Improved partial volume correction for single inversion time arterial spin labeling data

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.

Unsupervised arterial spin labeling image super-resolution via multi-scale generative adversarial network

PURPOSE

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an advanced non-invasive imaging technology that can measure cerebral blood flow (CBF) quantitatively without a contrast agent injection or radiation exposure. However, because of the weak labeling, conventional ASL images usually suffer from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time. Therefore, a method that can simultaneously improve the spatial resolution and SNR is needed.

METHODS

In this work, we proposed an unsupervised super-resolution (SR) method to improve ASL image resolution based on a pyramid of generative adversarial networks (GAN). Through layer-by-layer training, the generators can learn features from the coarsest to the finest. The last layer's generator which contains fine details and textures was used to generate the final SR ASL images. In our proposed framework, the corresponding T1-weighted MR image was supplied as a second-channel input of the generators to provide high-resolution prior information. In addition, a low-pass-filter loss term was included to suppress the noise of the original ASL images. To evaluate the performance of the proposed framework, a simulation study and two real-patient experiments based on the in vivo datasets obtained from 3 healthy subjects on a 3T MR scanner were conducted, regarding the low-resolution (LR) to normal-resolution (NR) and the NR-to-SR tasks. The proposed method was compared to the nearest neighbor interpolation, trilinear interpolation, 3rd order B-splines interpolation methods, and deep image prior (DIP) with the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as the quantification metrics. The averaged ASL images acquired with 44 min acquisition time were used as the ground truth for real-patient LR-to-NR study. The ablation studies of low-pass-filter loss term and T1-weighted MR image were performed based on simulation data.

RESULTS

For the simulation study, results show that the proposed method achieved significantly higher PSNR (p-value < 0.05) and SSIM (p-value < 0.05) than the nearest neighbor interpolation, trilinear interpolation, 3rd order B-splines interpolation, and DIP methods. For the real-patient LR-to-NR experiment, results show that the proposed method can generate high-quality SR ASL images with clearer structure boundaries and low noise levels, and has the highest mean PSNR and SSIM. For real-patient NR-to-SR tasks, the structure of the results using the proposed method is sharper and clearer, which are the most similar to the structure of the reference 44 min acquisition image than other methods. The proposed method also shows the ability to remove artifacts in the NR image while super-resolution. The ablation study verified that the low-pass-filter loss term and T1-weighted MR image are necessary for the proposed method.

CONCLUSIONS

The proposed unsupervised multi-scale GAN framework can simultaneously improve spatial resolution and reduce image noise. Experiment results from simulation data and 3 healthy subjects show that the proposed method achieves better performance than the nearest neighbor interpolation, the trilinear interpolation, the 3rd order B-splines interpolation, and DIP methods. This article is protected by copyright. All rights reserved.

Sensitivity of arterial Spin labeling for characterization of longitudinal perfusion changes in Frontotemporal dementia and related disorders

•

This study demonstrates the value of ASL for longitudinal monitoring of perfusion in FTD patients.

•

Good agreement was found in repeat measures of CBF in patients and controls.

•

Transit times were not a significant source of error for the selected post labeling delay (2 s).

Background

Advances in the understanding of the pathophysiology of frontotemporal dementia (FTD) and related disorders, along with the development of novel candidate disease modifying treatments, have stimulated the need for tools to assess the efficacy of new therapies. While perfusion imaging by arterial spin labeling (ASL) is an attractive approach for longitudinal imaging biomarkers of neurodegeneration, sources of variability between sessions including arterial transit times (ATT) and fluctuations in resting perfusion can reduce its sensitivity. Establishing the magnitude of perfusion changes that can be reliably detected is necessary to delineate longitudinal perfusion changes related to disease processes from the effects of these sources of error.

Purpose

To assess the feasibility of ASL for longitudinal monitoring of patients with FTD by quantifying between-session variability of perfusion on a voxel-by-voxel basis.

Methods and materials

ASL data were collected in 13 healthy controls and 8 patients with FTD or progressive supra-nuclear palsy. Variability in cerebral blood flow (CBF) by single delay pseudo-continuous ASL (SD-pCASL) acquired one month apart were quantified by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). Additionally, CBF by SD-pCASL and ATT by low-resolution multiple inversion time ASL (LowRes-pCASL) were compared to Hadamard encoded sequences which are able to simultaneously measure CBF and ATT with improved time-efficiency.

