Comparison of In Vivo and Ex Vivo Diffusion Tensor Imaging in Rhesus Macaques at Short and Long Diffusion Times

Diffusion tensor imaging (DTI) is widely used to non-invasively study neural tissue micro-structure. While DTI tractography of large nerve fibers is well accepted, visualization of smaller fibers and resolution of branching fibers is challenging. Sensitivity of DTI to diffusion anisotropy can be further enhanced using long diffusion time that can provide a more accurate representation of the tissue micro-structure. We previously reported that ex vivo fixed brain DTI at long tdiff (192 ms) showed improved sensitivity to fiber tracking compared to short tdiff (48 ms) in 4% formalin-fixed non-human primate (NHP) brains. This study further tested the hypothesis that DTI at longer diffusion time improves DTI fiber tracking in the in vivo NHP brains on a clinical 3 Tesla MRI scanner. Compared to fixed brains, the in vivo ADC was larger by a factor of 5. Also, the white-matter FA was 28% higher in the in vivo study as compared to our ex vivo experiments. Compared to short tdiff, long tdiff increased white-matter FA by 6.0±0.5%, diffusion was more anisotropic, tensor orientations along major fiber tracts were more coherent, and tracked fibers were about 10.1±2.9% longer in the corpus callosum and 7.3±2.8% longer along the cortico-spinal tract. The overall improvements in tractography were, however, less pronounced in the in vivo brain than in fixed brains. Nonetheless, these in vivo findings reinforce that DTI tractography at long diffusion time improves tracking of smaller fibers in regions of low fractional anisotropy

Parallel magnetic resonance imaging: characterization and comparison

Magnetic Resonance Imaging (MRI) is now increasingly being used for fast imaging applications such as real-time cardiac imaging, functional brain imaging, contrast enhanced MRI, etc. Imaging speed in MRI is mainly limited by different imaging parameters selected by the pulse sequences, the subject being imaged and the RF hardware system in operation. New pulse sequences have been developed in order to decrease the imaging time by a faster k-space scan. However, they may not be fast enough to facilitate imaging in real time. Parallel MRI (pMRI), a technique initially used for improving image SNR, has emerged as an effective complementary approach to reduce image scan-time. Five methods, viz., SENSE [Pruesmann, 1999], PILS [Griswold, 2000], SMASH [Sodickson, 1997], GRAPPA [Griswold, 2002] and SPACE RIP [Kyriakos, 2000]; developed in the past decade have been studied, simulated and compared in this research. Because of the dependence of the parallel imaging methods on numerous factors such as receiver coil configuration, k-space subsampling factor, k-space coverage in the imaging environment, there is a critical need to find the method giving the best results under certain imaging conditions. The tools developed in this research help the selection of the optimal method for parallel imaging depending on a particular imaging environment and scanning parameters. Simulations on real MR phased-array data show that SENSE and GRAPPA provide better image reconstructions when compared to the remaining techniques

Clinical Feasibility of Noninvasive Visualization of Lymphatic Flow with Principles of Spin Labeling MR Imaging: Implications for Lymphedema Assessment

Purpose To extend a commonly used noninvasive arterial spin labeling magnetic resonance (MR) imaging method for measuring blood flow to evaluate lymphatic flow. Materials and Methods All volunteers (n = 12) provided informed consent in accordance with institutional review board and HIPAA regulations. Quantitative relaxation time (T1 and T2) measurements were made in extracted human lymphatic fluid at 3.0 T. Guided by these parameters, an arterial spin labeling MR imaging approach was adapted to measure lymphatic flow (flow-alternating inversion-recovery lymphatic water labeling, 3 × 3 × 5 mm) in healthy subjects (n = 6; mean age, 30 years ± 1 [standard deviation]; recruitment duration, 2 months). Lymphatic flow velocity was quantified by performing spin labeling measurements as a function of postlabeling delay time and by measuring time to peak signal intensity in axillary lymph nodes. Clinical feasibility was evaluated in patients with stage II lymphedema (three women; age range, 43–64 years) and in control subjects with unilateral cuff-induced lymphatic stenosis (one woman, two men; age range, 31–35 years). Results Mean T1 and T2 relaxation times of lymphatic fluid at 3.0 T were 3100 msec ± 160 (range, 2930–3210 msec; median, 3200 msec) and 610 msec ± 12 (range, 598–618 msec; median, 610 msec), respectively. Healthy lymphatic flow (afferent vessel to axillary node) velocity was 0.61 cm/min ± 0.13 (n = 6). A reduction (P \u3c .005) in lymphatic flow velocity in the affected arms of patients and the affected arms of healthy subjects with manipulated cuff-induced flow reduction was observed. The ratio of unaffected to affected axilla lymphatic velocity (1.24 ± 0.18) was significantly (P \u3c .005) higher than the left-to-right ratio in healthy subjects (0.91 ± 0.18). Conclusion This work provides a foundation for clinical investigations whereby lymphedema etiogenesis and therapies may be interrogated without exogenous agents and with clinically available imaging equipment

