Whole‐brain perfusion imaging with balanced steady‐state free precession arterial spin labeling

Recently, balanced steady‐state free precession (bSSFP) readout has been proposed for arterial spin labeling (ASL) perfusion imaging to reduce susceptibility artifacts at a relatively high spatial resolution and signal‐to‐noise ratio (SNR). However, the main limitation of bSSFP‐ASL is the low spatial coverage. In this work, methods to increase the spatial coverage of bSSFP‐ASL are proposed for distortion‐free, high‐resolution, whole‐brain perfusion imaging. Three strategies of (i) segmentation, (ii) compressed sensing (CS) and (iii) a hybrid approach combining the two methods were tested to increase the spatial coverage of pseudo‐continuous ASL (pCASL) with three‐dimensional bSSFP readout. The spatial coverage was increased by factors of two, four and six using each of the three approaches, whilst maintaining the same total scan time (5.3 min). The number of segments and/or CS acceleration rate (R) correspondingly increased to maintain the same bSSFP readout time (1.2 s). The segmentation approach allowed whole‐brain perfusion imaging for pCASL‐bSSFP with no penalty in SNR and/or total scan time. The CS approach increased the spatial coverage of pCASL‐bSSFP whilst maintaining the temporal resolution, with minimal impact on the image quality. The hybrid approach provided compromised effects between the two methods. Balanced SSFP‐based ASL allows the acquisition of perfusion images with wide spatial coverage, high spatial resolution and SNR, and reduced susceptibility artifacts, and thus may become a good choice for clinical and neurological studies. Copyright © 2015 John Wiley & Sons, Ltd.     

Optimization of 4D vessel‐selective arterial spin labeling angiography using balanced steady‐state free precession and vessel‐encoding

Vessel‐selective dynamic angiograms provide a wealth of useful information about the anatomical and functional status of arteries, including information about collateral flow and blood supply to lesions. Conventional x‐ray techniques are invasive and carry some risks to the patient, so non‐invasive alternatives are desirable. Previously, non‐contrast dynamic MRI angiograms based on arterial spin labeling (ASL) have been demonstrated using both spoiled gradient echo (SPGR) and balanced steady‐state free precession (bSSFP) readout modules, but no direct comparison has been made, and bSSFP optimization over a long readout period has not been fully explored. In this study bSSFP and SPGR are theoretically and experimentally compared for dynamic ASL angiography. Unlike SPGR, bSSFP was found to have a very low ASL signal attenuation rate, even when a relatively large flip angle and short repetition time were used, leading to a threefold improvement in the measured signal‐to‐noise ratio (SNR) efficiency compared with SPGR. For vessel‐selective applications, SNR efficiency can be further improved over single‐artery labeling methods by using a vessel‐encoded pseudo‐continuous ASL (VEPCASL) approach. The combination of a VEPCASL preparation with a time‐resolved bSSFP readout allowed the generation of four‐dimensional (4D; time‐resolved three‐dimensional, 3D) vessel‐selective cerebral angiograms in healthy volunteers with 59 ms temporal resolution. Good quality 4D angiograms were obtained in all subjects, providing comparable structural information to 3D time‐of‐flight images, as well as dynamic information and vessel selectivity, which was shown to be high. A rapid 1.5 min dynamic two‐dimensional version of the sequence yielded similar image features and would be suitable for a busy clinical protocol. Preliminary experiments with bSSFP that included the extracranial vessels showed signal loss in regions of poor magnetic field homogeneity. However, for intracranial vessel‐selective angiography, the proposed bSSFP VEPCASL sequence is highly SNR efficient and could provide useful information in a range of cerebrovascular diseases. © 2016 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.     