Results

Agreement of grey-matter perfusion between sessions was found for both patients and controls (CV = 10.8% and 8.3% respectively) with good reliability for both groups (ICC > 0.6). Intensity normalization to remove day-to-day fluctuations in resting perfusion reduced the CV by 28%. Less than 5% of voxels had ATTs above the chosen post labelling delay (2 s), indicating that the ATT was not a significant source of error. Hadamard-encoded perfusion imaging yielded systematically higher CBF compared to SD-pCASL, but produced similar transit-time measurements. Power analysis revealed that SD-pCASL has the sensitivity to detect longitudinal changes as low as 10% with as few as 10 patient participants.

Conclusion

With the appropriate labeling parameters, SD-pCASL is a promising approach for assessing longitudinal changes in CBF associated with FTD.

A Cross-Sectional Study on the Impact of Arterial Stiffness on the Corpus Callosum, a Key White Matter Tract Implicated in Alzheimer's Disease

BACKGROUND

Vascular risk factors such as arterial stiffness play an important role in the etiology of Alzheimer's disease (AD), presumably due to the emergence of white matter lesions. However, the impact of arterial stiffness to white matter structure involved in the etiology of AD, including the corpus callosum remains poorly understood.

OBJECTIVE

The aims of the study are to better understand the relationship between arterial stiffness, white matter microstructure, and perfusion of the corpus callosum in older adults.

METHODS

Arterial stiffness was estimated using the gold standard measure of carotid-femoral pulse wave velocity (cfPWV). Cognitive performance was evaluated with the Trail Making Test part B-A. Neurite orientation dispersion and density imaging was used to obtain microstructural information such as neurite density and extracellular water diffusion. The cerebral blood flow was estimated using arterial spin labelling.

RESULTS

cfPWV better predicts the microstructural integrity of the corpus callosum when compared with other index of vascular aging (the augmentation index, the systolic blood pressure, and the pulse pressure). In particular, significant associations were found between the cfPWV, an alteration of the extracellular water diffusion, and a neuronal density increase in the body of the corpus callosum which was also correlated with the performance in cognitive flexibility.

CONCLUSION

Our results suggest that arterial stiffness is associated with an alteration of brain integrity which impacts cognitive function in older adults.

Partial volume correction in arterial spin labeling perfusion MRI: A method to disentangle anatomy from physiology or an analysis step too far?

The mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE), which in turn confounds the estimation of perfusion into a specific tissue of interest such as gray or white matter. This confound occurs because the image voxels contain a mixture of tissues with disparate perfusion properties, leading to estimated perfusion values that reflect primarily the volume proportions of tissues in the voxel rather than the perfusion of any particular tissue of interest within that volume. It is already recognized that PVE influences studies of brain perfusion, and that its effect might be even more evident in studies where changes in perfusion are co-incident with alterations in brain structure, such as studies involving a comparison between an atrophic patient population vs control subjects, or studies comparing subjects over a wide range of ages. However, the application of PVE correction (PVEc) is currently limited and the employed methodologies remain inconsistent. In this article, we outline the influence of PVE in ASL measurements of perfusion, explain the main principles of PVEc, and provide a critique of the current state of the art for the use of such methods. Furthermore, we examine the current use of PVEc in perfusion studies and whether there is evidence to support its wider adoption. We conclude that there is sound theoretical motivation for the use of PVEc alongside conventional, 'uncorrected', images, and encourage such combined reporting. Methods for PVEc are now available within standard neuroimaging toolboxes, which makes our recommendation straightforward to implement. However, there is still more work to be done to establish the value of PVEc as well as the efficacy and robustness of existing PVEc methods.