Bifurcated topological optimization for IVIM

In this work, we shed light on the issue of estimating Intravoxel Incoherent Motion (IVIM) for diffusion and perfusion estimation by characterizing the objective function using simplicial homology tools. We provide a robust solution via topological optimization of this model so that the estimates are more reliable and accurate. Estimating the tissue microstructure from diffusion MRI is in itself an ill-posed and a non-linear inverse problem. Using variable projection functional (VarPro) to fit the standard bi-exponential IVIM model we perform the optimization using simplicial homology based global optimization to better understand the topology of objective function surface. We theoretically show how the proposed methodology can recover the model parameters more accurately and consistently by casting it in a reduced subspace given by VarPro. Additionally we demonstrate that the IVIM model parameters cannot be accurately reconstructed using conventional numerical optimization methods due to the presence of infinite solutions in subspaces. The proposed method helps uncover multiple global minima by analyzing the local geometry of the model enabling the generation of reliable estimates of model parameters.The National Institute of Biomedical Imaging And Bioengineering (NIBIB) of the National Institutes of Health (NIH); University of Washington’s Royalty Research Fund; NIH grants; the German Research Foundation (DFG) and a grant from the Alfred P. Sloan Foundation and the Gordon & Betty Moore Foundation to the University of Washington eScience Institute Data Science Environment.http://www.frontiersin.org/Neuroscienceam2022Chemical Engineerin

Current Understanding of the Anatomy, Physiology, and Magnetic Resonance Imaging of Neurofluids: Update From the 2022 "ISMRM Imaging Neurofluids Study group" Workshop in Rome

Neurofluids is a term introduced to define all fluids in the brain and spine such as blood, cerebrospinal fluid, and interstitial fluid. Neuroscientists in the past millennium have steadily identified the several different fluid environments in the brain and spine that interact in a synchronized harmonious manner to assure a healthy microenvironment required for optimal neuroglial function. Neuroanatomists and biochemists have provided an incredible wealth of evidence revealing the anatomy of perivascular spaces, meninges and glia and their role in drainage of neuronal waste products. Human studies have been limited due to the restricted availability of noninvasive imaging modalities that can provide a high spatiotemporal depiction of the brain neurofluids. Therefore, animal studies have been key in advancing our knowledge of the temporal and spatial dynamics of fluids, for example, by injecting tracers with different molecular weights. Such studies have sparked interest to identify possible disruptions to neurofluids dynamics in human diseases such as small vessel disease, cerebral amyloid angiopathy, and dementia. However, key differences between rodent and human physiology should be considered when extrapolating these findings to understand the human brain. An increasing armamentarium of noninvasive MRI techniques is being built to identify markers of altered drainage pathways. During the three-day workshop organized by the International Society of Magnetic Resonance in Medicine that was held in Rome in September 2022, several of these concepts were discussed by a distinguished international faculty to lay the basis of what is known and where we still lack evidence. We envision that in the next decade, MRI will allow imaging of the physiology of neurofluid dynamics and drainage pathways in the human brain to identify true pathological processes underlying disease and to discover new avenues for early diagnoses and treatments including drug delivery. Evidence level: 1. Technical Efficacy: Stage 3

