Accelerated 3D‐GRASE imaging improves quantitative multiple post labeling delay arterial spin labeling

Boland, Markus; Stirnberg, Rüdiger; Pracht, Eberhard D.; Kramme, Johanna; Viviani, Roberto; Stingl, Julia C.; Stöcker, Tony

doi:10.1002/mrm.27226

Cited by 26 publications

(24 citation statements)

References 32 publications

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“…Since the presented technique for determining is still under investigation, this study has some limitations and potential sources of error in quantification. Physiological fluctuations can be minimized but not completely suppressed by background saturation and may disturb the ASL data 11 , 27 . In addition, the patient positioning and labeling alignment may affect the measurements twofold: 28 First, an unfavorable placement of the frequency-sensitive and vessel-flow-dependent labeling slab leads to a lower ASL signal and thus to potential CBF underestimations.…”

Section: Discussionmentioning

confidence: 99%

“…Generally, a low SNR can lead to inaccuracies and reduced precision at the parameter fitting stage, but also during preprocessing, in particular motion correction and normalization. One option to achieve better SNR per image or more SNR-equivalent images in the same scan time in parallel imaging, is to employ CAIPIRINHA instead of conventional GRAPPA sampling 27 , 29 . Unfortunately, the scanner software used in this study did not allow for online reconstruction of CAIPIRINHA data.…”

Section: Discussionmentioning

confidence: 99%

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Reliability of quantitative transverse relaxation time mapping with $${\text{T}}_{{2}}$$-prepared whole brain pCASL

Schidlowski

Stirnberg

Stöcker

et al. 2020

Sci Rep

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Arterial spin labeling (ASL) is increasingly applied for cerebral blood flow mapping, but $${\text{T}}_{{2}}$$ T 2 relaxation of the ASL signal magnetization is often ignored, although it may be clinically relevant. To investigate the extent, to which quantitative $${\text{T}}_{{2}}$$ T 2 values in gray matter (GM) obtained by pseudocontinuous ASL (pCASL) perfusion MRI can be reproduced, are reliable and a potential neuroscientific biomarker, a prospective study was performed with ten healthy volunteers (5F,28 ± 3y) at a 3 T scanner. A $${\text{T}}_{{2}}$$ T 2 -prepared pCASL sequence enabled the measurement of quantitative $${\text{T}}_{{2}}$$ T 2 and perfusion maps. $${\text{T}}_{{2}}$$ T 2 times were modeled per voxel and analyzed within four GM-regions-of-interest (ROI). The intraclass correlation coefficients (ICCs) of the quantified ASL-$${\text{T}}_{{2}}$$ T 2 varied across brain regions. When averaged across subjects and postlabeling delays (PLDs), the ICCs ranged from reasonable values in parietal regions (ICC = 0.56) to smaller values in frontal regions (ICC = 0.36). Corresponding subject-averaged within-subject coefficients of variation (WSCVs) showed good test–retest measurement precision ($${\text{WSCV}}_{{{\text{PLD}}}} \le 0.14$$ WSCV PLD ≤ 0.14 for all PLDs), but more pronounced inter-subject variance. Reliability and precision of quantified ASL-$${\text{T}}_{{2}}$$ T 2 were region-, PLD- and subject-specific, showing fair to robust results in occipital, parietal and temporal ROIs. The results give rise to consider the method for future cerebral studies, where variable perfusion or altered $${\text{T}}_{{2}}$$ T 2 times are suspected.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Reliability of quantitative transverse relaxation time mapping with $${\text{T}}_{{2}}$$-prepared whole brain pCASL

Schidlowski

Stirnberg

Stöcker

et al. 2020

Sci Rep

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“…An inhouse-developed pseudocontinuous arterial spin labeling (pCASL) sequence 15 (Figure 1) was extended by a T 2 preparation module prior to the GRASE readout. It enables the acquisition of multiple T 2 -weightings, resulting in different echo times (TEs) and the acquisition of multiple postlabeling delays (PLDs).…”

Section: Asl Sequencementioning

confidence: 99%

“…The scan protocol is based on the recommendations of the ISMRM consensus paper on ASL imaging 1 and a previous study. 15 The following ASL parameters were applied: unbalanced pCASL labeling, a labeling duration of 1.8 s (as a compromise between signal strength for T 2 -weighting, acquisition time and SAR) and TE T2Prep lengths of 0/30/40/60/80/120/160 ms. In order to achieve a steady state, two preparation scans were acquired and removed before data analysis.…”

