Oxygen saturation-dependent effects on blood transverse relaxation at low fields.

OBJECTIVE: Blood oxygenation can be measured using magnetic resonance using the paramagnetic effect of deoxy-haemoglobin, which decreases the [Formula: see text] relaxation time of blood. This [Formula: see text] contrast has been well characterised at the [Formula: see text] fields used in MRI (1.5 T and above). However, few studies have characterised this effect at lower magnetic fields. Here, the feasibility of blood oximetry at low field based on [Formula: see text] changes that are within a physiological relevant range is explored. This study could be used for specifying requirements for construction of a monitoring device based on low field permanent magnet systems. METHODS: A continuous flow circuit was used to control parameters such as oxygen saturation and temperature in a sample of blood. It flowed through a variable field magnet, where CPMG experiments were performed to measure its [Formula: see text]. In addition, the oxygen saturation was monitored by an optical sensor for comparison with the [Formula: see text] changes. RESULTS: These results show that at low [Formula: see text] fields, the change in blood [Formula: see text] due to oxygenation is small, but still detectable. The data measured at low fields are also in agreement with theoretical models for the oxy-deoxy [Formula: see text] effect. CONCLUSION: [Formula: see text] changes in blood due to oxygenation were observed at fields as low as 0.1 T. These results suggest that low field NMR relaxometry devices around 0.3 T could be designed to detect changes in blood oxygenation.

Two-dimensional subband Steiglitz-McBride algorithm for automatic analysis of two-dimensional nuclear magnetic resonance data.

Rapid, accurate, and automatic quantitation of two-dimensional nuclear magnetic resonance(2D-NMR) data is a challenging problem. Recently, a Bayesian information criterion based subband Steiglitz-McBride algorithm has been shown to exhibit superior performance on all three fronts when applied to the quantitation of one-dimensional NMR free induction decay data. In this paper, we demonstrate that the 2D Steiglitz-McBride algorithm, in conjunction with 2D subband decomposition and the 2D Bayesian information criterion, also achieves excellent results for 2D-NMR data in terms of speed, accuracy, and automation-especially when compared in these respects to the previously published analysis techniques for 2D-NMR data.

Is the Cushing mechanism a dynamic blood pressure-stabilizing system? Insights from Granger causality analysis of spontaneous blood pressure and cerebral blood flow.

Blood pressure (BP) regulation is widely recognized as being integral to the control of end-organ perfusion, but it remains unclear whether end-organ perfusion also plays a role in driving changes in BP. A randomized and placebo-controlled study design was followed to examine feedback relationships between very-low-frequency fluctuations in BP and cerebral blood flow (CBF) in humans under placebo treatment and α1-adrenergic blockade. To determine the causal relations among hemodynamic variables, BP, middle cerebral artery blood velocity (MCAv), and end-tidal CO 2 time-series were decimated, low-pass filtered (<0.07 Hz), fitted to vector autoregressive models, and tested for Granger causality in the time domain. Results showed that 1) at baseline, changes in BP and MCAv often interact in a closed-loop; and 2) α1-adrenergic blockade results in the dominant causal direction from BP to MCAv. These results suggest that, between subjects, cerebral pressure-flow interactions at time scales < 0.07 Hz are frequently bidirectional, and that in the presence of an intact autonomic nervous system BP may be regulated by reflex pathways sensitive to changes in CBF.

A subband Steiglitz-McBride algorithm for automatic analysis of FID data.

Fast, accurate, and automatic extraction of parameters of nuclear magnetic resonance free induction decay (FID) signal for chemical spectroscopy is a challenging problem. Recently, the Steiglitz-McBride algorithm has been shown to exhibit superior performance in terms of speed, accuracy, and automation when applied to the extraction of T2 relaxation parameters for myelin water imaging of brain. Applying it to FID data reveals that it falls short of the second objective, the accuracy. Especially, it struggles with the issue of missed spectral peaks when applied to chemical samples with relatively dense frequency spectra. To overcome this issue, a preprocessing stage of subband decomposition is proposed before the application of Steiglitz-McBride algorithm to the FID signal. It is demonstrated that by doing so, a considerable improvement in accuracy is achieved. But this is not gained at the cost of the first objective, the speed. An adaptive subband decomposition is employed in conjunction with the Bayesian information criteria to carry out an efficient decomposition according to spectral content of the signal under investigation. Furthermore, adaptive subband decomposition and the Bayesian information criteria also serve to make the resulting algorithm independent of user input, which also fulfills the third objective, the automation. This makes the proposed algorithm favorable for fast, accurate, and automatic extraction of FID signal parameters.

