Interface Focus最新文献

Intracranial pulse wave velocity (PWV) offers the potential to enhance neurovascular care when evaluating cerebrovascular disease. Using 4D flow MRI, we measured PWV in the intracranial vasculature stemming from the internal carotids and basilar arteries using three popular techniques: cross-correlation, waveform optimization and time-to-upstroke which have all been used intracranially, but never compared. Near-perfect agreement between cross-correlation and waveform optimization methods was observed, while the time-to-upstroke method estimated a significantly larger PWV and was more prone to non-physiological values in a cohort of 21 healthy individuals aged 48 ± 18 years. We then analysed our cohort PWV using an ensemble approach given the current lack of methodological consensus. This analysis identified two consistent findings. First, internal carotids measure significantly higher PWV than basilar vascular networks (3.64 ± 1.47 versus 2.53 ± 1.39 m s^-1). Second, in our cohort, intracranial PWV was age-independent. We hypothesize that age independence is a healthy physiological trait to minimize microvascular strain, protecting the integrity of the peripheral bed throughout ageing and cardiac pulsatile deformation. The cause for apparent age independence remains unknown. We also identified that previous work on intracranial PWV is likely biased towards the extracranial vasculature, which may explain the study differences in PWV magnitude and the age-dependent nature.

A phase-sensitive diffusion tensor magnetic resonance imaging (MRI) sequence is proposed with pulse timing optimization scheme to achieve velocity resolution of less than 20 μm s^-1 and an integrated image reconstruction and velocity map generation pipeline. The application of ultra-slow flow relevant to neurofluids is enabled by the use of a recently developed, ultra-high-performance brain MRI gradient system. By simultaneously reconstructing magnitude and phase data, both metrics that characterize diffusive fluid motion and coherent velocity maps are calculated non-invasively in human subjects, time-resolved over the entire cardiac cycle. The resulting acquisition and reconstruction of velocity maps in brain parenchyma, enabled by high-performance brain imaging systems, promises to be an important approach to investigating ultra-slow neurofluid flow and glymphatic circulation.

Understanding the pulsing dynamics of tissue and fluids in the intracranial environment is an evolving research theme aimed at gaining new insights into brain physiology and disease progression. This article provides an overview of related research in magnetic resonance imaging, ultrasound medical diagnostics and mathematical modelling of biological tissues and fluids. It highlights recent developments, illustrates current research goals and emphasizes the importance of collaboration between these fields.

Many children who have been treated for a posterior-fossa tumour experience neurocognitive problems after treatment with surgery, radiotherapy and/or chemotherapy, which significantly impact their quality of life. Knowledge about these underlying mechanisms is limited at this point. The displacement encoding with stimulated echoes (DENSE) sequence magnetic resonance imaging (MRI) at 7 T can be used to measure brain tissue pulsations, and provide information on both the blood vessels and microstructure simultaneously, which are potentially relevant parameters to assess these underlying mechanisms. A single-shot multi-slice DENSE sequence was used to obtain brain motion maps from which strain maps could be derived on a voxel-wise level. The robustness of this MRI sequence was studied using the root mean square displacement that was obtained during the registration in the analysis of the DENSE series. Although the paediatric participants exhibited noticeable head movement during the MR acquisition, good-quality strain maps were still obtained, displaying expected patterns similar to those seen in adults.

Computational models that accurately capture cerebrospinal fluid (CSF) dynamics are valuable tools to study neurological disorders and optimize clinical treatments. While CSF dynamics interrelate with deformations of the ventricular volumes, these deformations have been simplified and even discarded in computational models because of the lack of detailed measurements. Amplified magnetic resonance imaging (aMRI) enables visualization of these complex deformations, but this technique has not been used for predicting CSF dynamics. To assess the feasibility of using aMRI as an input for computational fluid dynamics (CFD) models of the CSF, we deduced the amplified deformations of the cerebral ventricles from an aMRI dataset and imposed these deformations in our CFD model. Then, we compared the resulting CSF flow rates with those measured in vivo. The aMRI deformations yielded CSF flow following a pulsatile pattern in line with the flow measurements. The CSF flow rates were, however, subject to noise and increased. As a result, scaling of the deformations with a factor 1/8 was necessary to match the measured flow rates. This is the first application of aMRI for modelling CSF flow, and we demonstrate that incorporating non-uniform deformations can contribute to more detailed predictions and advance our understanding of ventricular CSF dynamics.

Fluid movement in the interstitial space of the brain affects the clearance of waste products, which is an important factor in the pathophysiology of dementia. Estimating interstitial fluid (ISF) flow is critical to understanding these processes; yet, it has proven difficult to measure non-invasively. The pulsatile component of ISF flow may be particularly important for clearance, e.g. by facilitating fluid mixing. Directly measuring ISF flows is challenging due to the slow velocities and small volume fractions involved; however, pulsatile flows present a unique opportunity as their driving forces can be estimated from observations of pulsatile tissue motion. In this work, we present pressure gradient magnetic resonance imaging (pgMRI), which assimilates retrospectively gated pulsatile tissue deformations measured with a displacement encoding with stimulated echoes MRI sequence into a patient-specific poroelastic computational model by estimating the distribution of fluid sources. The new method is demonstrated to recover a spherical fluid source accurately from synthetic data with simulated noise of up to 20%, and to produce not previously reported in vivo brain fluid source images along with companion images of the three-dimensional stresses and pressure gradients which drive ISF movement. Repeated exams of four healthy volunteers demonstrated variability below 10% for pgMRI parameters in most cases.

