A general framework for optimizing arterial spin labeling MRI experiments

Determinants of cerebral blood flow and arterial transit time in healthy older adults

Cerebral blood flow (CBF) and arterial transit time (ATT), markers of brain vascular health, worsen with age. The primary aim of this cross-sectional study was to identify modifiable determinants of CBF and ATT in healthy older adults (n = 78, aged 60-81 years). Associations between cardiorespiratory fitness and CBF or ATT were of particular interest because the impact of cardiorespiratory fitness is not clear within existing literature. Secondly, this study assessed whether CBF or ATT relate to cognitive function in older adults. Multiple post-labelling delay pseudo-continuous arterial spin labelling estimated resting CBF and ATT in grey matter. Results from multiple linear regressions found higher BMI was associated with lower global CBF (β = -0.35, P = 0.008) and a longer global ATT (β = 0.30, P = 0.017), global ATT lengthened with increasing age (β = 0.43, P = 0.004), and higher cardiorespiratory fitness was associated with longer ATT in parietal (β = 0.44, P = 0.004) and occipital (β = 0.45, P = 0.003) regions. Global or regional CBF or ATT were not associated with processing speed, working memory, or attention. In conclusion, preventing excessive weight gain may help attenuate age-related declines in brain vascular health. ATT may be more sensitive to age-related decline than CBF, and therefore useful for early detection and management of cerebrovascular impairment. Finally, cardiorespiratory fitness appears to have little effect on CBF but may induce longer ATT in specific regions.

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

The effect of post-labeling delay on cerebral blood flow is influenced by age and sex: a study based on arterial spin-labeling magnetic resonance imaging

Background

Whether the effect of post-labeling delay (PLD) on cerebral blood flow (CBF) is influenced by age and sex in adults is unknown. In this study, we mainly aimed to explore the potential influence of age and sex on the effect of PLD on CBF.

Methods

This prospective study enrolled 90 healthy adult volunteers (49.47±15.63 years of age; age range, 20-77 years; 47 female; 43 male). All participants underwent 3-dimensional (3D) pseudo-continuous arterial spin labeling (ASL) imaging with 3 different PLDs (1,525, 2,025, and 2,525 ms). The CBF values for each PLD, the arterial transit time (ATT), and the spatial coefficient of variation (spatial CoV) were computed for 21 regions of interest (ROIs) in every participant. Multivariate regression analysis was conducted to assess the potential influence of age and sex on the effect of PLD on CBF and the relationships among CBF, ATT, PLD, age, sex, and spatial CoV.

Results

The CBF increased for 7.32 to 9.87 mL/100 g/min as the PLD increased per 1 second in the global gray matter, bilateral frontal, temporal lobes, the vascular territories of bilateral anterior and middle carotid artery. When the age increased per 1 year, the speed of the changes for CBF decreased for 0.26 to 0.3 mL/100 g/min/s in these regions. However, the CBF decreased for 12 to 17 mL/100 g/min as the PLD increased per 1 second in the bilateral limbic lobes, insula, and deep gray matter. In these regions, the speed of the changes for CBF increased for 0.2 to 0.28 mL/100 g/min/s as the age increased per 1 year. Furthermore, compared to the female, the speed of the changes for CBF decreased for 3.58 to 4.6 mL/100 g/min/s for the male in global gray matter, bilateral frontal, limbic lobes, and the vascular territories of bilateral anterior carotid artery, and the speed increased 4.49 to 5.09 mL/100 g/min/s for the male in the limbic lobes. In addition, the CBF decreased with aging and the CBF tended to be higher in females compared to males. At the same time, we found that the ATT of all ROIs increased with age and manifested higher in males than females. Moreover, we found that CBF decreased with the increase of ATT, and the effect of ATT on CBF was less influenced by PLD. Finally, we found that the spatial CoV of ASL in certain regions increased with the increase of ATT and age, and was greater in males.

Conclusions

The effect of PLD on CBF can be influenced by age and sex. The relationships among CBF, ATT, PLD, age, sex, and spatial CoV found in this study may have certain significance for the study of ASL imaging in the future.

VICTR: Venous transit time imaging by changes in T1 relaxation

PURPOSE

Abnormalities in cerebral veins are a common finding in many neurological diseases, yet there is a scarcity of MRI techniques to assess venous hemodynamic function. The present study aims to develop a noncontrast technique to measure a novel blood flow circulatory measure, venous transit time (VTT), which denotes the time it takes for water to travel from capillary to major veins.

METHODS

The proposed sequence, venous transit time imaging by changes in T1 relaxation (VICTR), is based on the notion that as water molecules transition from the tissue into the veins, they undergo a change in T1 relaxation time. The validity of the measured VTT was tested by studying the VTT along the anatomically known flow trajectory of venous vessels as well as using a physiological vasoconstrictive challenge of caffeine ingestion. Finally, we compared the VTT measured with VICTR MRI to a bolus-tracking method using gadolinium-based contrast agent.

RESULTS

VTT was measured to be 3116.3 ± 326.0 ms in the posterior superior sagittal sinus (SSS), which was significantly longer than 2865.0 ± 390.8 ms at the anterior superior sagittal sinus (p = 0.004). The test-retest assessment showed an interclass correlation coefficient of 0.964. VTT was significantly increased by 513.8 ± 239.3 ms after caffeine ingestion (p < 0.001). VTT measured with VICTR MRI revealed a strong correlation (R = 0.84, p = 0.002) with that measured with the contrast-based approach. VTT was found inversely correlated to cerebral blood flow and venous oxygenation across individuals.

CONCLUSION

A noncontrast MRI technique, VICTR MRI, was developed to measure the VTT of the brain.

Effects of the Training Data Condition on Arterial Spin Labeling Parameter Estimation Using a Simulation-Based Supervised Deep Neural Network

OBJECTIVE

A simulation-based supervised deep neural network (DNN) can accurately estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multidelay arterial spin labeling signals. However, the performance of deep learning depends on the characteristics of the training data set. We aimed to investigate the effects of the ground truth (GT) ranges of CBF and ATT on the performance of the DNN when training data were prepared using arterial spin labeling signal simulation.

METHODS

Deep neural networks were individually trained using 36 patterns of the training data sets. Simulation test data (1,000,000 points), 17 healthy volunteers, and 1 patient with moyamoya disease were included. The simulation test data were used to evaluate accuracy, precision, and noise immunity of the DNN. The best-performing DNN was determined by the normalized mean absolute error (NMAE), normalized root mean squared error (NRMSE), and normalized coefficient of variation over repeated training (CV Net ). Cerebral blood flow and ATT values and their histograms were compared between the GT and predicted values. For the in vivo data, the dependency of the predicted values on the GT ranges was visually evaluated by comparing CBF and ATT maps between the best-performing DNN and the other DNNs. Moreover, using the synthesized noisy images, noise immunity was compared between the best-performing DNN based on the simulation study and a conventional method.