Characterizing pulmonary blood flow distribution measured using arterial spin labeling

The arterial spin labeling (ASL) method provides images in which, ideally, the signal intensity of each image voxel is proportional to the local perfusion. For studies of pulmonary perfusion, the relative dispersion (RD, standard deviation/mean) of the ASL signal across a lung section is used as a reliable measure of flow heterogeneity. However, the RD of the ASL signals within the lung may systematically differ from the true RD of perfusion because the ASL image also includes signals from larger vessels, which can reflect the blood volume rather than blood flow if the vessels are filled with tagged blood during the imaging time. Theoretical studies suggest that the pulmonary vasculature exhibits a lognormal distribution for blood flow and thus an appropriate measure of heterogeneity is the geometric standard deviation (GSD). To test whether the ASL signal exhibits a lognormal distribution for pulmonary blood flow, determine whether larger vessels play an important role in the distribution, and extract physiologically relevant measures of heterogeneity from the ASL signal, we quantified the ASL signal before and after an intervention (head‐down tilt) in six subjects. The distribution of ASL signal was better characterized by a lognormal distribution than a normal distribution, reducing the mean squared error by 72% (p < 0.005). Head‐down tilt significantly reduced the lognormal scale parameter (p = 0.01) but not the shape parameter or GSD. The RD increased post‐tilt and remained significantly elevated (by 17%, p < 0.05). Test case results and mathematical simulations suggest that RD is more sensitive than the GSD to ASL signal from tagged blood in larger vessels, a probable explanation of the change in RD without a statistically significant change in GSD. This suggests that the GSD is a useful measure of pulmonary blood flow heterogeneity with the advantage of being less affected by the ASL signal from tagged blood in larger vessels. Copyright © 2009 John Wiley & Sons, Ltd.     

Arterial spin labeling‐fast imaging with steady‐state free precession (ASL‐FISP): a rapid and quantitative perfusion technique for high‐field MRI

Arterial spin labeling (ASL) is a valuable non‐contrast perfusion MRI technique with numerous clinical applications. Many previous ASL MRI studies have utilized either echo‐planar imaging (EPI) or true fast imaging with steady‐state free precession (true FISP) readouts, which are prone to off‐resonance artifacts on high‐field MRI scanners. We have developed a rapid ASL‐FISP MRI acquisition for high‐field preclinical MRI scanners providing perfusion‐weighted images with little or no artifacts in less than 2 s. In this initial implementation, a flow‐sensitive alternating inversion recovery (FAIR) ASL preparation was combined with a rapid, centrically encoded FISP readout. Validation studies on healthy C57/BL6 mice provided consistent estimation of in vivo mouse brain perfusion at 7 and 9.4 T (249 ± 38 and 241 ± 17 mL/min/100 g, respectively). The utility of this method was further demonstrated in the detection of significant perfusion deficits in a C57/BL6 mouse model of ischemic stroke. Reasonable kidney perfusion estimates were also obtained for a healthy C57/BL6 mouse exhibiting differential perfusion in the renal cortex and medulla. Overall, the ASL‐FISP technique provides a rapid and quantitative in vivo assessment of tissue perfusion for high‐field MRI scanners with minimal image artifacts. Copyright © 2014 John Wiley & Sons, Ltd.     

Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI

Arterial spin-labeling (ASL) perfusion MRI is a non-invasive method for quantifying cerebral blood flow (CBF). Standard ASL CBF calibration mainly relies on pair-wise subtraction of the spin-labeled images and controls images at each voxel separately, ignoring the abundant spatial correlations in ASL data. To address this issue, we previously proposed a multivariate support vector machine (SVM) learning-based algorithm for ASL CBF quantification (SVMASLQ). But the original SVMASLQ was designed to do CBF quantification for all image voxels simultaneously, which is not ideal for considering local signal and noise variations. To fix this problem, we here in this paper extended SVMASLQ into a patch-wise method by using a patch-wise classification kernel. At each voxel, an image patch centered at that voxel was extracted from both the control images and labeled images, which was then input into SVMASLQ to find the corresponding patch of the surrogate perfusion map using a non-linear SVM classifier. Those patches were eventually combined into the final perfusion map. Method evaluations were performed using ASL data from 30 young healthy subjects. The results showed that the patch-wise SVMASLQ increased perfusion map SNR by 6.6% compared to the non-patch-wise SVMASLQ.     

