Faculty Bibliography

PURPOSE/OBJECTIVE:Abnormalities in cerebral veins are a common finding in many neurological diseases, yet there is a scarcity of MRI techniques to assess venous hemodynamic function. The present study aims to develop a noncontrast technique to measure a novel blood flow circulatory measure, venous transit time (VTT), which denotes the time it takes for water to travel from capillary to major veins. METHODS:relaxation time. The validity of the measured VTT was tested by studying the VTT along the anatomically known flow trajectory of venous vessels as well as using a physiological vasoconstrictive challenge of caffeine ingestion. Finally, we compared the VTT measured with VICTR MRI to a bolus-tracking method using gadolinium-based contrast agent. RESULTS:VTT was measured to be 3116.3 ± 326.0 ms in the posterior superior sagittal sinus (SSS), which was significantly longer than 2865.0 ± 390.8 ms at the anterior superior sagittal sinus (p = 0.004). The test-retest assessment showed an interclass correlation coefficient of 0.964. VTT was significantly increased by 513.8 ± 239.3 ms after caffeine ingestion (p < 0.001). VTT measured with VICTR MRI revealed a strong correlation (R = 0.84, p = 0.002) with that measured with the contrast-based approach. VTT was found inversely correlated to cerebral blood flow and venous oxygenation across individuals. CONCLUSION/CONCLUSIONS:A noncontrast MRI technique, VICTR MRI, was developed to measure the VTT of the brain.

Studying the relationship between cerebral oxygen utilization and cognitive impairment is essential to understanding neuronal functional changes in the disease progression of multiple sclerosis (MS). This study explores the potential of using venous susceptibility in internal cerebral veins (ICVs) as an imaging biomarker for cognitive impairment in relapsing-remitting MS (RRMS) patients. Quantitative susceptibility mapping derived from fully flow-compensated MRI phase data was employed to directly measure venous blood oxygen saturation levels (S_vO₂) in the ICVs. Results revealed a significant reduction in the susceptibility of ICVs (212.4 ± 30.8 ppb vs 239.4 ± 25.9 ppb) and a significant increase of S_vO₂ (74.5 ± 1.89% vs 72.4 ± 2.23%) in patients with RRMS compared with age- and sex-matched healthy controls. Both the susceptibility of ICVs (r = 0.508, p = 0.031) and the S_vO₂ (r = -0.498, p = 0.036) exhibited a moderate correlation with cognitive decline in these patients assessed by the Paced Auditory Serial Addition Test, while no significant correlation was observed with clinical disability measured by the Expanded Disability Status Scale. The findings suggest that venous susceptibility in ICVs has the potential to serve as a specific indicator of oxygen metabolism and cognitive function in RRMS. .

Mapping the small venous vasculature of the hippocampus in vivo is crucial for understanding how functional changes of hippocampus evolve with age. Oxygen utilization in the hippocampus could serve as a sensitive biomarker for early degenerative changes, surpassing hippocampal tissue atrophy as the main source of information regarding tissue degeneration. Using an ultrahigh field (7T) susceptibility-weighted imaging (SWI) sequence, it is possible to capture oxygen-level dependent contrast of submillimeter-sized vessels. Moreover, the quantitative susceptibility mapping (QSM) results derived from SWI data allow for the simultaneous estimation of venous oxygenation levels, thereby enhancing the understanding of hippocampal function. In this study, we proposed two potential imaging markers in a cohort of 19 healthy volunteers aged between 20 and 74 years. These markers were: 1) hippocampal venous density on SWI images and 2) venous susceptibility (Δχ_vein) in the hippocampus-associated draining veins (the inferior ventricular veins (IVV) and the basal veins of Rosenthal (BVR) using QSM images). They were chosen specifically to help characterize the oxygen utilization of the human hippocampus and medial temporal lobe (MTL). As part of the analysis, we demonstrated the feasibility of measuring hippocampal venous density and Δχ_vein in the IVV and BVR at 7T with high spatial resolution (0.25 × 0.25 × 1 mm³). Our results demonstrated the in vivo reconstruction of the hippocampal venous system, providing initial evidence regarding the presence of the venous arch structure within the hippocampus. Furthermore, we evaluated the age effect of the two quantitative estimates and observed a significant increase in Δχ_vein for the IVV with age (p = 0.006, r² = 0.369). This may suggest the potential application of Δχ_vein in IVV as a marker for assessing changes in atrophy-related hippocampal oxygen utilization in normal aging and neurodegenerative diseases such as AD and dementia.