RESULTS

The simulation study showed that a network trained by the GT of CBF and ATT in the ranges of 0 to 120 mL/100 g/min and 0 to 4500 milliseconds, respectively, had the highest performance (NMAE CBF , 0.150; NRMSE CBF , 0.231; CV NET CBF , 0.028; NMAE ATT , 0.158; NRMSE ATT , 0.257; and CV NET ATT , 0.028). Although the predicted CBF and ATT varied with the GT range of the training data sets, the appropriate settings preserved the accuracy, precision, and noise immunity of the DNN. In addition, the same results were observed in in vivo studies.

CONCLUSIONS

The GT ranges to prepare the training data affected the performance of the simulation-based supervised DNNs. The predicted CBF and ATT values depended on the GT range; inappropriate settings degraded the accuracy, whereas appropriate settings of the GT range provided accurate and precise estimates.

Insights into hippocampal perfusion using high-resolution, multi-modal 7T MRI

We present a comprehensive study on the non-invasive measurement of hippocampal perfusion. Using high-resolution 7 tesla arterial spin labeling (ASL) data, we generated robust perfusion maps and observed significant variations in perfusion among hippocampal subfields, with CA1 exhibiting the lowest perfusion levels. Notably, these perfusion differences were robust and already detectable with 50 perfusion-weighted images per subject, acquired in 5 min. To understand the underlying factors, we examined the influence of image quality metrics, various tissue microstructure and morphometric properties, macrovasculature, and cytoarchitecture. We observed higher perfusion in regions located closer to arteries, demonstrating the influence of vascular proximity on hippocampal perfusion. Moreover, ex vivo cytoarchitectonic features based on neuronal density differences appeared to correlate stronger with hippocampal perfusion than morphometric measures like gray matter thickness. These findings emphasize the interplay between microvasculature, macrovasculature, and metabolic demand in shaping hippocampal perfusion. Our study expands the current understanding of hippocampal physiology and its relevance to neurological disorders. By providing in vivo evidence of perfusion differences between hippocampal subfields, our findings have implications for diagnosis and potential therapeutic interventions. In conclusion, our study provides a valuable resource for extensively characterizing hippocampal perfusion.

A comparative study of multi and single post labeling delay pseudocontinuous arterial spin labeling in patients with carotid artery stenosis

PURPOSE

Arterial Spin Labeling (ASL) allows for the non-invasive visualization of brain perfusion to detect abnormalities. In unilateral carotid artery stenosis, one hemisphere is less supplied with blood which results in a lower cerebral blood flow (CBF) compared to the healthy side. ASL can be performed time-resolved using multiple post labeling delay (PLD) times after labeling or static with a single delay, the latter allowing for a faster and more robust acquisition while bearing the risk of a falsely set delay resulting in unusable images. The purpose of this study is to compare the performance of multi-PLD and single-PLD ASL in patients with unilateral carotid artery stenosis both as means of diagnosis and therapeutic follow-up examination.

METHODS

ASL perfusion data of 17 patients with known unilateral carotid artery stenosis was used to compare the diagnostic performance of the multi-PLD and single-PLD approach. Comparisons were made based on the CBF values and the added benefit of arrival time maps showing slower blood flow in multi-PLD ASL which might be overlooked in the individual delay images both before and after therapy.

RESULTS

Both the multi-PLD and the single-PLD data could identify the side of the stenosis with hemispheric differences in each approach (p < 0.001) and depict the normalization of CBF after therapy (p > 0.05). There were no differences between the individual methods (p > 0.05).

CONCLUSION

In this work, we could show that multi-PLD ASL in patients with unilateral carotid artery stenosis is beneficial as it provides both CBF and arrival time maps, however when only a single-PLD acquisition is available, this appears sufficient in a clinical setting to investigate the presence of a unilateral stenosis.

Blood-brain barrier water exchange measurements using contrast-enhanced ASL

A technique for quantifying regional blood-brain barrier (BBB) water exchange rates using contrast-enhanced arterial spin labelling (CE-ASL) is presented and evaluated in simulations and in vivo. The two-compartment ASL model describes the water exchange rate from blood to tissue,k b , but to estimatek b in practice it is necessary to separate the intra- and extravascular signals. This is challenging in standard ASL data owing to the small difference inT 1 values. Here, a gadolinium-based contrast agent is used to increase thisT 1 difference and enable the signal components to be disentangled. The optimal post-contrast bloodT 1 (T 1 , b post ) at 3 T was determined in a sensitivity analysis, and the accuracy and precision of the method quantified using Monte Carlo simulations. Proof-of-concept data were acquired in six healthy volunteers (five female, age range 24-46 years). The sensitivity analysis identified the optimalT 1 , b post at 3 T as 0.8 s. Simulations showed thatk b could be estimated in individual cortical regions with a relative error ϵ < 1 % and coefficient of variation CoV = 30 %; however, a high dependence on bloodT 1 was also observed. In volunteer data, mean parameter values in grey matter were: arterial transit timet A = 1 . 15 ± 0 . 49  s, cerebral blood flow f = 58 . 0 ± 14 . 3  mL blood/min/100 mL tissue and water exchange ratek b = 2 . 32 ± 2 . 49 s-1 . CE-ASL can provide regional BBB water exchange rate estimates; however, the clinical utility of the technique is dependent on the achievable accuracy of measuredT 1 values.

Revascularization improves vascular hemodynamics - a study assessing cerebrovascular reserve and transit time in Moyamoya patients using MRI

Cerebrovascular reserve (CVR) reflects the capacity of cerebral blood flow (CBF) to change. Decreased CVR implies poor hemodynamics and is linked to a higher risk for stroke. Revascularization has been shown to improve CBF in patients with vasculopathy such as Moyamoya disease. Dynamic susceptibility contrast (DSC) can measure transit time to evaluate patients suspected of stroke. Arterial spin labeling (ASL) is a non-invasive technique for CBF, CVR, and arterial transit time (ATT) measurements. Here, we investigate the change in hemodynamics 4-12 months after extracranial-to-intracranial direct bypass in 52 Moyamoya patients using ASL with single and multiple post-labeling delays (PLD). Images were collected using ASL and DSC with acetazolamide. CVR, CBF, ATT, and time-to-maximum (Tmax) were measured in different flow territories. Results showed that hemodynamics improved significantly in regions affected by arterial occlusions after revascularization. CVR increased by 16 ± 11% (p < 0.01) and 25 ± 13% (p < 0.01) for single- and multi-PLD ASL, respectively. Transit time measured by multi-PLD ASL and post-vasodilation DSC reduced by 13 ± 7% (p < 0.01) and 9 ± 5% (p < 0.01), respectively. For all regions, ATT correlated significantly with Tmax (R2  =  0.59, p < 0.01). Thus, revascularization improved CVR and decreased transit times. Multi-PLD ASL can serve as an effective and non-invasive modality to examine vascular hemodynamics in Moyamoya patients.