Voxel-wise partial volume correction method for accurate estimation of tissue sodium concentration in 23 Na-MRI at 7 T

Sodium is crucial for the maintenance of cell physiology, and its regulation of the sodium-potassium pump has implications for various neurological conditions. The distribution of sodium concentrations in tissue can be quantitatively evaluated by means of sodium MRI (²³ Na-MRI). Despite its usefulness in diagnosing particular disease conditions, tissue sodium concentration (TSC) estimated from ²³ Na-MRI can be strongly biased by partial volume effects (PVEs) that are induced by broad point spread functions (PSFs) as well as tissue fraction effects. In this work, we aimed to propose a robust voxel-wise partial volume correction (PVC) method for ²³ Na-MRI. The method is based on a linear regression (LR) approach to correct for tissue fraction effects, but it utilizes a 3D kernel combined with a modified least trimmed square (3D-mLTS) method in order to minimize regression-induced inherent smoothing effects. We acquired ²³ Na-MRI data with conventional Cartesian sampling at 7 T, and spill-over effects due to the PSF were considered prior to correcting for tissue fraction effects using 3D-mLTS. In the simulation, we found that the TSCs of gray matter (GM) and white matter (WM) were underestimated by 20% and 11% respectively without correcting tissue fraction effects, but the differences between ground truth and PVE-corrected data after the PVC using the 3D-mLTS method were only approximately 0.6% and 0.4% for GM and WM, respectively. The capability of the 3D-mLTS method was further demonstrated with in vivo ²³ Na-MRI data, showing significantly lower regression errors (ie root mean squared error) as compared with conventional LR methods (p < 0.001). The results of simulation and in vivo experiments revealed that 3D-mLTS is superior for determining under- or overestimated TSCs while preserving anatomical details. This suggests that the 3D-mLTS method is well suited for the accurate determination of TSC, especially in small focal lesions associated with pathological conditions.

ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

Motion‐corrected and high‐resolution anatomically assisted (MOCHA) reconstruction of arterial spin labeling MRI

Purpose

A model‐based reconstruction framework is proposed for motion‐corrected and high‐resolution anatomically assisted (MOCHA) reconstruction of arterial spin labeling (ASL) data. In this framework, all low‐resolution ASL control‐label pairs are used to reconstruct a single high‐resolution cerebral blood flow (CBF) map, corrected for rigid‐motion, point‐spread‐function blurring and partial volume effect.

Methods

Six volunteers were recruited for CBF imaging using pseudo‐continuous ASL labeling, two‐shot 3D gradient and spin‐echo sequences and high‐resolution T₁‐weighted MRI. For 2 volunteers, high‐resolution scans with double and triple resolution in the partition direction were additionally collected. Simulations were designed for evaluations against a high‐resolution ground‐truth CBF map, including a simulated hyperperfused lesion and hyperperfusion/hypoperfusion abnormalities. The MOCHA technique was compared with standard reconstruction and a 3D linear regression partial‐volume effect correction method and was further evaluated for acquisitions with reduced control‐label pairs and k‐space undersampling.

Results

The MOCHA reconstructions of low‐resolution ASL data showed enhanced image quality, particularly in the partition direction. In simulations, both MOCHA and 3D linear regression provided more accurate CBF maps than the standard reconstruction; however, MOCHA resulted in the lowest errors and well delineated the abnormalities. The MOCHA reconstruction of standard‐resolution in vivo data showed good agreement with higher‐resolution scans requiring 4‐times and 9‐times longer acquisitions. The MOCHA reconstruction was found to be robust for 4‐times‐accelerated ASL acquisitions, achieved by reduced control‐label pairs or k‐space undersampling.

Conclusion

The MOCHA reconstruction reduces partial‐volume effect by direct reconstruction of CBF maps in the high‐resolution space of the corresponding anatomical image, incorporating motion correction and point spread function modeling. Following further evaluation, MOCHA should promote the clinical application of ASL.

Altered grey matter volume, perfusion and white matter integrity in very low birthweight adults