PURPOSE/OBJECTIVE:has important applications in cerebrovascular diseases. At present, these sequences are performed separately. This study aims to develop a novel MRI technique to simultaneously estimate these parameters. METHODS:* mapping). Test-retest repeatability and initial clinical application in two patients with stroke were evaluated. RESULTS:estimation was highly reliable, with voxelwise coefficient of variation (CoV) <5%. The CoV for arterial transit time and cerebral blood flow was 16% ± 3% and 25% ± 9%, respectively. The results from the two patients with stroke demonstrated that parametric maps derived from the proposed method can detect both ischemic and hemorrhagic stroke. CONCLUSION/CONCLUSIONS:The proposed method is a promising technique for multi-parametric mapping and has potential use in patients with stroke.

The intricate neurofluid dynamics and balance is essential in preserving the structural and functional integrity of the brain. Key among these forces are: hemodynamics, such as heartbeat-driven arterial and venous blood flow, and hydrodynamics, such as cerebrospinal fluid (CSF) circulation. The delicate interplay between these dynamics is crucial for maintaining optimal homeostasis within the brain. Currently, the widely accepted framework for understanding brain functions is the Monro-Kellie's doctrine, which posits a constant sum of intracranial CSF, blood flow and brain tissue volumes. However, in recent decades, there has been a growing interest in exploring the dynamic interplay between these elements and the impact of external factors, such as daily changes in body position. CSF circulation in particular plays a crucial role in the context of neurodegeneration and dementia, since its dysfunction has been associated with impaired clearance mechanisms and accumulation of toxic substances. Despite the implementation of various invasive and non-invasive imaging techniques to investigate the intracranial hemodynamic or hydrodynamic properties, a comprehensive understanding of how all these elements interact and are influenced by body position remains wanted. Establishing a comprehensive overview of this topic is therefore crucial and could pave the way for alternative care approaches. In this review, we aim to summarize the existing understanding of intracranial hemodynamic and hydrodynamic properties, fundamental for brain homeostasis, along with factors known to influence their equilibrium. Special attention will be devoted to elucidating the effects of body position shifts, given their significance and remaining ambiguities. Furthermore, we will explore recent advancements in imaging techniques utilized for real time and non-invasive measurements of dynamic body fluid properties in-vivo.

INTRODUCTION/UNASSIGNED:Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique with simultaneous (during stimulation) and cumulative effects (after repeated sessions) on blood flow and neuronal metabolism. These effects remain mostly unclear especially in multiple sclerosis (MS). This work aims to elucidate brain metabolic and hemodynamic underpinnings of tDCS and its potential therapeutic impact in MS patients using quantitative tDCS-MRI. METHODS/UNASSIGNED:) were obtained at pre-tDCS, during-tDCS and post-tDCS. RESULTS/UNASSIGNED:and CBF in pre-tDCS follow up, reaching the magnitudes measured at baseline during-tDCS. DISCUSSION/UNASSIGNED:TDCS induces an acute surge in metabolic activity persisting immediately after the stimulation is removed. Moreover, treatment composed of repeated tDCS-aCT paired sessions contributes to establishing long-lasting increases in neuronal activity.

PURPOSE:In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. MATERIALS AND METHOD:-10min methods in estimating the PS values. RESULTS:-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. CONCLUSIONS:We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.