Brain perfusion imaging by multi-delay arterial spin labeling: Impact of modeling dispersion and interaction with denoising strategies and pathology

PURPOSE

Arterial spin labeling (ASL) acquisitions at multiple post-labeling delays may provide more accurate quantification of cerebral blood flow (CBF), by fitting appropriate kinetic models and simultaneously estimating relevant parameters such as the arterial transit time (ATT) and arterial cerebral blood volume (aCBV). We evaluate the effects of denoising strategies on model fitting and parameter estimation when accounting for the dispersion of the label bolus through the vasculature in cerebrovascular disease.

METHODS

We analyzed multi-delay ASL data from 17 cerebral small vessel disease patients (50 ± 9 y) and 13 healthy controls (52 ± 8 y), by fitting an extended kinetic model with or without bolus dispersion. We considered two denoising strategies: removal of structured noise sources by independent component analysis (ICA) of the control-label image timeseries; and averaging the repetitions of the control-label images prior to model fitting.

RESULTS

Modeling bolus dispersion improved estimation precision and impacted parameter values, but these effects strongly depended on whether repetitions were averaged before model fitting. In general, repetition averaging improved model fitting but adversely affected parameter values, particularly CBF and aCBV near arterial locations in patients. This suggests that using all repetitions allows better noise estimation at the earlier delays. In contrast, ICA denoising improved model fitting and estimation precision while leaving parameter values unaffected.

CONCLUSION

Our results support the use of ICA denoising to improve model fitting to multi-delay ASL and suggest that using all control-label repetitions improves the estimation of macrovascular signal contributions and hence perfusion quantification near arterial locations. This is important when modeling flow dispersion in cerebrovascular pathology.

Developments in functional imaging of the placenta

The placenta is both the literal and metaphorical black box of pregnancy. Measurement of the function of the placenta has the potential to enhance our understanding of this enigmatic organ and serve to support obstetric decision making. Advanced imaging techniques are key to support these measurements. This review summarises emerging imaging technology being used to measure the function of the placenta and new developments in the computational analysis of these data. We address three important examples where functional imaging is supporting our understanding of these conditions: fetal growth restriction, placenta accreta, and twin-twin transfusion syndrome.

Modelling spatiotemporal dynamics of cerebral blood flow using multiple-timepoint arterial spin labelling MRI

Introduction: Cerebral blood flow (CBF) is an important physiological parameter that can be quantified non-invasively using arterial spin labelling (ASL) imaging. Although most ASL studies are based on single-timepoint strategies, multi-timepoint approaches (multiple-PLD) in combination with appropriate model fitting strategies may be beneficial not only to improve CBF quantification but also to retrieve other physiological information of interest. Methods: In this work, we tested several kinetic models for the fitting of multiple-PLD pCASL data in a group of 10 healthy subjects. In particular, we extended the standard kinetic model by incorporating dispersion effects and the macrovascular contribution and assessed their individual and combined effect on CBF quantification. These assessments were performed using two pseudo-continuous ASL (pCASL) datasets acquired in the same subjects but during two conditions mimicking different CBF dynamics: normocapnia and hypercapnia (achieved through a CO₂ stimulus). Results: All kinetic models quantified and highlighted the different CBF spatiotemporal dynamics between the two conditions. Hypercapnia led to an increase in CBF whilst decreasing arterial transit time (ATT) and arterial blood volume (aBV). When comparing the different kinetic models, the incorporation of dispersion effects yielded a significant decrease in CBF (∼10-22%) and ATT (∼17-26%), whilst aBV (∼44-74%) increased, and this was observed in both conditions. The extended model that includes dispersion effects and the macrovascular component has been shown to provide the best fit to both datasets. Conclusion: Our results support the use of extended models that include the macrovascular component and dispersion effects when modelling multiple-PLD pCASL data.

Optimization of 4D combined angiography and perfusion using radial imaging and arterial spin labeling

PURPOSE

To extend and optimize a non-contrast MRI technique to obtain whole head 4D (time-resolved 3D) qualitative angiographic and perfusion images from a single scan.

METHODS

4D combined angiography and perfusion using radial imaging and arterial spin labeling (CAPRIA) uses pseudocontinuous labeling with a 3D golden ratio ("koosh ball") readout to continuously image the blood water as it travels through the arterial system and exchanges into the tissue. High spatial/temporal resolution angiograms and low spatial/temporal resolution perfusion images can be flexibly reconstructed from the same raw k-space data. Constant and variable flip angle (CFA and VFA, respectively) excitation schedules were optimized through simulations and tested in healthy volunteers. A conventional sensitivity encoding (SENSE) reconstruction was compared against a locally low rank (LLR) reconstruction, which leverages spatiotemporal correlations. Comparison was also made with time-matched time-of-flight angiography and multi-delay EPI perfusion images. Differences in image quality were assessed through split-scan repeatability.

RESULTS

The optimized VFA schedule (2-9°) resulted in a significant (p < 0.001) improvement in image quality (up to 84% vs. CFA), particularly for the lower SNR perfusion images. The LLR reconstruction provided effective denoising without biasing the signal timecourses, significantly improving angiographic and perfusion image quality and repeatability (up to 143%, p < 0.001). 4D CAPRIA performed well compared with time-of-flight angiography and had better perfusion signal repeatability than the EPI-based approach (p < 0.001).

CONCLUSION

4D CAPRIA optimized using a VFA schedule and LLR reconstruction can yield high quality whole head 4D angiograms and perfusion images from a single scan.

Time-encoded pseudo-continuous arterial spin labeling: Increasing SNR in ASL dynamic angiography

PURPOSE

Dynamic angiography using arterial spin labeling (ASL) can provide detailed hemodynamic information. However, the long time-resolved readouts require small flip angles to preserve ASL signal for later timepoints, limiting SNR. By using time-encoded ASL to generate temporal information, the readout can be shortened. Here, the SNR improvements from using larger flip angles, made possible by the shorter readout, are quantitatively investigated.

METHODS

The SNR of a conventional protocol with nine Look-Locker readouts and a 4 × $$ \times $$ 3 time-encoded protocol with three Look-Locker readouts (giving nine matched timepoints) were compared using simulations and in vivo data. Both protocols were compared using readouts with constant flip angles (CFAs) and variable flip angles (VFAs), where the VFA scheme was designed to produce a consistent ASL signal across readouts. Optimization of the background suppression to minimize physiological noise across readouts was also explored.

RESULTS

The time-encoded protocol increased in vivo SNR by 103% and 96% when using CFAs or VFAs, respectively. Use of VFAs improved SNR compared with CFAs by 25% and 21% for the conventional and time-encoded protocols, respectively. The VFA scheme also removed signal discontinuities in the time-encoded data. Preliminary data suggest that optimizing the background suppression could improve in vivo SNR by a further 16%.

CONCLUSIONS

Time encoding can be used to generate additional temporal information in ASL angiography. This enables the use of larger flip angles, which can double the SNR compared with a non-time-encoded protocol. The shortened time-encoded readout can also lead to improved background suppression, reducing physiological noise and further improving SNR.