Partial volume correction of brain perfusion estimates using the inherent signal data of time‐resolved arterial spin labeling

Quantitative perfusion MRI based on arterial spin labeling (ASL) is hampered by partial volume effects (PVEs), arising due to voxel signal cross‐contamination between different compartments. To address this issue, several partial volume correction (PVC) methods have been presented. Most previous methods rely on segmentation of a high‐resolution T₁‐weighted morphological image volume that is coregistered to the low‐resolution ASL data, making the result sensitive to errors in the segmentation and coregistration. In this work, we present a methodology for partial volume estimation and correction, using only low‐resolution ASL data acquired with the QUASAR sequence. The methodology consists of a T₁‐based segmentation method, with no spatial priors, and a modified PVC method based on linear regression. The presented approach thus avoids prior assumptions about the spatial distribution of brain compartments, while also avoiding coregistration between different image volumes. Simulations based on a digital phantom as well as in vivo measurements in 10 volunteers were used to assess the performance of the proposed segmentation approach. The simulation results indicated that QUASAR data can be used for robust partial volume estimation, and this was confirmed by the in vivo experiments. The proposed PVC method yielded probable perfusion maps, comparable to a reference method based on segmentation of a high‐resolution morphological scan. Corrected gray matter (GM) perfusion was 47% higher than uncorrected values, suggesting a significant amount of PVEs in the data. Whereas the reference method failed to completely eliminate the dependence of perfusion estimates on the volume fraction, the novel approach produced GM perfusion values independent of GM volume fraction. The intra‐subject coefficient of variation of corrected perfusion values was lowest for the proposed PVC method. As shown in this work, low‐resolution partial volume estimation in connection with ASL perfusion estimation is feasible, and provides a promising tool for decoupling perfusion and tissue volume. Copyright © 2014 John Wiley & Sons, Ltd.     

Evaluation of segmented 3D acquisition schemes for whole‐brain high‐resolution arterial spin labeling at 3 T

Recent technical developments have significantly increased the signal‐to‐noise ratio (SNR) of arterial spin labeled (ASL) perfusion MRI. Despite this, typical ASL acquisitions still employ large voxel sizes. The purpose of this work was to implement and evaluate two ASL sequences optimized for whole‐brain high‐resolution perfusion imaging, combining pseudo‐continuous ASL (pCASL), background suppression (BS) and 3D segmented readouts, with different in‐plane k‐space trajectories. Identical labeling and BS pulses were implemented for both sequences. Two segmented 3D readout schemes with different in‐plane trajectories were compared: Cartesian (3D GRASE) and spiral (3D RARE Stack‐Of‐Spirals). High‐resolution perfusion images (2 × 2 × 4 mm³) were acquired in 15 young healthy volunteers with the two ASL sequences at 3 T. The quality of the perfusion maps was evaluated in terms of SNR and gray‐to‐white matter contrast. Point‐spread‐function simulations were carried out to assess the impact of readout differences on the effective resolution. The combination of pCASL, in‐plane segmented 3D readouts and BS provided high‐SNR high‐resolution ASL perfusion images of the whole brain. Although both sequences produced excellent image quality, the 3D RARE Stack‐Of‐Spirals readout yielded higher temporal and spatial SNR than 3D GRASE (spatial SNR = 8.5 ± 2.8 and 3.7 ± 1.4; temporal SNR = 27.4 ± 12.5 and 15.6 ± 7.6, respectively) and decreased through‐plane blurring due to its inherent oversampling of the central k‐space region, its reduced effective T_E and shorter total readout time, at the expense of a slight increase in the effective in‐plane voxel size. Copyright © 2014 John Wiley & Sons, Ltd.     