This study examined the long-term effects of being born very-low-birth-weight (VLBW, <1500 g) on adult cerebral structural development using a multi-method neuroimaging approach. The New Zealand VLBW study cohort comprised 413 individuals born VLBW in 1986. Of the 338 who survived to discharge, 229 were assessed at age 27–29 years. Of these, 150 had a 3 T MRI scan alongside 50 healthy term-born controls. The VLBW group included 53/57 participants born <28 weeks gestation. MRI analyses included: a) structural MRI to assess grey matter (GM) volume and cortical thickness; b) arterial spin labelling (ASL) to quantify GM perfusion; and c) diffusion tensor imaging (DTI) to measure white matter (WM) integrity. Compared to controls, VLBW adults had smaller GM volumes within frontal, temporal, parietal and occipital cortices, bilateral cingulate gyri and left caudate, as well as greater GM volumes in frontal, temporal and occipital areas. Thinner cortex was observed within frontal, temporal and parietal cortices. VLBW adults also had less GM perfusion within limited temporal areas, bilateral hippocampi and thalami. Finally, lower fractional anisotropy (FA) and axial diffusivity (AD) within principal WM tracts was observed in VLBW subjects. Within the VLBW group, birthweight was positively correlated with GM volume and perfusion in cortical and subcortical regions, as well as FA and AD across numerous principal WM tracts. Between group differences within temporal cortices were evident across all imaging modalities, suggesting that the temporal lobe may be particularly susceptible to disruption in development following preterm birth. Overall, findings reveal enduring and pervasive effects of preterm birth on brain structural development, with individuals born at lower birthweights having greater long-term neuropathology.

•

Very-low-birth-weight adults had smaller GM volumes and thinner cortex than controls.

•

VLBW adults also showed regions of larger grey matter volumes and thicker cortex.

•

Several small regions showed lower cerebral perfusion in VLBW adults than in controls.

•

Diffusion tensor MRI suggested poorer WM integrity in VLBW adults than in controls.

•

Within VLBW adults, all MRI measures showed positive associations with birthweight.

Recent progress in ASL

This article aims to provide the reader with an overview of recent developments in Arterial Spin Labeling (ASL) MRI techniques. A great deal of progress has been made in recent years in terms of the SNR and acquisition speed. New strategies have been introduced to improve labeling efficiency, reduce artefacts, and estimate other relevant physiological parameters besides perfusion. As a result, ASL techniques has become a reliable workhorse for researchers as well as clinicians. After a brief overview of the technique's fundamentals, this article will review new trends and variants in ASL including vascular territory mapping and velocity selective ASL, as well as arterial blood volume imaging techniques. This article will also review recent processing techniques to reduce partial volume effects and physiological noise. Next the article will examine how ASL techniques can be leveraged to calculate additional physiological parameters beyond perfusion and finally, it will review a few recent applications of ASL in the literature.

Partial volume correction for arterial spin labeling using the inherent perfusion information of multiple measurements

Background

Arterial spin labeling (ASL) provides a noninvasive way to measure cerebral blood flow (CBF). The CBF estimation from ASL is heavily contaminated by noise and the partial volume (PV) effect. The multiple measurements of perfusion signals in the ASL sequence are generally acquired and were averaged to suppress the noise. To correct the PV effect, several methods were proposed, but they were all performed directly on the averaged image, thereby ignoring the inherent perfusion information of mixed tissues that are embedded in multiple measurements. The aim of the present study is to correct the PV effect of ASL sequence using the inherent perfusion information in the multiple measurements.

Methods

In this study, we first proposed a statistical perfusion model of mixed tissues based on the distribution of multiple measurements. Based on the tissue mixture that was obtained from the high-resolution structural image, a structure-based expectation maximization (sEM) scheme was developed to estimate the perfusion contributions of different tissues in a mixed voxel from its multiple measurements. Finally, the performance of the proposed method was evaluated using both computer simulations and in vivo data.

Results

Compared to the widely used linear regression (LR) method, the proposed sEM-based method performs better on edge preservation, noise suppression, and lesion detection, and demonstrates a potential to estimate the CBF within a shorter scanning time. For in vivo data, the corrected CBF values of gray matter (GM) were independent of the GM probability, thereby indicating the effectiveness of the sEM-based method for the PV correction of the ASL sequence.

Conclusions

This study validates the proposed sEM scheme for the statistical perfusion model of mixed tissues and demonstrates the effectiveness of using inherent perfusion information in the multiple measurements for PV correction of the ASL sequence.

Differential Associations of Diastolic and Systolic Pressures With Cerebral Measures in Older Individuals With Mild Cognitive Impairment

BACKGROUND

Reports on the relative importance of the diastolic and systolic blood pressures (DBP and SBP) in age-related cognitive decline are mixed. Investigating the relation between DBP/SBP and functional and structural brain changes could elucidate which of the 2 measures is more critically important for brain function and, consequently, cognitive impairment.