Recent advances in arterial spin labeling perfusion MRI in patients with vascular cognitive impairment

Cognitive impairment (CI) is a major health concern in aging populations. It impairs patients' independent life and may progress to dementia. Vascular cognitive impairment (VCI) encompasses all cerebrovascular pathologies that contribute to cognitive impairment (CI). Moreover, the majority of CI subtypes involve various aspects of vascular dysfunction. Recent research highlights the critical role of reduced cerebral blood flow (CBF) in the progress of VCI, and the detection of altered CBF may help to detect or even predict the onset of VCI. Arterial spin labeling (ASL) is a non-invasive, non-ionizing perfusion MRI technique for assessing CBF qualitatively and quantitatively. Recent methodological advances enabling improved signal-to-noise ratio (SNR) and data acquisition have led to an increase in the use of ASL to assess CBF in VCI patients. Combined with other imaging modalities and biomarkers, ASL has great potential for identifying early VCI and guiding prediction and prevention strategies. This review focuses on recent advances in ASL-based perfusion MRI for identifying patients at high risk of VCI.

Cerebral blood flow quantification with multi-delay arterial spin labeling in ischemic stroke and the association with early neurological outcome

Restoring blood flow to brain tissue at risk of infarction is essential for tissue survival and clinical outcome. We used cerebral blood flow (CBF) quantified with multiple post-labeling delay (PLD) pseudocontinuous arterial spin labeling (ASL) MRI after ischemic stroke and assessed the association between CBF and early neurological outcome. We acquired ASL with 7 PLDs at 3.0 T in large vessel occlusion stroke patients at 24 h. We quantified CBF relative to the contralateral hemisphere (rCBF) and defined hyperperfusion as a ≥30% increase and hypoperfusion as a ≥40% decrease in rCBF. We included 44 patients (median age: 70 years, median NIHSS: 13, 40 treated with endovascular thrombectomy) of whom 37 were recanalized. Hyperperfusion in ischemic core occurred in recanalized but not in non-recanalized patients (65.8% vs 0%, p = 0.006). Hypoperfusion occurred only in the latter group (0% vs 85.7%, p < 0.001). In recanalized patients, hyperperfusion was also seen in salvaged penumbra (38.9%). Higher rCBF in ischemic core (aβ, -2.75 [95% CI: -4.11 to -1.40]) and salvaged penumbra (aβ, -5.62 [95% CI: -9.57 to -1.68]) was associated with lower NIHSS scores at 24 h. In conclusion, hyperperfusion frequently occurs in infarcted and salvaged brain tissue following successful recanalization and early neurological outcome is positively associated with the level of reperfusion.

Estimation of Cerebral Blood Flow and Arterial Transit Time From Multi-Delay Arterial Spin Labeling MRI Using a Simulation-Based Supervised Deep Neural Network

BACKGROUND

An inherently poor signal-to-noise ratio (SNR) causes inaccuracy and less precision in cerebral blood flow (CBF) and arterial transit time (ATT) when using arterial spin labeling (ASL). Deep neural network (DNN)-based parameter estimation can solve these problems.

PURPOSE

To reduce the effects of Rician noise on ASL parameter estimation and compute unbiased CBF and ATT using simulation-based supervised DNNs.

STUDY TYPE

Retrospective.

POPULATION

One million simulation test data points, 17 healthy volunteers (five women and 12 men, 33.2 ± 14.6 years of age), and one patient with moyamoya disease.

FIELD STRENGTH/SEQUENCE

3.0 T/Hadamard-encoded pseudo-continuous ASL with a three-dimensional fast spin-echo stack of spirals.

ASSESSMENT

Performances of DNN and conventional methods were compared. For test data, the normalized mean absolute error (NMAE) and normalized root mean squared error (NRMSE) between the ground truth and predicted values were evaluated. For in vivo data, baseline CBF and ATT and their relative changes with respect to SNR using artificial noise-added images were assessed.

STATISTICAL TESTS

One-way analysis of variance with post-hoc Tukey's multiple comparison test, paired t-test, and the Bland-Altman graphical analysis. Statistical significance was defined as P < 0.05.

RESULTS

For both CBF and ATT, NMAE and NRMSE were lower with DNN than with the conventional method. The baseline values were significantly smaller with DNN than with the conventional method (CBF in gray matter, 66 ± 10 vs. 71 ± 12 mL/100 g/min; white matter, 45 ± 6 vs. 46 ± 7 mL/100 g/min; ATT in gray matter, 1424 ± 201 vs. 1471 ± 154 msec). CBF and ATT increased with decreasing SNR; however, their change rates were smaller with DNN than were those with the conventional method. Higher CBF in the prolonged ATT region and clearer contrast in ATT were identified by DNN in a clinical case.

DATA CONCLUSION

DNN outperformed the conventional method in terms of accuracy, precision, and noise immunity.

EVIDENCE LEVEL

3 Technical Efficacy: Stage 1.

ASL-BIDS, the brain imaging data structure extension for arterial spin labeling

Arterial spin labeling (ASL) is a non-invasive MRI technique that allows for quantitative measurement of cerebral perfusion. Incomplete or inaccurate reporting of acquisition parameters complicates quantification, analysis, and sharing of ASL data, particularly for studies across multiple sites, platforms, and ASL methods. There is a strong need for standardization of ASL data storage, including acquisition metadata. Recently, ASL-BIDS, the BIDS extension for ASL, was developed and released in BIDS 1.5.0. This manuscript provides an overview of the development and design choices of this first ASL-BIDS extension, which is mainly aimed at clinical ASL applications. Discussed are the structure of the ASL data, focussing on storage order of the ASL time series and implementation of calibration approaches, unit scaling, ASL-related BIDS fields, and storage of the labeling plane information. Additionally, an overview of ASL-BIDS compatible conversion and ASL analysis software and ASL example datasets in BIDS format is provided. We anticipate that large-scale adoption of ASL-BIDS will improve the reproducibility of ASL research.

Using arterial spin labeling to measure cerebrovascular reactivity in Moyamoya disease: Insights from simultaneous PET/MRI

Cerebrovascular reactivity (CVR) reflects the CBF change to meet different physiological demands. The reference CVR technique is PET imaging with vasodilators but is inaccessible to most patients. DSC can measure transit time to evaluate patients suspected of stroke, but the use of gadolinium may cause side-effects. Arterial spin labeling (ASL) is a non-invasive MRI technique for CBF measurements. Here, we investigate the effectiveness of ASL with single and multiple post labeling delays (PLD) to replace PET and DSC for CVR and transit time mapping in 26 Moyamoya patients. Images were collected using simultaneous PET/MRI with acetazolamide. CVR, CBF, arterial transit time (ATT), and time-to-maximum (Tmax) were measured in different flow territories. Results showed that CVR was lower in occluded regions than normal regions (by 68 ± 12%, 52 ± 5%, and 56 ± 9%, for PET, single- and multi-PLD PCASL, respectively, all p < 0.05). Multi-PLD PCASL correlated slightly higher with PET (CCC = 0.36 and 0.32 in affected and unaffected territories respectively). Vasodilation caused ATT to reduce by 4.5 ± 3.1% (p < 0.01) in occluded regions. ATT correlated significantly with Tmax (R² > 0.35, p < 0.01). Therefore, multi-PLD ASL is recommended for CVR studies due to its high agreement with the reference PET technique and the capability of measuring transit time.

