Background: Cerebral small vessel disease (SVD) lesions on MRI are common in patients with cognitive impairment. It has been suggested that cerebral hypoperfusion is involved in the etiology of... Show moreBackground: Cerebral small vessel disease (SVD) lesions on MRI are common in patients with cognitive impairment. It has been suggested that cerebral hypoperfusion is involved in the etiology of these lesions. Objective: The aim of the study was to determine the relationship between cerebral blood flow (CBF) and SVD burden in patients referred to a memory clinic with SVD on MRI. Method: We included 132 memory clinic patients (mean age 73 +/- 10, 56% male) with SVD on MRI. We excluded patients with large non-lacunar cortical infarcts. Global CBF (mL/min per 100 mL of brain tissue) was derived from 2-dimensional phase-contrast MRI focused on the internal carotid arteries and the basilar artery. SVD burden was defined as the sum of (each 1 point): white matter hyperintensities (WMHs) Fazekas 1 or more, lacunes, microbleeds (MBs), or enlarged perivascular spaces (PVS) presence, and each SVD feature separately. Linear regression analyses were performed to study the association between CBF and SVD burden, age- and sex-adjusted. Results: Median SVD burden score was 2, 36.4% of patients had MBs, 35.6% lacunar infarcts, 48.4% intermediate to severe enlarged PVS, and 57.6% a WMH Fazekas score 2 or more. Median WMH volume was 21.4 mL (25% quartile: 9.6 mL, 75% quartile: 32.5 mL). Mean CBF +/- SD was 44.0 +/- 11.9 mL/min per 100 mL brain. There was no relation between CBF and overall SVD burden (CBF difference per burden score point [95% CI]: -0.5 [-2.4; 1.4] mL/min/100 mL brain, p = 0.9). CBF did also not differ according to presence or absence or an high burden of any of the individual SVD features. Moreover, there was no significant relation between WMH volume and CBF (CBF difference per ml increase in WMH [95% CI] -0.6 [-1.5; 0.3] mL/min/100 mL brain p = 0.2). Conclusion: Global CBF was not related to overall SVD burden or with individual SVD features in this memory clinic cohort, indicating that in this setting these lesions were not primarily due to cerebral hypoperfusion. Show less
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... Show moreArterial 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. Show less
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... Show moreArterial 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. Show less
The main goal of this thesis is to further improve the ASL methodology for cerebral perfusion imaging by means of technological development. In this thesis, several new ASL techniques are... Show moreThe main goal of this thesis is to further improve the ASL methodology for cerebral perfusion imaging by means of technological development. In this thesis, several new ASL techniques are proposed, which cover the full breath of ASL-methodology, thereby enabling or improving the detection of different aspects of cerebral hemodynamics. VE-DASL is proposed to achieve fast cerebral flow territory mapping within 30 seconds. Time-encoded pCASL with a single voxel PRESS spectroscopic readout was implemented to detect multi-phase white matter perfusion in a highly time efficient manner. To better understand the signal generation mechanisms of IVIM, an ASL-preparation module was employed before an IVIM read-out, the diffusion properties of the blood pool were exclusively measured and the perfusion contribution was isolated. A novel method was proposed, optimized and validated to measure the labeling efficiency of pCASL directly by performing multi-phase pCASL imaging distal to the labeling plane. The longitudinal relaxation times of blood at different magnetic field strengths were measured and the expected gain in SNR by performing ASL at ultra-high magnetic field was quantified. Show less