PURPOSE/OBJECTIVE:Filter exchange imaging (FEXI) and diffusion time (t)-dependent diffusion kurtosis imaging (DKI(t)) are both sensitive to water exchange between tissue compartments. The restrictive effects of tissue microstructure, however, introduce bias to the exchange rate obtained by these two methods, as their interpretation conventionally rely on the Kärger model of barrier limited exchange between Gaussian compartments. Here, we investigated whether FEXI and DKI(t) can provide comparable exchange rates in ex vivo mouse brains. THEORY AND METHODS/METHODS:FEXI and DKI(t) data were acquired from ex vivo mouse brains on a preclinical MRI system. Phase cycling and negative slice prewinder gradients were used to minimize the interferences from imaging gradients. RESULTS:) from DKI(t) along the radial direction. In comparison, discrepancies between FEXI and DKI(t) were found in the cortex due to low filter efficiency and confounding effects from tissue microstructure. CONCLUSION/CONCLUSIONS:The results suggest that FEXI and DKI(t) are sensitive to the same exchange processes in white matter when separated from restrictive effects of microstructure. The complex microstructure in gray matter, with potential exchange among multiple compartments and confounding effects of microstructure, still pose a challenge for FEXI and DKI(t).

The purpose of this study was to develop and test a 3D multi-parameter MR fingerprinting (MRF) method for brain imaging applications. The subject cohort included 5 healthy volunteers, repeatability tests done on 2 healthy volunteers and tested on two multiple sclerosis (MS) patients. A 3D-MRF imaging technique capable of quantifying T ₁ , T ₂ and T _1ρ was used. The imaging sequence was tested in standardized phantoms and 3D-MRF brain imaging with multiple shots (1, 2 and 4) in healthy human volunteers and MS patients. Quantitative parametric maps for T ₁ , T ₂ , T _1ρ , were generated. Mean gray matter (GM) and white matter (WM) ROIs were compared for each mapping technique, Bland-Altman plots and intra-class correlation coefficient (ICC) were used to assess repeatability and Student T-tests were used to compare results in MS patients. Standardized phantom studies demonstrated excellent agreement with reference T ₁ /T _2/ T _1ρ mapping techniques. This study demonstrates that the 3D-MRF technique is able to simultaneously quantify T ₁ , T ₂ and T _1ρ for tissue property characterization in a clinically feasible scan time. This multi-parametric approach offers increased potential to detect and differentiate brain lesions and to better test imaging biomarker hypotheses for several neurological diseases, including MS.

Background: Increasing evidence suggests the subtle changes of Blood-Brain Barrier (BBB) permeability in normal aging and in Alzheimer"™s disease using Dynamic Contrast-Enhanced MRI (DCE-MRI). However, measuring this subtle change poses great challenge for accurate measurement, resulting in inconsistent results among previous studies. Two major challenges are long scan times, as suggested by previous studies and selection of the arterial input function (AIF). In this study, we aim to estimate the capillary level input function (CIF) using a deep learning network to overcome these two challenges. Methods: Healthy volunteers (n= 8, ages: 21-76) were recruited for DCE-MRI scan for 28min. Golden-angle RAdial Sampling Parallel (GRASP) sequence was used to obtain the dynamic images at ∼5s/frame. Individual AIF was sampled from the superior sagittal sinus of the brain (Fig.1a). FSL was used to segment the gray and white matters (Fig.1b). Each voxel was fitted using the graphical Patlak model (Fig.2a) to assess the vascular permeability-surface area product (PS) for both 28-min data and 10-min truncated data. We used a 3x3 kernel sliding through the images (Fig.3) and feed each voxel"™s dynamic as the input to our vision-transformer. Training data were generated using individual AIFs with a mathematical model, consisting of two Gaussian and one exponential function, and used to simulate dynamic patches using the Extended Patlak model (Fig.2b). Result: When the 10-min data are used, the conventional approach with AIF results in overestimation of PS when the scan-time is reduced, while the network-predicted CIF allows more accurate estimation, with refence to the results using the 28-min data, as illustrated by an example in Figure 4. Figure 5 shows the regional permeability differences between young and old subjects, where the conventional approach with AIF does not show the difference, while the approach with CIF shows subtle increases in PS with aging. Conclusion: Our proposed CIF-based approach provides an appropriate input-function for DCE analysis, allowing assessment of subtle permeability changes in the BBB.