Multidelay ASL of the pediatric brain

Arterial spin labeling (ASL) is a powerful noncontrast MRI technique for evaluation of cerebral blood flow (CBF). A key parameter in single-delay ASL is the choice of postlabel delay (PLD), which refers to the timing between the labeling of arterial free water and measurement of flow into the brain. Multidelay ASL (MDASL) utilizes several PLDs to improve the accuracy of CBF calculations using arterial transit time (ATT) correction. This approach is particularly helpful in situations where ATT is unknown, including young subjects and slow-flow conditions. In this article, we discuss the technical considerations for MDASL, including labeling techniques, quantitative metrics, and technical artefacts. We then provide a practical summary of key clinical applications with real-life imaging examples in the pediatric brain, including stroke, vasculopathy, hypoxic-ischemic injury, epilepsy, migraine, tumor, infection, and metabolic disease.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

VESPA ASL: VElocity and SPAtially Selective Arterial Spin Labeling

PURPOSE

Spatially selective arterial spin labeling (ASL) perfusion MRI is sensitive to arterial transit times (ATT) that can result in inaccurate perfusion quantification when ATTs are long. Velocity-selective ASL is robust to this effect because blood is labeled within the imaging region, allowing immediate label delivery. However, velocity-selective ASL cannot characterize ATTs, which can provide important clinical information. Here, we introduce a novel pulse sequence, called VESPA ASL, that combines velocity-selective and pseudo-continuous ASL to simultaneously label different pools of arterial blood for robust cerebral blood flow (CBF) and ATT measurement.

METHODS

The VESPA ASL sequence is similar to velocity-selective ASL, but the velocity-selective labeling is made spatially selective, and pseudo-continuous ASL is added to fill the inflow time. The choice of inflow time and other sequence settings were explored. VESPA ASL was compared to multi-delay pseudo-continuous ASL and velocity-selective ASL through simulations and test-retest experiments in healthy volunteers.

RESULTS

VESPA ASL is shown to accurately measure CBF in the presence of long ATTs, and ATTs < TI can also be measured. Measurements were similar to established ASL techniques when ATT was short. When ATT was long, VESPA ASL measured CBF more accurately than multi-delay pseudo-continuous ASL, which tended to underestimate CBF.

CONCLUSION

VESPA ASL is a novel and robust approach to simultaneously measure CBF and ATT and offers important advantages over existing methods. It fills an important clinical need for noninvasive perfusion and transit time imaging in vascular diseases with delayed arterial transit.

Utilizing 3D Arterial Spin Labeling to Identify Cerebrovascular Leak and Glymphatic Obstruction in Neurodegenerative Disease

New approaches are required to successfully intervene therapeutically in neurodegenerative diseases. Addressing the earliest phases of disease, blood brain barrier (BBB) leak before the accumulation of misfolded proteins has significant potential for success. To do so, however, a reliable, noninvasive and economical test is required. There are two potential methods of identifying the BBB fluid leak that results in the accumulation of normally excluded substances which alter neuropil metabolism, protein synthesis and degradation with buildup of misfolded toxic proteins. The pros and cons of dynamic contrast imaging (DCI or DCE) and 3D TGSE PASL are discussed as potential early identifying methods. The results of prior publications of the 3D ASL technique and an overview of the associated physiologic challenges are discussed. Either method may serve well as reliable physiologic markers as novel therapeutic interventions directed at the vasculopathy of early neurodegenerative disease are developed. They may serve well in addressing other neurologic diseases associated with either vascular leak and/or reduced glymphatic flow.

Validation of the estimation of the macrovascular contribution in multi-timepoint arterial spin labeling MRI using a 2-component kinetic model

PURPOSE

In this paper, the ability to quantify cerebral blood flow by arterial spin labeling (ASL) was studied by investigating the separation of the macrovascular and tissue component using a 2-component model. Underlying assumptions of this model, especially the inclusion of dispersion in the analysis, were studied, as well as the temporal resolution of the ASL datasets.

METHODS

Four different datasets were acquired: (1) 4D ASL angiography to characterize the macrovascular component and to study dispersion modeling within this component, (2) high temporal resolution ASL data to investigate the separation of the 2 components and the effect of dispersion modelling on this separation, (3) low temporal resolution ASL dataset to study the effect of the temporal resolution on the separation of the 2 components, and (4) low temporal resolution ASL data with vascular crushing.

RESULTS

The model that included a gamma dispersion kernel had the best fit to the 4D ASL angiography. For the high temporal resolution ASL dataset, inclusion of the gamma dispersion kernel led to more signal included in the arterial blood volume map, which resulted in decreased cerebral blood flow values. The arterial blood volume and cerebral blood flow maps showed overall higher arterial blood volume values and lower cerebral blood flow values for the high temporal resolution dataset compared to the low temporal resolution dataset.

CONCLUSION

Inclusion of a gamma dispersion kernel resulted in better fitting of the model to the data. The separation of the macrovascular and tissue component is affected by the inclusion of a gamma dispersion kernel and the temporal resolution of the ASL dataset.

On the use of multicompartment models of diffusion and relaxation for placental imaging

Multi-compartment models of diffusion and relaxation are ubiquitous in magnetic resonance research especially applied to neuroimaging applications. These models are increasingly making their way into the world of placental imaging. This review provides a framework for their motivation and implementation and describes some of the outstanding questions that need to be answered before they can be routinely adopted.

Brain perfusion magnetic resonance imaging using pseudocontinuous arterial spin labeling in 314 dogs and cats

Background

Arterial spin labeling (ASL) is a noninvasive brain perfusion magnetic resonance imaging (MRI) technique that has not been assessed in clinical veterinary medicine.

Hypothesis/Objectives

To test the feasibility of ASL using a 1.5 Tesla scanner and provide recommendations for optimal quantification of cerebral blood flow (CBF) in dogs and cats.

Animals

Three hundred fourteen prospectively selected client‐owned dogs and cats.

Methods

Each animal underwent brain MRI including morphological sequences and ≥1 ASL sequences using different sites of blood labeling and postlabeling delays (PLD). Calculated ASL success rates were compared. The CBF was quantified in animals that had morphologically normal brain MRI results and parameters of ASL optimization were investigated.

Results

Arterial spin labeling was easily implemented with an overall success rate of 95% in animals with normal brain MRI. Technical recommendations included (a) positioning of the imaging slab at the foramen magnum and (b) selected PLD of 1025 ms in cats and dogs <7 kg, 1525 ms in dogs 7 to 38 kg, and 2025 ms in dogs >38 kg. In 37 dogs, median optimal CBF in the cortex and thalamic nuclei were 114 and 95 mL/100 g/min, respectively. In 28 cats, median CBF in the cortex and thalamic nuclei were 113 and 114 mL/100 g/min, respectively.

