Thomas T. Liu

Purpose: By leveraging the small‐vessel specificity of velocity‐selective arterial spin labeling (VSASL), we present a novel technique for measuring cerebral MicroVascular Pulsatility named MVP‐VSASL. Theory and Methods: We present a theoretical model relating the pulsatile, cerebral blood flow‐driven VSASL signal to the microvascular pulsatility index (), a widely used metric for quantifying cardiac‐dependent fluctuations. The model describes the dependence of the of VSASL signal (denoted ) on bolus duration (an adjustable VSASL sequence parameter) and provides guidance for selecting a value of that maximizes the SNR of the measurement. The model predictions were assessed in humans using data acquired with retrospectively cardiac‐gated VSASL sequences over a broad range of values. In vivo measurements were also used to demonstrate the feasibility of whole‐brain voxel‐wise pulsatility mapping, assess intrasession repeatability of , and illustrate the potential of this method to explore an association with age. Results: The theoretical model showed excellent agreement to the empirical data in a gray matter region of interest (average value of 0.898 0.107 across six subjects). We further showed excellent intrasession repeatability of the pulsatility measurement (, ) and the potential to characterize associations with age (, ). Conclusion: We have introduced a novel, VSASL‐based cerebral microvascular pulsatility technique, which may facilitate investigation of cognitive disorders where damage to the microvasculature has been implicated.

Purpose: To develop a generalized signal model for dual‐module velocity‐selective arterial spin labeling (dm‐VSASL) that can integrate arbitrary saturation and inversion profiles.Theory and Methods: A recently developed mathematical framework for single‐module VSASL is extended to address the increased complexity of dm‐VSASL and to model the use of realistic velocity‐selective profiles in the label‐control and vascular crushing modules. Expressions for magnetization difference, arterial delivery functions, labeling efficiency, and cerebral blood flow (CBF) estimation error are presented. Sources of error are examined and timing requirements to minimize quantification errors are derived.Results: For ideal velocity‐selective profiles, the predicted signals match those of prior work. With realistic profiles, a CBF‐dependent estimation error can occur when velocity‐selective inversion (VSI) is used for the labeling modules and velocity‐selective saturation (VSS) is used for the vascular crushing module. The error reflects a mismatch between the leading and trailing edges of the delivery function for the second bolus and can be minimized by choosing a nominal labeling cutoff velocity that is lower than the nominal saturation cutoff velocity. In the presence of and inhomogeneities, the labeling efficiency of dual‐module VSI is more attenuated than that of dual‐module VSS.Conclusion: The proposed signal model will enable researchers to more accurately assess and compare the performance of realistic dm‐VSASL implementations and improve the quantification of dm‐VSASL CBF measures.

Purpose: To mitigate the B₀/B₁⁺ sensitivity of velocity‐selective inversion (VSI) pulse trains for velocity‐selective arterial spin labeling (VSASL) by implementing adiabatic refocusing. This approach aims to achieve artifact‐free VSI‐based perfusion imaging through single‐pair label‐control subtractions, reducing the need for the currently required four‐pair dynamic phase‐cycling (DPC) technique when using a velocity‐insensitive control.Methods: We introduce a Fourier‐transform VSI (FT‐VSI) train that incorporates sinc‐modulated hard excitation pulses with MLEV‐8‐modulated adiabatic hyperbolic secant refocusing pairs. We compare performance between this train and the standard composite refocusing train, including with and without DPC, for dual‐module VSI VSASL. We evaluate (1) simulated velocity‐selective profiles and subtraction fidelity across a broad B₀/B₁⁺ range, (2) subtraction fidelity in phantoms, and (3) image quality, artifact presence, and gray‐matter perfusion heterogeneity (as measured by the spatial coefficient of variation) in healthy human subjects.Results: Adiabatic refocusing significantly improves FT‐VSI robustness to B₀/B₁⁺ inhomogeneity for a single label‐control subtraction. Subtraction fidelity is dramatically improved in both simulation and phantoms compared with composite refocusing without DPC, and is similar compared with DPC methods. In humans, marked artifacts seen with the non‐DPC composite refocusing approach are eliminated, corroborated by significantly reduced gray‐matter heterogeneity (via lower spatial coefficient of variation values).Conclusion: A novel VSASL labeling train using adiabatic refocusing pulses for VSI was found to reduce artifacts related to B₀/B₁⁺ inhomogeneity, thereby providing an alternative to DPC and its associated limitations, which include increased vulnerability to physiological noise and motion, reduced functional MRI applicability, and suboptimal data censoring.

Purpose: To develop a generalized signal model for dual-module velocity-selective arterial spin labeling (dm-VSASL) that can integrate arbitrary saturation and inversion profiles. Theory and Methods: A recently developed mathematical framework for single-module VSASL is extended to address the increased complexity of dm-VSASL and to model the use of realistic velocity-selective profiles in the label-control and vascular crushing modules. Expressions for magnetization difference, arterial delivery functions, labeling efficiency, and cerebral blood flow (CBF) estimation error are presented. Sources of error are examined and timing requirements to minimize quantification errors are derived. Results: For ideal velocity-selective profiles, the predicted signals match those of prior work. With realistic profiles, a CBF-dependent estimation error can occur when velocity-selective inversion (VSI) is used for the labeling modules and velocity-selective saturation (VSS) is used for the vascular crushing module. The error reflects a mismatch between the leading and trailing edges of the delivery function for the second bolus and can be minimized by choosing a nominal labeling cutoff velocity that is lower than the nominal saturation cutoff velocity. In the presence of B ₀ and B ₁ inhomogeneities, the labeling efficiency of dual-module VSI is more attenuated than that of dual-module VSS. Conclusion: The proposed signal model will enable researchers to more accurately assess and compare the performance of realistic dm-VSASL implementations and improve the quantification of dm-VSASL CBF measures.