METHODS

We investigated the association of SBP and DBP with cortical volume, cerebral blood flow (CBF), and white matter lesions (WML), in nondemented older adults with and without mild cognitive impairment (MCI; N = 265, 185 MCI, mean age = 64 years). Brachial blood pressure was measured twice while seated, and the average of the 2 measures was used. Cortical volume, gray matter (GM) CBF, and WML were estimated using T1-weighted imaging, arterial spin labeling, and fluid attenuation inversion recovery, respectively.

RESULTS

Reduced cortical volume was associated with elevated DBP (β= -0.18, P = 0.034) but not with SBP (β = -0.10, P = 0.206). GM CBF was associated with DBP (β = -0.13, P = 0.048) but not with SBP (β = -0.07, P = 0.275). Likewise, CBF within brain regions where MCI patients showed hypoperfusion were only associated with DBP (DBP: β = -0.17, P = 0.005; SBP: β = -0.09, P = 0.120). WML volume was associated with both DBP (β = 0.20, P = 0.005) and SBP (β = 0.30, P < 0.001). For all measures, there was no interaction between DBP/SBP and cognitive status, indicating that these associations were independent of the cognitive status.

CONCLUSIONS

Independently of the cognitive status, DBP is more critically important for GM volume and perfusion, whereas WML is associated with both blood pressures, likely reflecting long-term effect of hypertension and autoregulation dysfunction.

Effects of systematic partial volume errors on the estimation of gray matter cerebral blood flow with arterial spin labeling MRI

OBJECTIVE

Partial volume (PV) correction is an important step in arterial spin labeling (ASL) MRI that is used to separate perfusion from structural effects when computing the mean gray matter (GM) perfusion. There are three main methods for performing this correction: (1) GM-threshold, which includes only voxels with GM volume above a preset threshold; (2) GM-weighted, which uses voxel-wise GM contribution combined with thresholding; and (3) PVC, which applies a spatial linear regression algorithm to estimate the flow contribution of each tissue at a given voxel. In all cases, GM volume is obtained using PV maps extracted from the segmentation of the T1-weighted (T1w) image. As such, PV maps contain errors due to the difference in readout type and spatial resolution between ASL and T1w images. Here, we estimated these errors and evaluated their effect on the performance of each PV correction method in computing GM cerebral blood flow (CBF).

MATERIALS AND METHODS

Twenty-two volunteers underwent scanning using 2D echo planar imaging (EPI) and 3D spiral ASL. For each PV correction method, GM CBF was computed using PV maps simulated to contain estimated errors due to spatial resolution mismatch and geometric distortions which are caused by the mismatch in readout between ASL and T1w images. Results were analyzed to assess the effect of each error on the estimation of GM CBF from ASL data.

RESULTS

Geometric distortion had the largest effect on the 2D EPI data, whereas the 3D spiral was most affected by the resolution mismatch. The PVC method outperformed the GM-threshold even in the presence of combined errors from resolution mismatch and geometric distortions. The quantitative advantage of PVC was 16% without and 10% with the combined errors for both 2D and 3D ASL. Consistent with theoretical expectations, for error-free PV maps, the PVC method extracted the true GM CBF. In contrast, GM-weighted overestimated GM CBF by 5%, while GM-threshold underestimated it by 16%. The presence of PV map errors decreased the calculated GM CBF for all methods.

CONCLUSION

The quality of PV maps presents no argument for the preferential use of the GM-threshold method over PVC in the clinical application of ASL.

Arterial spin labeling for the measurement of cerebral perfusion and angiography

Arterial spin labeling (ASL) is an MRI technique that was first proposed a quarter of a century ago. It offers the prospect of non-invasive quantitative measurement of cerebral perfusion, making it potentially very useful for research and clinical studies, particularly where multiple longitudinal measurements are required. However, it has suffered from a number of challenges, including a relatively low signal-to-noise ratio, and a confusing number of sequence variants, thus hindering its clinical uptake. Recently, however, there has been a consensus adoption of an accepted acquisition and analysis framework for ASL, and thus a better penetration onto clinical MRI scanners. Here, we review the basic concepts in ASL and describe the current state-of-the-art acquisition and analysis approaches, and the versatility of the method to perform both quantitative cerebral perfusion measurement, along with quantitative cerebral angiographic measurement.