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.     

ASL‐MRICloud: An online tool for the processing of ASL MRI data

Arterial spin labeling (ASL) MRI is increasingly used in research and clinical settings. The purpose of this work is to develop a cloud‐based tool for ASL data processing, referred to as ASL‐MRICloud, which may be useful to the MRI community. In contrast to existing ASL toolboxes, which are based on software installation on the user's local computer, ASL‐MRICloud uses a web browser for data upload and results download, and the computation is performed on the remote server. As such, this tool is independent of the user's operating system, software version, and CPU speed. The ASL‐MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of ASL, including pseudo‐continuous ASL and pulsed ASL, and can process single‐delay and multi‐delay ASL data. The outputs of ASL‐MRICloud include absolute and relative values of cerebral blood flow, arterial transit time, voxel‐wise masks indicating regions with potential hyper‐perfusion and hypo‐perfusion, and an image quality index. The ASL tool is also integrated with a T₁‐based brain segmentation and normalization tool in MRICloud to allow generation of parametric maps in standard brain space as well as region‐of‐interest values. The tool was tested on a large data set containing 309 ASL scans as well as on publicly available ASL data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.     

Time‐efficient measurement of multi‐phase arterial spin labeling MR signal in white matter

White matter (WM) perfusion has great potential as a physiological biomarker in many neurological diseases. Although it has been demonstrated previously that arterial spin labeling magnetic resonance imaging (ASL‐MRI) enables the detection of the perfusion‐weighted signal in most voxels in WM, studies of cerebral blood flow (CBF) in WM by ASL‐MRI are relatively scarce because of its particular challenges, such as significantly lower perfusion and longer arterial transit times relative to gray matter (GM). Recently, ASL with a spectroscopic readout has been proposed to enhance the sensitivity for the measurement of WM perfusion. However, this approach suffers from long acquisition times, especially when acquiring multi‐phase ASL datasets to improve CBF quantification. Furthermore, the potential increase in the signal‐to‐noise ratio (SNR) by spectroscopic readout compared with echo planar imaging (EPI) readout has not been proven experimentally. In this study, we propose the use of time‐encoded pseudo‐continuous ASL (te‐pCASL) with single‐voxel point‐resolved spectroscopy (PRESS) readout to quantify WM cerebral perfusion in a more time‐efficient manner. Results are compared with te‐pCASL with a conventional EPI readout for both WM and GM perfusion measurements. Perfusion measurements by te‐pCASL PRESS and conventional EPI showed no significant difference for quantitative WM CBF values (Student's t‐test, p = 0.19) or temporal SNR (p = 0.33 and p = 0.81 for GM and WM, respectively), whereas GM CBF values (p = 0.016) were higher using PRESS than EPI readout. WM CBF values were found to be 18.2 ± 7.6 mL/100 g/min (PRESS) and 12.5 ± 5.5 mL/100 g/min (EPI), whereas GM CBF values were found to be 77.1 ± 11.2 mL/100 g/min (PRESS) and 53.6 ± 9.6 mL/100 g/min (EPI). This study demonstrates the feasibility of te‐pCASL PRESS for the quantification of WM perfusion changes in a highly time‐efficient manner, but it does not result in improved temporal SNR, as does traditional te‐pCASL EPI, which remains the preferred option because of its flexibility in use.     

Intravoxel incoherent motion MRI as a means to measure in vivo perfusion: A review of the evidence

The idea that in vivo intravoxel incoherent motion magnetic resonance signal is influenced by blood motion in the microvasculature is exciting, because it suggests that local and quantitative perfusion information can be obtained in a simple and elegant way from a few diffusion‐weighted images, without contrast injection. When the method was proposed in the late 1980s some doubts appeared as to its feasibility, and, probably because the signal to noise and image quality at the time was not sufficient, no obvious experimental evidence could be produced to alleviate them. Helped by the tremendous improvements seen in the last three decades in MR hardware, pulse design, and post‐processing capabilities, an increasing number of encouraging reports on the value of intravoxel incoherent motion perfusion imaging have emerged. The aim of this article is to review the current published evidence on the feasibility of in vivo perfusion imaging with intravoxel incoherent motion MRI.     