Head motion is a significant source of artifacts in resting-state fMRI (rsfMRI) studies and has been shown to affect resting-state functional connectivity (rsFC) measurements. In many rsfMRI studies, motion parameters estimated from volume registration are used to characterize head motion and to mitigate motion artifacts in rsfMRI data. While prior task-based fMRI studies have shown that task-evoked brain activations may induce temporally correlated bias in the motion estimates, resulting in artificial activations after registration, relatively little is known about neural-related bias in rsfMRI motion parameter. In this study, we demonstrate that neural-related bias exists in rsfMRI motion estimates and characterize the potential effects of the bias on rsFC estimates. Using a public multi-echo rsfMRI dataset, we use the differences between motion estimates from the first echo and second echo data as a measure of neural-induced bias. We show that the resting-state global activity of the brain, as characterized with the global signal (GS), induces bias in the motion estimates in the y- and z-translational axes. Furthermore, we demonstrate that the GS-related bias reflects superior-inferior and anterior-posterior asymmetries in the GS beta coefficient map. Finally, we demonstrate that regression with biased motion estimates can negatively bias rsFC estimates and also reduce rsFC differences between young and old subjects.

Introduction: Despite evidence suggesting deleterious effects of cannabis and nicotine tobacco product (NTP) use on white matter integrity, there have been limited studies examining white matter integrity among users of both cannabis and nicotine. Further, updated white matter methodology provides opportunities to investigate use patterns on neurite orientation dispersion and density (NODDI) indices and subtle tissue changes related to the intra- and extra-neurite compartment. We aimed to investigate how cannabis and NTP use among adolescents and young adults interacts to impact the white matter integrity microstructure. Materials and Methods: A total of 221 participants between the ages of 16 and 22 completed the Customary Drinking and Drug Use Record (CDDR) to measure substance use, and underwent a magnetic resonance imaging (MRI) session. Participants were divided into NTP-control and NTP groupings and cannabis-control and cannabis groupings (≥26 NTP/cannabis uses in past 6 months). Tract-Based Spatial Statistics (TBSS) and two-way between-subjects ANOVA investigated the effects of NTP use group, cannabis use group, and their interaction on fractional anisotropy (FA) and NODDI indices while controlling for age and biological sex. Results: NTP use was associated with decreased FA values and increased orientation dispersion in the left anterior capsule. There were no significant effects of cannabis use or the interaction of NTP and cannabis use on white matter outcomes. Discussion: NTP use was associated with altered white matter integrity in an adolescent and young adult sample. Findings suggest that NTP-associated alterations may be linked to altered fiber tract geometry and dispersed neurite structures versus myelination, as well as differential effects of NTP and cannabis use on white matter structure. Future work is needed to investigate how altered white matter is related to downstream behavioral effects from NTP use.

PurposeTo present a theoretical framework that rigorously defines and analyzes key concepts and quantities for velocity selective arterial spin labeling (VSASL).Theory and MethodsAn expression for the VSASL arterial delivery function is derived based on (1) labeling and saturation profiles as a function of velocity and (2) physiologically plausible approximations of changes in acceleration and velocity across the vascular system. The dependence of labeling efficiency on the amplitude and effective bolus width of the arterial delivery function is defined. Factors that affect the effective bolus width are examined, and timing requirements to minimize quantitation errors are derived.ResultsThe model predicts that a flow-dependent negative bias in the effective bolus width can occur when velocity selective inversion (VSI) is used for the labeling module and velocity selective saturation (VSS) is used for the vascular crushing module. The bias can be minimized by choosing a nominal labeling cutoff velocity that is lower than the nominal cutoff velocity of the vascular crushing module.ConclusionThe elements of the model are specified in a general fashion such that future advances can be readily integrated. The model can facilitate further efforts to understand and characterize the performance of VSASL and provide critical theoretical insights that can be used to design future experiments and develop novel VSASL approaches.

Head motion is a significant source of artifacts in resting-state fMRI (rsfMRI) studies and has been shown to affect resting-state functional connectivity (rsFC) measurements. In many rsfMRI studies, motion parameters estimated from volume registration are used to characterize head motion and to mitigate motion artifacts in rsfMRI data. While prior task-based fMRI studies have shown that task-evoked brain activations may induce temporally correlated bias in the motion estimates, resulting in artificial activations after registration, relatively little is known about neural-related bias in rsfMRI motion parameters. In this study, we demonstrate that neural-related bias exists in rsfMRI motion estimates and characterize the potential effects of the bias on rsFC estimates. Using a public multi-echo rsfMRI dataset, we use the differences between motion estimates from the first echo and second echo data as a measure of neural-induced bias. We show that the resting-state global activity of the brain, as characterized with the global signal (GS), induces bias in the motion estimates in the y- and z-translational axes. Furthermore, we demonstrate that the GS-related bias reflects superior-inferior and anterior-posterior asymmetries in the GS beta coefficient map. Finally, we demonstrate that regression with biased motion estimates can negatively bias rsFC estimates and reduce rsFC differences between young and old subjects.