Structural Correlation-based Outlier Rejection (SCORE) algorithm for arterial spin labeling time series

PURPOSE

To propose and validate Structural Correlation-based Outlier REjection (SCORE), a novel algorithm for removal of artifacts arising from outlier control-label pairs in 2D arterial spin labeling (ASL) data.

MATERIALS AND METHODS

The proposed method was assessed with respect to other state-of-the-art ASL signal processing approaches using 2D pulsed ASL data obtained with a 3T Siemens scanner from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Longitudinal data from control participants acquired 3 months apart were used to assess within-subject coefficient of variation (wsCV) based on the assumption that the optimal signal processing strategy will minimize control subject retest variability in Cerebral Blood Flow (CBF). SCORE was further evaluated by determining its sensitivity for distinguishing patients with Alzheimer's disease (AD) from controls based on hypoperfusion in predefined regions of interest (ROIs) that are known to be sensitive to AD-related changes.

RESULTS

SCORE coupled with a preprocessing step to discard a few extreme outliers (combined algorithm referred to as SCORE+) reduced wsCV up to 21% in gray matter and 39% in smaller ROIs compared to the reference algorithms. It also provided an average increase in effect size for patient-control differences of 50% compared to other algorithms in a priori ROIs sensitive to AD-related changes. This increase was statistically significant (P < 0.05) for the majority of the ROIs and methods as evaluated by permutation tests.

CONCLUSION

CBF maps generated with SCORE or SCORE + provide improved retest reliability in control subjects while simultaneously increasing sensitivity to pathological CBF effects between controls and patients. J. Magn. Reson. Imaging 2016 Level of Evidence: 2 J. MAGN. RESON. IMAGING 2017;45:1786-1797.

Spatio-temporal TGV denoising for ASL perfusion imaging

In arterial spin labeling (ASL) a perfusion weighted image is achieved by subtracting a label image from a control image. This perfusion weighted image has an intrinsically low signal to noise ratio and numerous measurements are required to achieve reliable image quality, especially at higher spatial resolutions. To overcome this limitation various denoising approaches have been published using the perfusion weighted image as input for denoising. In this study we propose a new spatio-temporal filtering approach based on total generalized variation (TGV) regularization which exploits the inherent information of control and label pairs simultaneously. In this way, the temporal and spatial similarities of all images are used to jointly denoise the control and label images. To assess the effect of denoising, virtual ground truth data were produced at different SNR levels. Furthermore, high-resolution in-vivo pulsed ASL data sets were acquired and processed. The results show improved image quality, quantitative accuracy and robustness against outliers compared to seven state of the art denoising approaches.

A linear mixed perfusion model for tissue partial volume correction of perfusion estimates in dynamic susceptibility contrast MRI: Impact on absolute quantification, repeatability, and agreement with pseudo-continuous arterial spin labeling

PURPOSE

The partial volume effect (PVE) is an important source of bias in brain perfusion measurements. The impact of tissue PVEs in perfusion measurements with dynamic susceptibility contrast MRI (DSC-MRI) has not yet been well established. The purpose of this study was to suggest a partial volume correction (PVC) approach for DSC-MRI and to study how PVC affects DSC-MRI perfusion results.

METHODS

A linear mixed perfusion model for DSC-MRI was derived and evaluated by way of simulations. Twenty healthy volunteers were scanned twice, including DSC-MRI, arterial spin labeling (ASL), and partial volume measurements. Two different algorithms for PVC were employed and assessed.

RESULTS

Simulations showed that the derived model had a tendency to overestimate perfusion values in voxels with high fractions of cerebrospinal fluid. PVC reduced the tissue volume dependence of DSC-MRI perfusion values from 44.4% to 4.2% in gray matter and from 55.3% to 14.2% in white matter. One PVC method significantly improved the voxel-wise repeatability, but PVC did not improve the spatial agreement between DSC-MRI and ASL perfusion maps.