Identification of human sympathetic neurovascular control using multivariate wavelet decomposition analysis.

The dynamic regulation of cerebral blood flow (CBF) is thought to involve myogenic and chemoreflex mechanisms, but the extent to which the sympathetic nervous system also plays a role remains debated. Here we sought to identify the role of human sympathetic neurovascular control by examining cerebral pressure-flow relations using linear transfer function analysis and multivariate wavelet decomposition analysis that explicitly accounts for the confounding effects of dynamic end-tidal Pco2 (PetCO2 ) fluctuations. In 18 healthy participants randomly assigned to the α1-adrenergic blockade group (n = 9; oral Prazosin, 0.05 mg/kg) or the placebo group (n = 9), we recorded blood pressure, middle cerebral blood flow velocity, and breath-to-breath PetCO2 Analyses showed that the placebo administration did not alter wavelet phase synchronization index (PSI) values, whereas sympathetic blockade increased PSI for frequency components ≤0.03 Hz. Additionally, three-way interaction effects were found for PSI change scores, indicating that the treatment response varied as a function of frequency and whether PSI values were PetCO2 corrected. In contrast, sympathetic blockade did not affect any linear transfer function parameters. These data show that very-low-frequency CBF dynamics have a composite origin involving, not only nonlinear and nonstationary interactions between BP and PetCO2 , but also frequency-dependent interplay with the sympathetic nervous system.

Finite element modelling of cochlear electrical coupling.

The operation of each hair cell within the cochlea generates a change in electrical potential at the frequency of the vibrating basilar membrane beneath the hair cell. This electrical potential influences the operation of the cochlea at nearby locations and can also be detected as the cochlear microphonic signal. The effect of such potentials has been proposed as a mechanism for the non-local operation of the cochlear amplifier, and the interaction of such potentials has been thought to be the cause of the broadness of cochlea microphonic tuning curves. The spatial extent of influence of these potentials is an important parameter for determining the significance of their effects. Calculations of this extent have typically been based on calculating the longitudinal resistance of each of the scalae from the scala cross sectional area, and the conductivity of the lymph. In this paper, the range of influence of the electrical potential is examined using an electrical finite element model. It is found that the range of influence of the hair cell potential is much shorter than the conventional calculation, but is consistent with recent measurements.

Efficient time-domain simulation of nonlinear, state-space, transmission-line models of the cochlea (L).

Nonlinear models of the cochlea are best implemented in the time domain, but their computational demands usually limit the duration of the simulations that can reasonably be performed. This letter presents a modified state space method and its application to an example nonlinear one-dimensional transmission-line cochlear model. The sparsity pattern of the individual matrices for this alternative formulation allows the use of significantly faster numerical algorithms. Combined with a more efficient implementation of the saturating nonlinearity, the computational speed of this modified state space method is more than 40 times faster than that of the original formulation.

Informed Adaptive Sensing.

For many inverse problems, the data on which the solution is based is acquired sequentially. We present an approach to the solution of such inverse problems where a sensor can be directed (or otherwise reconfigured on the fly) to acquire a particular measurement. An example problem is magnetic resonance image reconstruction. We use an estimate of mutual information derived from an empirical conditional distribution provided by a generative model to guide our measurement acquisition given measurements acquired so far. The conditionally generated data is a set of samples which are representative of the plausible solutions that satisfy the acquired measurements. We present experiments on toy and real world data sets. We focus on image data but we demonstrate that the method is applicable to a broader class of problems. We also show how a learned model such as a deep neural network can be leveraged to allow generalisation to unseen data. Our informed adaptive sensing method outperforms random sampling, variance based sampling, sparsity based methods, and compressed sensing.

Single-Sided Magnet System for Quantitative MR Relaxometry and Preclinical In-Vivo Monitoring.