Conclusions and Clinical Importance

Our survey of brain perfusion ASL‐MRI demonstrated the feasibility of ASL at 1.5 Tesla, suggested technical recommendations and provided CBF values that should be helpful in the characterization of various brain diseases in dogs and cats.

Examination of optimized protocols for pCASL: Sensitivity to macrovascular contamination, flow dispersion, and prolonged arterial transit time

Purpose

Previously, multi‐ post‐labeling delays (PLD) pseudo‐continuous arterial spin labeling (pCASL) protocols have been optimized for the estimation accuracy of the cerebral blood flow (CBF) with/without the arterial transit time (ATT) under a standard kinetic model and a normal ATT range. This study aims to examine the estimation errors of these protocols under the effects of macrovascular contamination, flow dispersion, and prolonged arrival times, all of which might differ substantially in elderly or pathological groups.

Methods

Simulated data for four protocols with varying degrees of arterial blood volume (aBV), flow dispersion, and ATTs were fitted with different kinetic models, both with and without explicit correction for macrovascular signal contamination (MVC), to obtain CBF and ATT estimates. Sensitivity to MVC was defined and calculated when aBV > 0.5%. A previously acquired dataset was retrospectively analyzed to compare with simulation.

Results

All protocols showed underestimation of CBF and ATT in the prolonged ATT range. With MVC, the protocol optimized for CBF only (CBFopt) had the lowest sensitivity value to MVC, 33.47% and 60.21% error per 1% aBV in simulation and in vivo, respectively, among multi‐PLD protocols. All multi‐PLD protocols showed a significant decrease in estimation error when an extended kinetic model was used. Increasing flow dispersion at short ATTs caused increasing CBF and ATT overestimation in all protocols.

Conclusion

CBFopt was the least sensitive protocol to prolonged ATT and MVC for CBF estimation while maintaining reasonably good performance in estimating ATT. Explicitly including a macrovascular component in the kinetic model was shown to be a feasible approach in controlling for MVC.

Three-dimensional gradient and spin-echo readout for time-encoded pseudo-continuous arterial spin labeling: Influence of segmentation factor and flow compensation

PURPOSE

To monitor the complete passage of the labeled blood through the vascular tree into tissue and improve the quantification of ASL maps, we evaluated the effect of 3D gradient and spin-echo (GRASE) readout segments on temporal SNR (tSNR) and image blurriness for time-encoded pseudo-continuous arterial spin labeling and the effect of flow-compensation gradients on the presence of intravascular signal.

METHODS

Fifteen volunteers were scanned using time-encoded pCASL with 2D EPI and single-segment, two-segments, and three-segments 3D-GRASE readouts with first-order flow compensation (FC) gradients. Two-segments 3D-GRASE scans were acquired with 25%, 50%, 75%, and 100% of full first-order FC. Temporal SNR was assessed, and cerebral blood flow and arterial blood volume were quantified for all readout strategies.

RESULTS

For single-segment 3D GRASE, tSNR was comparable to 2D EPI for perfusion signal but worse for the arterial signal. Two-segments and three-segments 3D GRASE resulted in higher tSNR than 2D EPI for perfusion and arterial signal. The arterial signal was not well visualized for 3D-GRASE data without FC. Visualization of the intravascular signal at postlabeling delays of 660 ms and 1060 ms was restored with FC. Adequate visualization of the intravascular signal was achieved from 75% of FC gradient strength at a postlabeling delay of 660 ms. For a postlabeling delay of 1060 ms, full-FC gradients were the best option to depict intravascular signal.

CONCLUSION

Segmented GRASE provided higher effective tSNR compared with 2D-EPI and single-segment GRASE. Flow compensation with GRASE readout should be carefully controlled when applying for time-encoded pCASL to visualize intravascular signal.

Non-linear fitting with joint spatial regularization in arterial spin labeling

Multi-Delay single-shot arterial spin labeling (ASL) imaging provides accurate cerebral blood flow (CBF) and, in addition, arterial transit time (ATT) maps but the inherent low SNR can be challenging. Especially standard fitting using non-linear least squares often fails in regions with poor SNR, resulting in noisy estimates of the quantitative maps. State-of-the-art fitting techniques improve the SNR by incorporating prior knowledge in the estimation process which typically leads to spatial blurring. To this end, we propose a new estimation method with a joint spatial total generalized variation regularization on CBF and ATT. This joint regularization approach utilizes shared spatial features across maps to enhance sharpness and simultaneously improves noise suppression in the final estimates. The proposed method is evaluated at three levels, first on synthetic phantom data including pathologies, followed by in vivo acquisitions of healthy volunteers, and finally on patient data following an ischemic stroke. The quantitative estimates are compared to two reference methods, non-linear least squares fitting and a state-of-the-art ASL quantification algorithm based on Bayesian inference. The proposed joint regularization approach outperforms the reference implementations, substantially increasing the SNR in CBF and ATT while maintaining sharpness and quantitative accuracy in the estimates.

The costs and benefits of estimating T1 of tissue alongside cerebral blood flow and arterial transit time in pseudo-continuous arterial spin labeling

Multi-post-labeling-delay pseudo-continuous arterial spin labeling (multi-PLD PCASL) allows for absolute quantification of the cerebral blood flow (CBF) as well as the arterial transit time (ATT). Estimating these perfusion parameters from multi-PLD PCASL data is a non-linear inverse problem, which is commonly tackled by fitting the single-compartment model (SCM) for PCASL, with CBF and ATT as free parameters. The longitudinal relaxation time of tissue T_1t is an important parameter in this model, as it governs the decay of the perfusion signal entirely upon entry in the imaging voxel. Conventionally, T_1t is fixed to a population average. This approach can cause CBF quantification errors, as T_1t can vary significantly inter- and intra-subject. This study compares the impact on CBF quantification, in terms of accuracy and precision, of either fixing T_1t , the conventional approach, or estimating it alongside CBF and ATT. It is shown that the conventional approach can cause a significant bias in CBF. Indeed, simulation experiments reveal that if T_1t is fixed to a value that is 10% off its true value, this may already result in a bias of 15% in CBF. On the other hand, as is shown by both simulation and real data experiments, estimating T_1t along with CBF and ATT results in a loss of CBF precision of the same order, even if the experiment design is optimized for the latter estimation problem. Simulation experiments suggest that an optimal balance between accuracy and precision of CBF estimation from multi-PLD PCASL data can be expected when using the two-parameter estimator with a fixed T_1t value between population averages of T_1t and the longitudinal relaxation time of blood T_1b .

Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow

Arterial Spin Labeling (ASL) is a non-invasive, non-contrast, perfusion imaging technique which is inherently SNR limited. It is, therefore, important to carefully design scan protocols to ensure accurate measurements. Many pseudo-continuous ASL (PCASL) protocol designs have been proposed for measuring cerebral blood flow (CBF), but it has not yet been demonstrated which design offers the most accurate and repeatable CBF measurements. In this study, a wide range of literature PCASL protocols were first optimized for CBF accuracy and then compared using Monte Carlo simulations and in vivo experiments. The protocols included single-delay, sequential and time-encoded multi-timepoint protocols, and several novel protocol designs, which are hybrids of time-encoded and sequential multi-timepoint protocols. It was found that several multi-timepoint protocols produced more confident, accurate, and repeatable CBF estimates than the single-delay protocol, while also generating maps of arterial transit time. Of the literature protocols, the time-encoded protocol with T₁-adjusted label durations gave the most confident and accurate CBF estimates in vivo (16% and 40% better than single-delay), while the sequential multi-timepoint protocol was the most repeatable (20% more repeatable than single-delay). One of the novel hybrid protocols, Hybrid_T1-adj, was found to produce the most confident, accurate and repeatable CBF estimates out of all the protocols tested in both simulations and in vivo (24%, 47%, and 28% more confident, accurate, and repeatable than single-delay in vivo). The Hybrid_T1-adj protocol makes use of the best aspects of both time-encoded and sequential multi-timepoint protocols and should be a useful tool for accurately and efficiently measuring CBF.

Robust arterial transit time and cerebral blood flow estimation using combined acquisition of Hadamard-encoded multi-delay and long-labeled long-delay pseudo-continuous arterial spin labeling: a simulation and in vivo study

Arterial transit time (ATT) prolongation causes an error of cerebral blood flow (CBF) measurement during arterial spin labeling (ASL). To improve the accuracy of ATT and CBF in patients with prolonged ATT, we propose a robust ATT and CBF estimation method for clinical practice. The proposed method consists of a three-delay Hadamard-encoded pseudo-continuous ASL (H-pCASL) with an additional-encoding and single-delay with long-labeled long-delay (1dLLLD) acquisition. The additional-encoding allows for the reconstruction of a single-delay image with long-labeled short-delay (1dLLSD) in addition to the normal Hadamard sub-bolus images. Five different images (normal Hadamard 3 delay, 1dLLSD, 1dLLLD) were reconstructed to calculate ATT and CBF. A Monte Carlo simulation and an in vivo study were performed to access the accuracy of the proposed method in comparison to normal 7-delay (7d) H-pCASL with equally divided sub-bolus labeling duration (LD). The simulation showed that the accuracy of CBF is strongly affected by ATT. It was also demonstrated that underestimation of ATT and CBF by 7d H-pCASL was higher with longer ATT than with the proposed method. Consistent with the simulation, the 7d H-pCASL significantly underestimated the ATT compared to that of the proposed method. This underestimation was evident in the distal anterior cerebral artery (ACA; P = 0.0394) and the distal posterior cerebral artery (PCA; 2 P = 0.0255). Similar to the ATT, the CBF was underestimated with 7d H-pCASL in the distal ACA (P = 0.0099), distal middle cerebral artery (P = 0.0109), and distal PCA (P = 0.0319) compared to the proposed method. Improving the SNR of each delay image (even though the number of delays is small) is crucial for ATT estimation. This is opposed to acquiring many delays with short LD. The proposed method confers accurate ATT and CBF estimation within a practical acquisition time in a clinical setting.

Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression

PURPOSE

Arterial Spin Labeling (ASL) is a quantitative, non-invasive alternative for perfusion imaging that does not use contrast agents. The magnetic resonance fingerprinting (MRF) framework can be adapted to ASL to estimate multiple physiological parameters simultaneously. In this work, we introduce an optimization scheme to increase the sensitivity of the ASL fingerprint. We also propose a regression based estimation framework for MRF-ASL.

METHODS

To improve the sensitivity of MRF-ASL signals to underlying parameters, we optimized ASL labeling durations using the Cramer-Rao Lower Bound (CRLB). This paper also proposes a neural network regression based estimation framework trained using noisy synthetic signals generated from our ASL signal model. We tested our methods in silico and in vivo, and compared with multiple post labeling delay (multi-PLD) ASL and unoptimized MRF-ASL. We present comparisons of estimated maps for the six parameters of our signal model.

RESULTS

The scan design process facilitated precise estimates of multiple hemodynamic parameters and tissue properties from a single scan, in regions of normal gray and white matter, as well as regions with anomalous perfusion activity in the brain. In particular, there was a 86.7% correlation of perfusion estimates with the ground truth in silico, using our proposed techniques. In vivo, there was roughly a 7 fold improvement in the Coefficient of Variation (CoV) for white matter perfusion, and 2 fold improvement in gray matter perfusion CoV in comparison to a reference Multi PLD method. The regression based estimation approach provided perfusion estimates rapidly, with estimation times of around 1s per map.

CONCLUSIONS

Scan design optimization, coupled with regression-based estimation is a powerful tool for improving precision in MRF-ASL.

Supporting measurements or more averages? How to quantify cerebral blood flow most reliably in 5 minutes by arterial spin labeling

Purpose

To determine whether sacrificing part of the scan time of pseudo‐continuous arterial spin labeling (PCASL) for measurement of the labeling efficiency and blood T1 is beneficial in terms of CBF quantification reliability.

Methods

In a simulation framework, 5‐minute scan protocols with different scan time divisions between PCASL data acquisition and supporting measurements were evaluated in terms of CBF estimation variability across both noise and ground truth parameter realizations taken from the general population distribution. The entire simulation experiment was repeated for a single‐post‐labeling delay (PLD), multi‐PLD, and free‐lunch time‐encoded (te‐FL) PCASL acquisition strategy. Furthermore, a real data study was designed for preliminary validation.

Results

For the considered population statistics, measuring the labeling efficiency and the blood T1 proved beneficial in terms of CBF estimation variability for any distribution of the 5‐minute scan time compared to only acquiring ASL data. Compared to single‐PLD PCASL without support measurements as recommended in the consensus statement, a 26%, 33%, and 42% reduction in relative CBF estimation variability was found for optimal combinations of supporting measurements with single‐PLD, free‐lunch, and multi‐PLD PCASL data acquisition, respectively. The benefit of taking the individual variation of blood T1 into account was also demonstrated in the real data experiment.

Conclusions

Spending time to measure the labeling efficiency and the blood T1 instead of acquiring more averages of the PCASL data proves to be advisable for robust CBF quantification in the general population.

Classifying intracranial stenosis disease severity from functional MRI data using machine learning

Translation of many non-invasive hemodynamic MRI methods to cerebrovascular disease patients has been hampered by well-known artifacts associated with delayed blood arrival times and reduced microvascular compliance. Using machine learning and support vector machine (SVM) algorithms, we investigated whether arrival time-related artifacts in these methods could be exploited as novel contrast sources to discriminate angiographically confirmed stenotic flow territories. Intracranial steno-occlusive moyamoya patients (n = 53; age = 45 ± 14.2 years; sex = 43 F) underwent (i) catheter angiography, (ii) anatomical MRI, (iii) cerebral blood flow (CBF)-weighted arterial spin labeling, and (iv) cerebrovascular reactivity (CVR)-weighted hypercapnic blood-oxygenation-level-dependent MRI. Mean, standard deviation (std), and 99th percentile of CBF, CVR, CVR_Delay, and CVR_Max were calculated in major anterior and posterior flow territories perfused by vessels with vs. without stenosis (≥70%) confirmed by catheter angiography. These and demographic variables were input into SVMs to evaluate discriminatory capacity for stenotic flow territories using k-fold cross-validation and receiver-operating-characteristic-area-under-the-curve to quantify variable combination relevance. Anterior circulation CBF-std, attributable to heterogeneous endovascular signal and prolonged arterial transit times, was the best performing single variable and CVR_Delay-mean and CBF-std, both reflective of delayed vascular compliance, were a high-performing two-variable combination (specificity = 0.67; sensitivity = 0.75). Findings highlight the relevance of hemodynamic imaging and machine learning for identifying cerebrovascular impairment.