FAWSETS perfusion measurements in exercising skeletal muscle

Arterial spin labeling (ASL) techniques are now recognized as valid tools for providing accurate measurements of cerebral and cardiac perfusion. The labeling process used with most ASL techniques creates two problems, magnetization transfer (MT) effects and arterial transit time effects, that require compensation. The compensation process limits time resolution and hinders absolute quantification. MT effects are particularly problematic in skeletal muscle because they are large and change rapidly during exercise. The protocol presented here was developed specifically for quantification of perfusion in exercising skeletal muscle. The ASL technique that was implemented, FAWSETS, eliminates MT effects and arterial transit times. Localized, single-voxel perfusion measurements were acquired from rat hind limbs at rest, during ischemia and during three different levels of stimulated exercise. The results demonstrate sufficient sensitivity to determine the time constants for perfusion changes at onset of, and during recovery from, exercise and to distinguish the differences in the amplitude of the perfusion response to different levels of exercise. Additional measurements were conducted to demonstrate insensitivity to MT effects. The exercise protocol is easily adaptable to phosphorous magnetic resonance measurements, allowing the possibility to acquire local measurements of perfusion and metabolism from the same tissue in future experiments.     

Optimization of flow‐sensitive alternating inversion recovery (FAIR) for perfusion functional MRI of rodent brain

Arterial spin labeling (ASL) MRI provides a noninvasive method to image perfusion, and has been applied to map neural activation in the brain. Although pulsed labeling methods have been widely used in humans, continuous ASL with a dedicated neck labeling coil is still the preferred method in rodent brain functional MRI (fMRI) to maximize the sensitivity and allow multislice acquisition. However, the additional hardware is not readily available and hence its application is limited. In this study, flow‐sensitive alternating inversion recovery (FAIR) pulsed ASL was optimized for fMRI of rat brain. A practical challenge of FAIR is the suboptimal global inversion by the transmit coil of limited dimensions, which results in low effective labeling. By using a large volume transmit coil and proper positioning to optimize the body coverage, the perfusion signal was increased by 38.3% compared with positioning the brain at the isocenter. An additional 53.3% gain in signal was achieved using optimized repetition and inversion times compared with a long TR. Under electrical stimulation to the forepaws, a perfusion activation signal change of 63.7 ± 6.3% can be reliably detected in the primary somatosensory cortices using single slice or multislice echo planar imaging at 9.4 T. This demonstrates the potential of using pulsed ASL for multislice perfusion fMRI in functional and pharmacological applications in rat brain. Copyright © 2012 John Wiley & Sons, Ltd.     

A multimodality vascular imaging phantom with fiducial markers visible in DSA, CTA, MRA, and ultrasound

The objective was to design a vascular phantom compatible with digital subtraction angiography, computerized tomography angiography, ultrasound and magnetic resonance angiography (MRA). Fiducial markers were implanted at precise known locations in the phantom to facilitate identification and orientation of plane views from three-dimensional (3-D) reconstructed images. A vascular conduit connected to tubing at the extremities of the phantom ran through an agar-based gel filling it. A vessel wall in latex was included around the conduit to avoid diffusion of contrast agents. Using a lost-material casting technique based on a low melting point metal, geometries of pathological vessels were modeled. During the experimental testing, fiducial markers were detectable in all modalities without distortion. No leak of gadolinium through the vascular wall was observed on MRA after 5 hours. Moreover, no significant deformation of the vascular conduit was noted during the fabrication process (confirmed by microtome slicing along the vessel). The potential use of the phantom for calibration, rescaling, and fusion of 3-D images obtained from the different modalities as well as its use for the evaluation of intra- and inter-modality comparative studies of imaging systems are discussed. In conclusion, the vascular phantom can allow accurate calibration of radiological imaging devices based on x-ray, magnetic resonance and ultrasound and quantitative comparisons of the geometric accuracy of the vessel lumen obtained with each of these methods on a given well defined 3-D geometry.     