CONCLUSION

Significant PVEs were found for DSC-MRI perfusion estimates, and PVC successfully reduced those effects. The findings suggest that PVC might be an important consideration for DSC-MRI applications. Magn Reson Med 77:2203-2214, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Perfusion Deficits and Functional Connectivity Alterations in Memory-Related Regions of Patients with Post-Traumatic Stress Disorder

To explore the potential alterations in cerebral blood flow (CBF) and functional connectivity of recent onset post-traumatic stress disorder (PTSD) induced by a single prolonged trauma exposure, we recruited 20 survivors experiencing the same coal mining flood disaster as the PTSD (n = 10) and non-PTSD (n = 10) group, respectively. The pulsed arterial spin labeling (ASL) images were acquired with a 3.0T MRI scanner and the partial volume (PV) effect in the images was corrected for better CBF estimation. Alterations in CBF were analyzed using both uncorrected and PV-corrected CBF maps. By using altered CBF regions as regions-of-interest, seed-based functional connectivity analysis was then performed. While only one CBF deficit in right corpus callosum of PTSD patients was detected using uncorrected CBF, three more regions (bilateral frontal lobes and right superior frontal gyrus) were identified using PV-corrected CBF. Furthermore, the regional CBF of right superior frontal gyrus exhibited significantly negative correlation with the symptom severity (r = -0.759, p = 0.018). The resting-state functional connectivity analysis revealed increased connectivity between left frontal lobe and right parietal lobe. The results indicated the symptom-specific perfusion deficits and an aberrant connectivity in memory-related regions of PTSD patients when using PV-corrected ASL data. It also suggested that PV-corrected CBF exhibits more subtle changes that may be beneficial to perfusion and connectivity analysis.

A novel approach to measure local cerebral haematocrit using MRI

The percentage blood volume occupied by red blood cells is known as haematocrit. While it is straightforward to measure haematocrit in large arteries, it is very challenging to do it in microvasculature (cerebral haematocrit). Currently, this can only be done using invasive methods (e.g. PET), but their use is very limited. Local variations in cerebral haematocrit have been reported in various brain abnormalities (e.g. stroke, tumours). We propose a new approach to image cerebral haematocrit using MRI, which relies on combining data from two measurements: one that provideshaematocrit-weightedand other onehaematocrit-independentvalues of the same parameter, thus providing an easily obtainable measurement of this important physiological parameter. Four different implementations are described, with one illustrated as proof-of-concept using data from healthy subjects. Cerebral haematocrit measurements were found to be in general agreement with literature values from invasive techniques (e.g. cerebral/arterial ratios of 0.88 and 0.86 for sub-cortical and cortical regions), and showed good test-retest reproducibility (e.g. coefficient-of-variation: 15% and 13% for those regions). The method was also able to detect statistically significant haematocrit gender differences in cortical regions (p < 0.01). The proposed MRI technique should have important applications in various neurological diseases, such as in stroke and brain tumours.

Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods

Arterial spin labeling (ASL) is an emerging MRI technique for non-invasive measurement of cerebral blood flow (CBF). Compared to invasive perfusion imaging modalities, ASL suffers from low sensitivity due to poor signal-to-noise ratio (SNR), susceptibility to motion artifacts and low spatial resolution, all of which limit its reliability. In this work, the effects of various state of the art image processing techniques for addressing these ASL limitations are investigated. A processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, partial volume effect correction, and CBF quantification was developed and assessed. To further improve the SNR for pseudo-continuous ASL (PCASL) by accounting for errors in tagging efficiency, the data from multiphase (MP) acquisitions were analyzed using a novel weighted-averaging scheme. The performances of each step in terms of SNR and reproducibility were evaluated using test-retest ASL data acquired from 12 young healthy subjects. The proposed processing pipeline was shown to improve the within-subject coefficient of variation and regional reproducibility by 17% and 16%, respectively, compared to CBF maps computed following motion correction but without the other processing steps. The CBF measurements of MP-PCASL compared to PCASL had on average 23% and 10% higher SNR and reproducibility, respectively.

