The feasibility of quantitative MRI of perivascular spaces at 7 T

Cai, Kejia; Tain, Rongwen; Das, Sandhitsu R.; Damen, Frederick W.; Sui, Yi; Vályi-Nagy, Tibor; Elliott, Mark A.; Zhou, Xiaohong Joe

doi:10.1016/j.jneumeth.2015.09.001

Cited by 56 publications

(70 citation statements)

References 46 publications

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“…Recently, [12] and [13] presented computational methods to obtain quantificative measurements of PVS and validated the usefulness of their procedures in clinical research, but both approaches are semi-automatic being, therefore, prone to interobserver variations and could be time-consuming. [14] also proposed a method for quantifying PVS using high-resolution 7T MRI scanners but the use of such field strengths, although providing good spatial resolution and signal-to-noise ratio, has limited clinical use. [15] use a Frangi filter whose parameters are optimized by means of the ordered logit model to enhance the differentiation between PVS and the background, but is unsuitable for images with anisotropic voxels commonly used in clinical settings (e.g.…”

Section: Introductionmentioning

confidence: 99%

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

et al. 2017

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Correspondence: Víctor González-Castro (victor.gonzalez@ed.ac.uk) or María del C. Valdés Hernández (M. Valdes-Hernan@ed.ac.uk) In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform's coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58-0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53-0.72)) and comparable between both the observers (κ = 0.68 (0.61-0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

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Section: Introductionmentioning

confidence: 99%

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

et al. 2017

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“…Improving upon the previous study at 7T, 23 we implemented the EPVS quantification method for the MRI dataset collected at a clinical 3T scanner. Compared to 7T EPVS MR imaging, the lower magnetic field strength of these scanners entails a lower detection sensitivity and a reduced signal to noise ratio.…”

Section: Discussionmentioning

confidence: 99%

“…Although Cai et al has already established that the EPVS density is similarly increased in AD patients in MR images acquired at 7T, 23 it would be interesting to compare the EPVS level between MCI and AD patients at 3T in order to understand the time course of PVS enlargement during the development of AD from cognitively intact, to MCI, and finally, to clinically manifest dementia or AD. With large sample size, it will also be interesting to see if EPVS density may discriminate out MCI patients whose clinical symptoms are less evident and who have low neuropsychiatric testing scores.…”

Section: Discussionmentioning

confidence: 99%

“…A quantitative method for mapping brain PVS in MR images of healthy and AD patients acquired on a 7T whole-body MR scanner has previously been reported in Cai et al 23 Using a train of algorithms, including pixel-wise spatial gradient, the hyperintense PVS were segmented and their density calculated as a percentage of volume fraction (v/v%). The initial results demonstrated that the PVS density is significantly higher in the AD patients when compared to healthy controls.…”

Section: Introductionmentioning

confidence: 99%

“…The initial results demonstrated that the PVS density is significantly higher in the AD patients when compared to healthy controls. 23 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantitative MRI of Perivascular Spaces at 3T for Early Diagnosis of Mild Cognitive Impairment

Niazi¹,

Karaman

Das

et al. 2018

AJNR Am J Neuroradiol

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Background and Purpose The limitations inherent to the current methods of diagnosing Mild Cognitive Impairment (MCI) have constrained the use of early therapeutic interventions to delay the progression of MCI to dementia. This study evaluated whether quantifying enlarged perivascular spaces (EPVS) observed on MR imaging can help us differentiate MCI from cognitively healthy controls and, thus, have an application in the diagnosis of MCI. Methods We automated the identification of EPVS in brain MR Images using a custom quantitative program designed with MATLAB. We then quantified EPVS densities for MCI patients (n=14) and age-matched cognitively healthy controls (n=15), and compared them to determine if EPVS density can serve as an imaging surrogate for MCI diagnosis. Results Quantified as a percentage of volume fraction, EPVS densities were calculated to be 2.82±0.40 v/v% for the controls and 4.17±0.57 v/v% for the MCI group in the subcortical brain (p<0.0001), and 2.74±0.57 v/v% for the controls and 3.90±0.62 v/v% for the MCI cohort in the basal ganglia (p<0.0001). Maximum intensity projections exhibited a visually conspicuous difference in the EPVS distributions for an MCI and a control patient. Using ROC curve analysis, the sensitivity and the specificity for using EPVS as a differentiating biomarker between MCI and controls were determined to be 92.86% and 93.33% respectively. Conclusion EPVS density was found to be significantly higher in MCI compared to age-matched healthy control subjects. EPVS density, therefore, may be a useful imaging biomarker for the diagnosis of MCI.

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5‐Year Associations among Cerebral Arterial Pulsatility, Perivascular Space Dilation, and White Matter Lesions

Vikner

Karalija

Eklund

et al. 2022

Annals of Neurology

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High cerebral arterial pulsatility index (PI), white matter lesions (WMLs), enlarged perivascular spaces (PVSs), and lacunar infarcts are common findings in the elderly population, and considered indicators of small vessel disease (SVD). Here, we investigate the potential temporal ordering among these variables, with emphasis on determining whether high PI is an early or delayed manifestation of SVD. Methods: In a population-based cohort, 4D flow MRI data for cerebral arterial pulsatility was collected for 159 participants at baseline (age 64-68), and for 122 participants at follow-up 5 years later. Structural MRI was used for WML and PVS segmentation, and lacune identification. Linear mixed-effects (LME) models were used to model longitudinal changes testing for pairwise associations, and latent change score (LCS) models to model multiple relationships among variables simultaneously. Results: Longitudinal 5-year increases were found for WML, PVS, and PI. Cerebral arterial PI at baseline did not predict changes in WML or PVS volume. However, WML and PVS volume at baseline predicted 5-year increases in PI. This was shown for PI increases in relation to baseline WML and PVS volumes using LME models (R ≥ 0.24; p < 0.02 and R ≥ 0.23; p < 0.03, respectively) and LCS models (β = 0.28; p = 0.015 and β = 0.28; p = 0.009, respectively). Lacunes at baseline were unrelated to PI. Interpretation: In healthy older adults, indicators of SVD are related in a lead-lag fashion, in which the expression of WML and PVS precedes increases in cerebral arterial PI. Hence, we propose that elevated PI is a relatively late manifestation, rather than a risk factor, for cerebral SVD.

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The feasibility of quantitative MRI of perivascular spaces at 7 T

Cited by 56 publications

References 46 publications

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

Quantitative MRI of Perivascular Spaces at 3T for Early Diagnosis of Mild Cognitive Impairment

5‐Year Associations among Cerebral Arterial Pulsatility, Perivascular Space Dilation, and White Matter Lesions

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