Rapid and robust pulmonary proton ZTE imaging in the mouse

Pulmonary MRI is challenging because of the low proton density and rapid transverse relaxation in the lung associated with microscopic magnetic field inhomogeneities caused by tissue–air interfaces. Therefore, low signal is obtained in gradient and spin echo proton images. Alternatively, non‐proton MRI using hyperpolarized gases or radial techniques with ultrashort or zero TE have been proposed to image the lung. Also with the latter approach, the general challenge remains to provide full coverage of the lung at sufficient spatial resolution, signal‐to‐noise ratio (SNR) and image quality within a reasonable scan time. This task is further aggravated by physiological motion and is particularly demanding in small animals, such as mice. In this work, three‐dimensional (3D) zero echo time (ZTE) imaging is employed for efficient pulmonary MRI. Four protocols with different averaging and respiratory triggering schemes are developed and compared with respect to image quality and SNR. To address the critical issue of background signal in ZTE images, a subtraction approach is proposed, providing images virtually free of disturbing signal from nearby hardware parts. The protocols are tested for pulmonary MRI in six mice at 4.7 T, consistently providing images of high quality with a 3D isotropic resolution of 313 µm and SNR values in the lung between 8.0 and 18.5 within scan times between 1 min 21 s and 4 min 44 s. A generally high robustness of the ZTE approach against motion is observed, whilst respiratory triggering further improves the SNR and visibility of image details. The developed techniques are expected to enable efficient preclinical animal studies in the lung and will also be of importance for human applications. Further improvements are expected from radiofrequency (RF) coils with increased SNR and reduced background signal. Copyright © 2014 John Wiley & Sons, Ltd.     

Detecting Stripe Artifacts in Ultrasound Images

Brain perfusion diseases such as acute ischemic stroke are detectable through computed tomography (CT)-/magnetic resonance imaging (MRI)-based methods. An alternative approach makes use of ultrasound imaging. In this low-cost bedside method, noise and artifacts degrade the imaging process. Especially stripe artifacts show a similar signal behavior compared to acute stroke or brain perfusion diseases. This document describes how stripe artifacts can be detected and eliminated in ultrasound images obtained through harmonic imaging (HI). On the basis of this new method, both proper identification of areas with critically reduced brain tissue perfusion and classification between brain perfusion defects and ultrasound stripe artifacts are made possible.     

Systemic inflammation and alterations to cerebral blood flow in obstructive sleep apnea

Systemic inflammation and alterations to regional cerebral blood flow (CBF) have been reported previously in obstructive sleep apnea (OSA). This study utilized arterial spin labelling (ASL) perfusion magnetic resonance imaging (MRI) to evaluate CBF in OSA patients and determine its relationship with systemic inflammation. Twenty male patients with moderate and severe OSA [apnea–hypopnea index (AHI) >15] and 16 healthy male volunteers (AHI <5) were recruited. Early‐ or late‐phase changes in leucocyte apoptosis and its subsets were determined by flow cytometry. Perfusion MRI data were acquired with a pulsed continuous ASL technique. The CBF maps were compared using voxel‐based statistics to determine differences between the OSA and control groups. The differences in CBF, clinical severity and leucocyte apoptosis were correlated. Exploratory groupwise comparison between the two groups revealed that the OSA patients exhibited low CBF values in the vulnerable regions. The lower regional CBF values were correlated with higher clinical disease severity and leucocyte apoptosis. OSA impairs cerebral perfusion in vulnerable regions, and this deficit is associated with increased disease severity. The apparent correlation between systemic inflammation and cerebral perfusion may be indicative of haemodynamic alterations and their consequences in OSA.     