Perfusion magnetic resonance imaging: a comprehensive update on principles and techniques

Perfusion is a fundamental biological function that refers to the delivery of oxygen and nutrients to tissue by means of blood flow. Perfusion MRI is sensitive to microvasculature and has been applied in a wide variety of clinical applications, including the classification of tumors, identification of stroke regions, and characterization of other diseases. Perfusion MRI techniques are classified with or without using an exogenous contrast agent. Bolus methods, with injections of a contrast agent, provide better sensitivity with higher spatial resolution, and are therefore more widely used in clinical applications. However, arterial spin-labeling methods provide a unique opportunity to measure cerebral blood flow without requiring an exogenous contrast agent and have better accuracy for quantification. Importantly, MRI-based perfusion measurements are minimally invasive overall, and do not use any radiation and radioisotopes. In this review, we describe the principles and techniques of perfusion MRI. This review summarizes comprehensive updated knowledge on the physical principles and techniques of perfusion MRI.

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.

A variable flip angle-based method for reducing blurring in 3D GRASE ASL

Arterial Spin Labeling (ASL) is an MRI technique to measure cerebral blood flow directly and noninvasively, and thus provides a more direct quantitative correlate of neural activity than blood-oxygen-level-dependent fMRI. A 3D gradient and spin-echo (GRASE) sequence is capable of enhancing signal-to-noise ratio, and has been shown to be a very useful readout module for ASL sequences. Nonetheless, the introduction of significant blurring in its single-shot version, due to T2 decay along the partition dimension, compromises the achievable spatial resolution, limiting the potential of this technique for whole-brain coverage. To address this issue, a method for reducing blurring based on a variable flip angle (VFA) scheme is proposed in this study for 3D GRASE ASL perfusion. Numerical simulations show that the proposed method is capable of reducing the blurring significantly compared to the standard constant flip angle approach; this result was further confirmed using in vivo data. The proposed VFA method should therefore be of significance to 3D GRASE ASL fMRI studies, since it is able to reduce blurring without sacrificing temporal resolution.

Template-based approach for detecting motor task activation-related hyperperfusion in pulsed ASL data

Arterial spin labeling (ASL) permits the noninvasive measurement of quantitative values of cerebral blood flow (CBF) and is thus well adapted to study inter- and intrasubject perfusion variations whether at rest or during an fMRI task. In this study, a template approach to detect brain activation as a CBF difference between resting and activated groups was compared with a standard generalized linear model (GLM) analysis. A basal perfusion template of PICORE-Q2TIPS ASL images acquired at 3T from a group of 25 healthy subjects (mean age 31.6 ± 8.3 years) was created. The second group of 12 healthy subjects (mean age 28.6 ± 2.7 years) performed a block-design motor task. The template was compared with the mean activated image of the second group both at the individual and at the group level to extract activation maps. The results obtained using a GLM analysis of the whole sequence was used as ground truth for comparison. The influences of spatial normalization using DARTEL registration and of correction of partial volume effects (PVE) in the construction of the template were assessed. Results showed that a basal perfusion template can detect activation-related hyperperfusion in motor areas. The true positive ratio was increased by 2.5% using PVE-correction and by 3.2% using PVE-correction with DARTEL registration. On average, the group comparison presented a 2.2% higher true positive ratio than the one-to-many comparison.

Partial volume correction in arterial spin labeling using a Look-Locker sequence

PURPOSE

Partial volume (PV) effects are caused by limited spatial resolution and significantly affect cerebral blood flow investigations with arterial spin labeling. Therefore, accurate PV correction (PVC) procedures are required. PVC is commonly based on PV maps obtained from segmented high-resolution T1 -weighted images. Segmentation of these images is error-prone, and it can be difficult to coregister these images accurately with the single-shot ASL images such as those created by echo-planar imaging (EPI). In this paper, an alternative method for PV map generation is proposed.

METHODS

The Look-Locker EPI (LL-EPI) acquisition is used for analyzing the T1 -recovery curve and for subsequent PV map generation. The new method was evaluated in five healthy volunteers (mean age 30 ± 3.7 years).

RESULTS

By applying a linear regression method for PVC, a 12% decrease in regression error was reached with the new method.

CONCLUSION

PV maps extraction from LL-EPI is a viable, possibly superior alternative to the standard approach based on segmentation of high-resolution T1 -weighted images.