Current Understanding of the Anatomy, Physiology, and Magnetic Resonance Imaging of Neurofluids: Update From the 2022 “<scp>ISMRM</scp> Imaging Neurofluids Study group” Workshop in Rome

Neurofluids is a term introduced to define all fluids in the brain and spine such as blood, cerebrospinal fluid, and interstitial fluid. Neuroscientists in the past millennium have steadily identified the several different fluid environments in the brain and spine that interact in a synchronized harmonious manner to assure a healthy microenvironment required for optimal neuroglial function. Neuroanatomists and biochemists have provided an incredible wealth of evidence revealing the anatomy of perivascular spaces, meninges and glia and their role in drainage of neuronal waste products. Human studies have been limited due to the restricted availability of noninvasive imaging modalities that can provide a high spatiotemporal depiction of the brain neurofluids. Therefore, animal studies have been key in advancing our knowledge of the temporal and spatial dynamics of fluids, for example, by injecting tracers with different molecular weights. Such studies have sparked interest to identify possible disruptions to neurofluids dynamics in human diseases such as small vessel disease, cerebral amyloid angiopathy, and dementia. However, key differences between rodent and human physiology should be considered when extrapolating these findings to understand the human brain. An increasing armamentarium of noninvasive MRI techniques is being built to identify markers of altered drainage pathways. During the three‐day workshop organized by the International Society of Magnetic Resonance in Medicine that was held in Rome in September 2022, several of these concepts were discussed by a distinguished international faculty to lay the basis of what is known and where we still lack evidence. We envision that in the next decade, MRI will allow imaging of the physiology of neurofluid dynamics and drainage pathways in the human brain to identify true pathological processes underlying disease and to discover new avenues for early diagnoses and treatments including drug delivery.Evidence level: 1Technical Efficacy: Stage

Frequency drift in MR spectroscopy at 3T

Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B-0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites.Method: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC).Results: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p &lt; 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI.Discussion: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.</p

Diffusion tensor imaging at long diffusion time

Diffusion Tensor Imaging (DTI) is a well-established magnetic resonance technique that can non-invasively interpret tissue geometry and track neural pathways by sampling the diffusion of water molecules in the brain tissue. However, it is currently limited to tracking large nerve fiber bundles and fails to faithfully resolve thinner fibers. Conventional DTI studies use a diffusion time, t[subscript diff] of 30 ms - 55 ms for diffusion measurements. This work proposes the use of DTI at long t[subscript diff] to enhance the sensitivity of the method towards regions of low diffusion anisotropy and improve tracking of smaller fibers. The Stimulated Echo Acquisition Mode (STEAM) sequence was modified to allow DTI measurements at long t[subscript diff] (approximately 200 ms), while avoiding T2 signal loss. For comparison, DTI data was acquired using STEAM at the shorter value of t[subscript diff] and with the standard Double Spin Echo sequence with matched signal-to-noise ratio. This approach was tested on phantoms and fixed monkey brains and then translated to in vivo studies in rhesus macaques. Qualitative and quantitative comparison of the techniques was based on fractional anisotropy, diffusivity, three-phase plots and directional entropy. Tensor-field maps and probabilistic connectivity fronts were evaluated for all three acquisitions. Comparison of the tracked nerve pathways showed that fibers obtained at long t[subscript diff] were much longer. Further, the optic tract was tracked in ex vivo fixed rhesus brains for cross validation. The optic tract, traced at long t[subscript diff], conformed to the well documented anatomical description, thus confirming the accuracy of tract tracing at long t[subscript diff]. The benefits of DTI at long t[subscript diff] indeed help to realize the potential of tensor based tractography towards studying neural development and diagnosing neuro-pathologies, albeit the improvement is more significant ex vivo than in vivo.Ph.D.Committee Chair: Hu, Xiaoping; Committee Member: Brummer, Marijn; Committee Member: Duong, Tim; Committee Member: Keilholz, Shella; Committee Member: Schumacher, Eri

Table_1_Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls.DOCX

PurposeConventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer’s disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer’s disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls.MethodsWe used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies–Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05.ResultsWe identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (pConclusionWe show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.</p