. This article investigates the effect of B(0) field inhomogeneity on the tagging efficiency of the pCASL pulse sequence as a possible cause of this variability. Both theory and simulated data predict that the efficiency of pseudo-continuous labeling pulses can be degraded in the presence of off-resonance effects. These findings are corroborated by human in vivo measurements of tagging efficiency. On the basis of this theoretical framework, a method utilizing B(0) field map information is proposed to correct for the possible loss in tagging efficiency of the pCASL pulse sequence. The efficiency of the proposed correction method is evaluated using numerical simulations and in vivo implementation. The data show that the proposed method can effectively recover the lost tagging efficiency and signal-to-noise ratio of pCASL caused by off-resonance effects.
View details for DOI 10.1002/nbm.1675
View details for Web of Science ID 000298745600003
View details for PubMedID 21387447
Quantitative analysis of arterial spin labeling FMRI data using a general linear model
MAGNETIC RESONANCE IMAGING
2010; 28 (7): 919-927
The first implementation of real-time acquisition and analysis of arterial spin labeling-based functional magnetic resonance imaging time series is presented in this article. The implementation uses a pseudo-continuous labeling scheme followed by a spiral k-space acquisition trajectory. Real-time reconstruction of the images, preprocessing, and regression analysis of the functional magnetic resonance imaging data were implemented on a laptop computer interfaced with the MRI scanner. The method allows the user to track the current raw data, subtraction images, and the cumulative t-statistic map overlaid on a cumulative subtraction image. The user is also able to track the time course of individual time courses and interactively selects a region of interest as a nuisance covariate. The pulse sequence allows the user to adjust acquisition and labeling parameters while observing their effect on the image within two successive pulse repetition times. This method is demonstrated by two functional imaging experiments: a simultaneous finger-tapping and visual stimulation paradigm, and a bimanual finger-tapping task.
View details for DOI 10.1002/mrm.22922
View details for Web of Science ID 000291115500007
View details for PubMedID 21446035
Functional magnetic resonance imaging activation detection: Fuzzy cluster analysis in wavelet and multiwavelet domains
JOURNAL OF MAGNETIC RESONANCE IMAGING
2005; 22 (3): 381-389
Arterial spin labeling techniques can yield quantitative measures of perfusion by fitting a kinetic model to difference images (tagged-control). Because of the noisy nature of the difference images investigators typically average a large number of tagged versus control difference measurements over long periods of time. This averaging requires that the perfusion signal be at a steady state and not at the transitions between active and baseline states in order to quantitatively estimate activation induced perfusion. This can be an impediment for functional magnetic resonance imaging task experiments. In this work, we introduce a general linear model (GLM) that specifies Blood Oxygenation Level Dependent (BOLD) effects and arterial spin labeling modulation effects and translate them into meaningful, quantitative measures of perfusion by using standard tracer kinetic models. We show that there is a strong association between the perfusion values using our GLM method and the traditional subtraction method, but that our GLM method is more robust to noise.
View details for DOI 10.1016/j.mri.2010.03.035
View details for Web of Science ID 000281046100001
View details for PubMedID 20456889
Controlling the false positive rate in fuzzy clustering using randomization: application to fMRI activation detection
MAGNETIC RESONANCE IMAGING
2004; 22 (5): 631-638
To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents.Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared.The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis.More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features.
View details for DOI 10.1002/jmri.20392
View details for Web of Science ID 000231747100008
View details for PubMedID 16104010
Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false-positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false-positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. Controlling the false-positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this article, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space. In both cases, the HRF-based feature space provides a greater sensitivity compared to the cross-correlation feature space and conventional cross-correlation analysis. Application of the proposed method to finger-tapping fMRI data, using HRF-based feature space, detected activation in sub-cortical regions, whereas both of the FCM with cross-correlation feature space and the conventional cross-correlation method failed to detect them.
View details for DOI 10.1016/j.mri.2004.01.035
View details for Web of Science ID 000221688400005
View details for PubMedID 15172056