Section: Monoexponential T 2 Estimation Protocols (Prot IV /Prot Ev )mentioning

confidence: 99%

Blood–brain barrier permeability measurement by biexponentially modeling whole‐brain arterial spin labeling data with multiple T₂‐weightings

et al. 2020

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Blood–brain barrier (BBB) permeability assessment remains of ongoing interest in clinical practice and research. Transitions between intravascular (IV) and extravascular (EV) gray matter (GM) compartments may provide information regarding the microstructural status of the BBB. Due to different transverse relaxation times (T2) of water protons in vessels and GM, it is possible to determine the compartment in which these protons are located. This work presents and investigates the feasibility of a simplified analytical approach for compartmentalizing the proportions of magnetically marked water protons into IV and EV GM components by biexponentially modeling T2‐weighted arterial spin labeling (ASL) data. Numerous model assumptions were used to stabilize the fit and achieve in vivo applicability. Particularly, transverse relaxation times of IV and EV water protons were determined from the analysis of two supporting T2‐weighted ASL measurements, utilizing a monoexponential signal model. This stabilized a two‐parameter biexponential fit of ASL data with T2 preparation (PLD = 0.9/1.2/1.5/1.8 s, TET2Prep = 0/30/40/60/80/120/160 ms), which thereby robustly provided estimates of the IV and EV compartment fractions. Experiments were conducted with three healthy volunteers in a 3 T scanner. Averaged over all subjects, the labeled water protons inherit T2,IV = 200 ± 18 ms initially and adapt T2,EV = 91 ± 2 ms with a longer retention time in cerebral structures. Accordingly, the EVlocated ASL signal fraction rises with increasing PLD from 0.31 ± 0.11 at the shortest PLD of 0.9 s to 0.73 ± 0.02 at the longest PLD of 1.8s. These results indicate a transition of the water protons from IV to EV space. The findings support the potential of biexponential modeling for compartmentalizing ASL spin fractions between IV and EV space. The novel integration of monoexponential parameter estimates stabilizes the two‐compartment model fit, suggesting that this technique is suitable for robustly estimating the BBB permeability in vivo.

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“…Partial‐volume effects can be reduced by increasing the acquisition’s spatial resolution; however, the reduced SNR requires more averaging (ie, longer acquisition time), which increases motion sensitivity. Hence, a number of denoising and undersampled MRI techniques have been proposed to reduce noise while using as few averages as possible. Currently, PVC involves several preprocessing steps of ASL images (deconvolution, denoising, and motion correction) and of structural MR image (registration, segmentation, and downsampling) .…”

Section: Introductionmentioning

confidence: 99%

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

Mehranian

McGinnity

Neji

et al. 2020

Magnetic Resonance in Med

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Running head title:High-resolution ASL image reconstruction Word count: ~6000Abstract Purpose: A model-based reconstruction framework is proposed for MOtion-Corrected and High-resolution anatomically-Assisted (MOCHA) reconstruction of ASL data. In this framework, all low-resolution ASL controllabel pairs are used to reconstruct a single high-resolution cerebral blood flow (CBF) map, corrected for rigid motion, point-spread-function (PSF) blurring and partial-volume effect (PVE).Methods: Six volunteers were recruited for CBF imaging using PCASL labelling, 2-shot 3D-GRASE sequences and high-resolution T1-weighted MRI. For two volunteers, high-resolution scans with double and triple resolution in the partition direction were additionally collected. Simulations were designed for evaluations against a highresolution ground-truth CBF map, including a simulated hyper-perfused lesion and hyper/hypo-perfusion abnormalities. MOCHA was compared to standard reconstruction and a 3D linear regression (3DLR) PVE correction method and was further evaluated for acquisitions with reduced control-label pairs and k-space undersampling. Results:MOCHA reconstructions of low-resolution ASL data showed enhanced image quality particularly in the partition direction. In simulations, both MOCHA and 3DLR provided more accurate CBF maps than the standard reconstruction, however MOCHA resulted in the lowest errors and well delineated the abnormalities. MOCHA reconstruction of standard-resolution in-vivo data showed good agreement with higher-resolution scans requiring 4× and 9× longer acquisitions. MOCHA was found to be robust for 4×-accelerated ASL acquisitions, achieved by reduced control-label pairs or k-space undersampling. Conclusion: MOCHA reconstruction reduces PVE by direct reconstruction of CBF maps in the high-resolution space of the corresponding anatomical image, incorporating motion correction and PSF modelling. Following further evaluation, MOCHA should promote the clinical application of ASL.

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Accelerated 3D‐GRASE imaging improves quantitative multiple post labeling delay arterial spin labeling

Cited by 26 publications

References 32 publications

Reliability of quantitative transverse relaxation time mapping with $${\text{T}}_{{2}}$$-prepared whole brain pCASL

Reliability of quantitative transverse relaxation time mapping with $${\text{T}}_{{2}}$$-prepared whole brain pCASL

Blood–brain barrier permeability measurement by biexponentially modeling whole‐brain arterial spin labeling data with multiple T₂‐weightings

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

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Accelerated 3D‐GRASE imaging improves quantitative multiple post labeling delay arterial spin labeling

Cited by 26 publications

References 32 publications

Reliability of quantitative transverse relaxation time mapping with $${\text{T}}_{{2}}$$-prepared whole brain pCASL

Reliability of quantitative transverse relaxation time mapping with $${\text{T}}_{{2}}$$-prepared whole brain pCASL

Blood–brain barrier permeability measurement by biexponentially modeling whole‐brain arterial spin labeling data with multiple T2‐weightings

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

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Blood–brain barrier permeability measurement by biexponentially modeling whole‐brain arterial spin labeling data with multiple T₂‐weightings