Robust single-shot acquisition of high resolution whole brain ASL images by combining time-dependent 2D CAPIRINHA sampling with spatio-temporal TGV reconstruction

For ASL perfusion imaging in clinical settings the current guidelines recommends pseudo-continuous arterial spin labeling with segmented 3D readout. This combination achieves the best signal to noise ratio with reasonable resolution but is prone to motion artifacts due to the segmented readout. Motion robust single-shot 3D acquisitions suffer from image blurring due to the T2 decay of the sampled signals during the long readout. To tackle this problem, we propose an accelerated 3D-GRASE sequence with a time-dependent 2D-CAIPIRINHA sampling pattern. This has several advantages: First, the single-shot echo trains are shortened by the acceleration factor; Second, the temporal incoherence between measurements is increased; And third, the coil sensitivity maps can be estimated directly from the averaged k-space data. To obtain improved perfusion images from the undersampled time series, we developed a variational image reconstruction approach employing spatio-temporal total-generalized-variation (TGV) regularization. The proposed ASL-TGV method reduced the total acquisition time, improved the motion robustness of 3D ASL data, and the image quality of the cerebral blood flow (CBF) maps compared to those by a standard segmented approach. An evaluation was performed on 5 healthy subjects including intentional movement for 2 subjects. Single-shot whole brain CBF-maps with high resolution3.1 × 3.1 × 3 mm and image quality can be acquired in 1min 46sec. Additionally high quality CBF- and arterial transit time (ATT) -maps from single-shot multi-post-labeling delay (PLD) data can be gained with the proposed method. This method may improve the robustness of 3D ASL in clinical settings, and may be applied for perfusion fMRI.

Quantification of cerebral perfusion and cerebrovascular reserve using Turbo-QUASAR arterial spin labeling MRI

Purpose

To compare cerebral blood flow (CBF) and cerebrovascular reserve (CVR) quantification from Turbo‐QUASAR (quantitative signal targeting with alternating radiofrequency labeling of arterial regions) arterial spin labeling (ASL) and single post‐labeling delay pseudo‐continuous ASL (PCASL).

Methods

A model‐based method was developed to quantify CBF and arterial transit time (ATT) from Turbo‐QUASAR, including a correction for magnetization transfer effects caused by the repeated labeling pulses. Simulations were performed to assess the accuracy of the model‐based method. Data from an in vivo experiment conducted on a healthy cohort were retrospectively analyzed to compare the CBF and CVR (induced by acetazolamide) measurement from Turbo‐QUASAR and PCASL on the basis of global and regional differences. The quality of the two ASL data sets was examined using the coefficient of variation (CoV).

Results

The model‐based method for Turbo‐QUASAR was accurate for CBF estimation (relative error was 8% for signal‐to‐noise ratio = 5) in simulations if the bolus duration was known. In the in vivo experiment, the mean global CVR estimated by Turbo‐QUASAR and PCASL was between 63% and 64% and not significantly different. Although global CBF values of the two ASL techniques were not significantly different, regional CBF differences were found in deep gray matter in both pre‐ and postacetazolamide conditions. The CoV of Turbo‐QUASAR data was significantly higher than PCASL.

Conclusion

Both ASL techniques were effective for quantifying CBF and CVR, despite the regional differences observed. Although CBF estimated from Turbo‐QUASAR demonstrated a higher variability than PCASL, Turbo‐QUASAR offers the advantage of being able to measure and control for variation in ATT.

Imaging Clinically Relevant Pain States Using Arterial Spin Labeling

Arterial Spin Labeling (ASL) is a perfusion-based functional magnetic resonance imaging technique that uses water in arterial blood as a freely diffusible tracer to measure regional cerebral blood flow (rCBF) noninvasively. To date its application to the study of pain has been relatively limited. Yet, ASL possesses key features that make it uniquely positioned to study pain in certain paradigms. For instance, ASL is sensitive to very slowly fluctuating brain signals (in the order of minutes or longer). This characteristic makes ASL particularly suitable to the evaluation of brain mechanisms of tonic experimental, post-surgical and ongoing/or continuously varying pain in chronic or acute pain conditions (whereas BOLD fMRI is better suited to detect brain responses to short-lasting or phasic/evoked pain). Unlike positron emission tomography or other perfusion techniques, ASL allows the estimation of rCBF without requiring the administration of radioligands or contrast agents. Thus, ASL is well suited for within-subject longitudinal designs (e.g., to study evolution of pain states over time, or of treatment effects in clinical trials). ASL is also highly versatile, allowing for novel paradigms exploring a flexible array of pain states, plus it can be used to simultaneously estimate not only pain-related alterations in perfusion but also functional connectivity. In conclusion, ASL can be successfully applied in pain paradigms that would be either challenging or impossible to implement using other techniques. Particularly when used in concert with other neuroimaging techniques, ASL can be a powerful tool in the pain imager's toolbox.

A general framework for optimizing arterial spin labeling MRI experiments

PURPOSE

Arterial spin labeling (ASL) MRI is a non-invasive perfusion imaging technique that is inherently SNR limited, so scan protocols ideally need to be rigorously optimized to provide the most accurate measurements. A general framework is presented for optimizing ASL experiments to achieve optimal accuracy for perfusion estimates and, if required, other hemodynamic parameters, within a fixed scan time. The effectiveness of this framework is then demonstrated by optimizing the post-labeling delays (PLDs) of a multi-PLD pseudo-continuous ASL experiment and validating the improvement using simulations and in vivo data.

THEORY AND METHODS

A simple framework is proposed based on the use of the Cramér-Rao lower bound to find the protocol design which minimizes the predicted parameter estimation errors. Protocols were optimized for cerebral blood flow (CBF) accuracy or both CBF and arterial transit time (ATT) accuracy and compared to a conventional multi-PLD protocol, with evenly spaced PLDs, and a single-PLD protocol, using simulations and in vivo experiments in healthy volunteers.

RESULTS

Simulations and in vivo data agreed extremely well with the predicted performance of all protocols. For the in vivo experiments, optimizing for just CBF resulted in a 48% and 15% decrease in CBF errors, relative to the reference multi-PLD and single-PLD protocols, respectively. Optimizing for both CBF and ATT reduced CBF errors by 37%, without a reduction in ATT accuracy, relative to the reference multi-PLD protocol.

CONCLUSION

The presented framework can effectively design ASL experiments to minimize measurement errors based on the requirements of the scan.