Simultaneous detection of changes in perfusion and BOLD contrast

A new functional magnetic resonance imaging (fMRI) technique for simultaneous detection (SIDE) of changes in perfusion and blood oxygenation level dependent (BOLD) contrast is described. Perfusion contrast is generated by using magnetically labeled endogenous water proton spins as a freely diffusible tracer. A single slice-selective inversion pulse is combined with dual echo echo-planar imaging to generate a spin-echo (SE) image sensitive to changes in perfusion and a gradient-echo (GE) image sensitive to changes in both perfusion and BOLD contrast. The SIDE technique was applied to detect functional changes induced by a visual search task. A theoretical analysis is provided to calculate quantitative maps of changes in cerebral blood flow (DeltaCBF) and effective transverse relaxation time (DeltaT(2)*) from the corresponding signal changes in the SE and GE images. Since SE an GE images are generated from the same longitudinal magnetization, no errors due to spatial or temporal mismatch can arise in the quantification of DeltaCBF and DeltaT(2)*.     

Perfusion has no effect on the in vivo CEST effect from Cr (CrCEST) in skeletal muscle

Creatine, a key component of muscle energy metabolism, exhibits a chemical exchange saturation transfer (CEST) effect between its amine group and bulk water, which has been exploited to spatially and temporally map creatine changes in skeletal muscle before and after exercise. In addition, exercise leads to an increase in muscle perfusion. In this work, we determined the effects of perfused blood on the CEST effects from creatine in skeletal muscle. Experiments were performed on healthy human subjects (n = 5) on a whole‐body Siemens 7T magnetic resonance imaging (MRI) scanner with a 28‐channel radiofrequency (RF) coil. Reactive hyperemia, induced by inflation and subsequent deflation of a pressure cuff secured around the thigh, was used to increase tissue perfusion whilst maintaining the levels of creatine kinase metabolites. CEST, arterial spin labeling (ASL) and ³¹P MRS data were acquired at baseline and for 6 min after cuff deflation. Reactive hyperemia resulted in substantial increases in perfusion in human skeletal muscle of the lower leg as measured by the ASL mean percentage difference. However, no significant changes in CrCEST asymmetry (CrCEST_asym) or ³¹P MRS‐derived PCr levels of skeletal muscle were observed following cuff deflation. This work demonstrates that perfusion changes do not have a major confounding effect on CrCEST measurements.     

Quantification of Network Perfusion in ASL Cerebral Blood Flow Data with Seed Based and ICA Approaches

Independent component analysis (ICA) or seed based approaches (SBA) in functional magnetic resonance imaging blood oxygenation level dependent (BOLD) data became widely applied tools to identify functionally connected, large scale brain networks. Differences between task conditions as well as specific alterations of the networks in patients as compared to healthy controls were reported. However, BOLD lacks the possibility of quantifying absolute network metabolic activity, which is of particular interest in the case of pathological alterations. In contrast, arterial spin labeling (ASL) techniques allow quantifying absolute cerebral blood flow (CBF) in rest and in task-related conditions. In this study, we explored the ability of identifying networks in ASL data using ICA and to quantify network activity in terms of absolute CBF values. Moreover, we compared the results to SBA and performed a test–retest analysis. Twelve healthy young subjects performed a fingertapping block-design experiment. During the task pseudo-continuous ASL was measured. After CBF quantification the individual datasets were concatenated and subjected to the ICA algorithm. ICA proved capable to identify the somato-motor and the default mode network. Moreover, absolute network CBF within the separate networks during either condition could be quantified. We could demonstrate that using ICA and SBA functional connectivity analysis is feasible and robust in ASL-CBF data. CBF functional connectivity is a novel approach that opens a new strategy to evaluate differences of network activity in terms of absolute network CBF and thus allows quantifying inter-individual differences in the resting state and task-related activations and deactivations.