We present a modelling framework for describing bulk fluid flow in brain tissue. Within this framework, using computational simulation, we estimate bulk flow velocities in the grey matter parenchyma due to static or slowly varying water potential gradients-hydrostatic pressure gradients and osmotic pressure gradients. Working with the situation that experimental evidence and some model parameter estimates, as we point out, are presently insufficient to estimate velocities precisely, we explore feasible parameter ranges resulting in a range of estimates. We consider the effect of realistic microvascular architecture (extracted from mouse cortical grey matter). Although the estimated velocities are small in magnitude (e.g. in comparison to blood flow velocities), the passive transport of solutes with the bulk fluid can be a relevant process when considering larger molecules transported over larger distances. We compare velocity magnitudes resulting from filtration and pulsations. Filtration can lead to continuous directed fluid flow in the parenchyma, while pulsation-driven flow is (at least partly) reversible. For the first time, we consider the effect of the vascular architecture on the velocity distribution in a tissue sample of ca 1 mm³ cortical grey matter tissue. We conclude that both filtration and pulsations are potentially potent drivers for fluid flow.

Stenosis of major intracranial arteries is a significant cause of stroke, with assessment of trans-stenotic pressure drops being a key marker of functional stenosis severity. Non-invasive methods for quantifying intracranial pressure changes are hence crucial; however, the narrow and tortuous cerebrovascular network poses challenges to traditional assessment methods such as transcranial Doppler. This study investigates the use of novel deep learning-enhanced super-resolution (SR) four-dimensional (4D) flow magnetic resonance imaging (MRI) in combination with a physics-informed virtual work-energy relative pressure technique to quantify pressure drops across stenotic intracranial arteries. Performance was validated in intracranial-mimicking in vitro experiments using pulsatile flow before being transferred into an in vivo cohort of patients with intracranial atherosclerotic disease. Conversion into sub-millimetre SR imaging significantly improved the accuracy of regional relative pressure estimations in the pulsing brain arteries, mitigating biases observed at >1 mm resolution imaging, and agreeing strongly with reference catheter-based invasive measurements across both moderate and severe stenoses. The in vivo analysis also revealed a significant increase in pressure drops when converting into sub-millimetre SR data, underlining the importance of apparent image resolution in a clinical setting. The results highlight the potential of SR 4D flow MRI for non-invasive quantification of cerebrovascular pressure changes in pulsing intracranial arteries across stenotic vessel segments.

Cerebrospinal fluid (CSF) dynamics are essential in the waste clearance of the brain. Disruptions in CSF flow are linked to various neurological conditions, highlighting the need for accurate measurement of its dynamics. Current methods typically capture high-speed CSF movements or focus on a single-frequency component, presenting challenges for comprehensive analysis. This study proposes a novel approach using displacement encoding with stimulated echoes (DENSE) MRI to assess the full spectrum of CSF motion within the brain. Through simulations, we evaluated the feasibility of disentangling distinct CSF motion components, including heartbeat- and respiration-driven flows, as well as a net velocity component due to continuous CSF turnover, and tested the performance of our method under incorrect assumptions about the underlying model of CSF motion. Results demonstrate that DENSE MRI can accurately separate these components, and reliably estimate a net velocity, even when periodic physiological motions vary over time. The method proved to be robust for including low-frequency components, incorrect assumptions on the nature of the net velocity component and missing CSF components in the model. This approach offers a comprehensive measurement technique for quantifying CSF dynamics, advancing our understanding of the relative role of various drivers of CSF dynamics in brain clearance.

This study investigates intracranial dynamics following the Monro-Kellie doctrine, depicting how brain pulsatility, cerebrospinal fluid (CSF) flow and cerebral blood flow (CBF) interact under resting and exercise conditions. Using quantitative amplified magnetic resonance imaging (q-aMRI) alongside traditional MRI flow metrics, we measured and analysed blood flow, CSF dynamics and brain displacement in a cohort of healthy adults both at rest and during low-intensity handgrip exercise. Exercise was found to reduce pulsatility in CBF while increasing CSF flow and eliminating CSF regurgitation, highlighting a shift towards more sustained forward flow patterns (from cranial to spinal compartments). Displacement analysis using q-aMRI revealed a consistent trend of reduced whole brain motion during exercise, though as the sample of data that met quality control was low (n = 5), this was not a significant result. There was an observable decrease in the motion of third and fourth ventricles, linking ventricular displacement to CSF flow alterations. These findings suggest that exercise may not only affect the rate and directionality of CSF flow but also modulate brain tissue motion, supporting cerebral homeostasis. This study offers insights into how the brain adapts dynamically under varying conditions, with implications for understanding intracranial pressure regulation in humans and diagnostic contexts.