Enhancement of automated blood flow estimates (ENABLE) from arterial spin‐labeled MRI

Shirzadi, Zahra; Stefanović, Bojana; Chappell, Michael A.; Ramirez, Joel; Schwindt, Graeme; Masellis, Mario; Black, Sandra E.; MacIntosh, Bradley J.

doi:10.1002/jmri.25807

Cited by 33 publications

(38 citation statements)

References 37 publications

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“…Each ASL image includes 104 intermediate ASL difference images obtained from 52 control‐label pairs. A quality control procedure was performed to exclude poor‐quality intermediate ASL difference images . After the quality control step, the number of retained ASL difference images in this sample ranged from 31 to 103 out of the possible 104 intermediate images (mean = 84; SD = 14).…”

Section: Resultsmentioning

confidence: 99%

“…A quality control procedure was performed to exclude poor-quality intermediate ASL difference images. 15 After the quality control step, the number of retained ASL difference images in this sample ranged from 31 to 103 out of the possible 104 intermediate images (mean = 84; SD = 14). Figure 1 shows three representative CBF maps from the participants in this study with their corresponding density plots of voxel intensities in GM.…”

Section: Statistical Assessmentsmentioning

confidence: 87%

“…These include ASL artifacts from head motion, partial volume effects, susceptibility‐induced distortions, and site effects. To reduce image artifacts, we used a recently developed pipeline (ENABLE) that identifies and excludes poor‐quality intermediate ASL difference images and optimized for the chosen postlabeling delay . To address partial volume effects, we corrected ASL CBF maps using tissue segmentation maps obtained from structural images .…”

Section: Discussionmentioning

confidence: 99%

“…To reduce image artifacts, we used a recently developed pipeline (ENABLE) that identifies and excludes poor-quality intermediate ASL difference images and optimized for the chosen postlabeling delay. 15 To address partial volume effects, we corrected ASL CBF maps using tissue segmentation maps obtained from structural images. 16 ASL images in this study were not corrected for susceptibility artifacts, as only a single proton density image was collected.…”

Section: Discussionmentioning

confidence: 99%

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Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images

Shirzadi

Stefanović

Mutsaerts

et al. 2019

Magnetic Resonance Imaging

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Background The spatial coefficient of variation (sCoV) of arterial spin‐labeled (ASL) MRI can index cerebral blood flow spatial heterogeneity. This metric reflects delayed blood delivery—seen as a hyperintense ASL signal juxtaposed by hypointense regions. Purpose To investigate the use of ASL‐sCoV in the classification of cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) cohorts. Study Type Prospective/cohort. Population Baseline ASL images from AD neuroimaging initiative dataset in three groups of CU, MCI, and AD (N = 258). Field Strength/Sequence Pulsed ASL (PICORE QT2) images were acquired on 3 T Siemens systems (TE/TR = 12/3400 msec, TI1/2 = 700/1900 msec). Assessment ASL‐sCoV was calculated in temporal, parietal, occipital, and frontal lobes as well as whole gray matter. Statistical Tests The primary analysis used an analysis of covariance to investigate sCoV and cognitive group (CU, MCI, AD) associations. We also evaluated the repeatability of sCoV by calculating within‐subject agreement in a subgroup of CU participants with a repeat ASL. The secondary analyses assessed ventricular volume, amyloid burden, glucose uptake, ASL‐sCoV, and regional CBF as cognitive group classifiers using logistic regression models and receiver operating characteristic analyses. Results We found that global and temporal lobe sCoV differed between cognitive groups (P = 0.006). Post‐hoc tests showed that temporal lobe sCoV was lower in CU than in MCI (Cohen's d = –0.36) or AD (Cohen's d = –1.36). We found that sCoV was moderately repeatable in CU (intersession intraclass correlation = 0.50; intrasession intraclass correlation = 0.88). Subsequent logistic regression analyses revealed that temporal lobe sCoV and amyloid uptake classified CU vs. MCI (P < 0.01; accuracy = 78%). Temporal lobe sCoV, amyloid, and glucose uptake classified CU vs. AD (P < 0.01; accuracy = 97%); glucose uptake significantly classified MCI vs. AD (P < 0.01; accuracy = 85%). Data Conclusion We showed that ASL spatial heterogeneity can be used alongside AD neuroimaging markers to distinguish cognitive groups, in particular, cognitively unimpaired from cognitively impaired individuals. Level of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:858–867.

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Section: Resultsmentioning

confidence: 99%

Section: Statistical Assessmentsmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images

Shirzadi

Stefanović

Mutsaerts

et al. 2019

Magnetic Resonance Imaging

Self Cite

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“…Another tool, Automatic Software for ASL Processing (ASAP), is a MATLAB‐based package that wraps scripts into a user‐friendly interface and is not based on the command line. In addition to the above‐mentioned toolboxes, which provide comprehensive processing of ASL MRI data, several algorithms aiming to improve specific processing steps have also been proposed, such as Enhancement of Automated Blood Flow Estimates (ENABLE) for eliminating poor volumes of ASL signal, and Voxel‐Wise Functional Connectomics for functional ASL denoising …”

Section: Discussionmentioning

confidence: 99%

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

Liu

et al. 2018

NMR in Biomedicine

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

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The association between resting‐state functional magnetic resonance imaging and aortic pulse‐wave velocity in healthy adults

Hussein

Matthews

Syme

et al. 2020

Human Brain Mapping

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Resting-state functional magnetic resonance imaging (rs-fMRI) is frequently used to study brain function; but, it is unclear whether BOLD-signal fluctuation amplitude and functional connectivity are associated with vascular factors, and how vascularhealth factors are reflected in rs-fMRI metrics in the healthy population. As arterial stiffening is a known age-related cardiovascular risk factor, we investigated the associations between aortic stiffening (as measured using pulse-wave velocity [PWV]) and rs-fMRI metrics. We used cardiac MRI to measure aortic PWV (an established indicator of whole-body vascular stiffness), as well as dual-echo pseudo-continuous arterial-spin labeling to measure BOLD and CBF dynamics simultaneously in a group of generally healthy adults. We found that: (1) higher aortic PWV is associated with lower variance in the resting-state BOLD signal; (2) higher PWV is also associated with lower BOLD-based resting-state functional connectivity; (3) regions showing lower connectivity do not fully overlap with those showing lower BOLD variance with higher PWV; (4) CBF signal variance is a significant mediator of the above findings, only when averaged across regions-of-interest. Furthermore, we found no significant association between BOLD signal variance and systolic blood pressure, which is also a known predictor of vascular stiffness. Age-related vascular stiffness, as measured by PWV, provides a unique scenario to demonstrate the extent of vascular bias in rs-fMRI signal fluctuations and functional connectivity. These findings suggest that a substantial portion of age-related rs-fMRI differences may be driven by vascular effects rather than directly by brain function. K E Y W O R D Saging, aortic stiffness, arterial stiffening, blood pressure, blood-oxygenation level dependent signal (BOLD), BOLD signal amplitude, functional connectivity, functional MRI (fMRI), phasecontrast imaging, pulse wave velocity, resting-state fMRI

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Enhancement of automated blood flow estimates (ENABLE) from arterial spin‐labeled MRI

Abstract: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:647-655.

Cited by 33 publications

References 37 publications

Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images

Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images

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

The association between resting‐state functional magnetic resonance imaging and aortic pulse‐wave velocity in healthy adults

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