OBJECTIVE: We have developed a single-sided magnet system that allows Magnetic Resonance relaxation and diffusion parameters to be measured. METHODS: A single-sided magnet system has been developed, using an array of permanent magnets. The magnet positions are optimised to produce a B0 magnetic field with a spot that is relatively homogenous and can project into a sample. NMR relaxometry experiments are used to measure quantitative parameters such as T2, T1 and apparent diffusion coefficient (ADC) on samples on the benchtop. To explore preclinical application, we test whether it can detect changes during acute global cerebral hypoxia in an ovine model. RESULTS: The magnet produces a 0.2 T field projected into the sample. Measurements of benchtop samples show that it can measure T1, T2 and ADC, producing trends and values that are in line with literature measurements. In-vivo studies show a decrease in T2 during cerebral hypoxia that recovers following normoxia. CONCLUSION: The single-sided MR system has the potential to allow non-invasive measurements of the brain. We also demonstrate that it can operate in a pre-clinical environment, allowing T2 to be monitored during brain tissue hypoxia. SIGNIFICANCE: MRI is a powerful technique for non-invasive diagnosis in the brain, but its application has been limited by the requirements for magnetic field strength and homogeneity that imaging methods have. The technology described in this study provides a portable alternative to acquiring clinically significant MR parameters without the need for traditional imaging equipment.

Improved data efficiency for NMR diffusion-relaxation processing.

Two dimensional diffusion and transverse (T2) NMR relaxation measurements are effective for a variety of research and industrial processes. Conversion of the measurements into a D-T2 map is performed using an inverse integral transformation. A difficulty with D-T2 estimation from data acquired without pulsed field gradients (using, for example, the inherent static field gradient of a single-sided magnet) is that the diffusion and relaxation kernels are coupled. One commonly used solution is to introduce a time offset to enable the kernels to be decoupled, but this has the undesirable results of causing some of the data, and a large proportion of the signal energy, to become unusable. This paper presents two methods of processing the data that do not require this wastage. Both methods are based on insights that arise from considering the linear operator that describes the forwards integral transformation. One method involves data compression, while the other method is an application (that we call FLINT) of the fast iterative soft thresholding algorithm. Both methods are able to use all of the available data. The paper demonstrates the improved accuracy that results from these methods on simulated data, as well as the improved discovery of important features on measured data.

Adaptive phase calibration of a microphone array for acoustic holography.

Previous work has indicated that a limitation on the performance of a circular microphone array for holographic sound field recording at low frequencies is phase mismatch between the microphones in the array. At low frequencies these variations become more significant than at mid-range and high frequencies because the high order phase mode responses at low frequencies are lower in amplitude. This paper demonstrates the feasibility of a "self-calibration" method. The basis of the calibration is to estimate the location of one or more wide-band sources using mid-range frequencies and to use this source location information to perform correction to the array at low frequencies. In its simplest form the calibration must be performed in an anechoic environment, since multipath effects at widely differing frequencies are uncorrelated. The approach is first demonstrated in such an environment using recordings from an array of high quality microphones. The technique is then extended to an adaptive calibration that can be used in an environment that is somewhat reverberant. The validity of the adaptive approach is demonstrated using recordings from an array of inexpensive microphones.

Detection of Impaired Sympathetic Cerebrovascular Control Using Functional Biomarkers Based on Principal Dynamic Mode Analysis.

This study sought to determine whether models of cerebrovascular function based on Laguerre-Volterra kernels that account for nonlinear cerebral blood flow (CBF) dynamics can detect the effects of functional cerebral sympathetic blockade. We retrospectively analyzed continuous beat-to-beat blood pressure, middle cerebral blood velocity, and partial-pressure of end-tidal CO2 (PETCO2) recordings from eighteen healthy individuals who were treated with either an oral dose of the α1-adrenergic receptor blocker Prazosin or a placebo treatment. The global principal dynamic modes (PDMs) were analyzed using Laguerre-Volterra kernels to examine the nonlinear system dynamics. Our principal findings were: (1) very low frequency (<0.03 Hz) linear components of first-order kernels for BP and PETCO2 are mutually coupled to CBF dynamics with the ability to separate individuals between control and blockade conditions, and (2) the gains of the nonlinear functions associated with low-pass and ≈0.03 Hz global PDMs for the BP are sensitive to sympathetic blockade. Collectively these results suggest that very low frequency global PDMs for BP may have potential utility as functional biomarkers of sympathetic neurovascular dysfunction which can occur in conditions like autonomic failure, stroke and traumatic brain injury.

Non-Linear Characterisation of Cerebral Pressure-Flow Dynamics in Humans.

Cerebral metabolism is critically dependent on the regulation of cerebral blood flow (CBF), so it would be expected that vascular mechanisms that play a critical role in CBF regulation would be tightly conserved across individuals. However, the relationships between blood pressure (BP) and cerebral blood velocity fluctuations exhibit inter-individual variations consistent with heterogeneity in the integrity of CBF regulating systems. Here we sought to determine the nature and consistency of dynamic cerebral autoregulation (dCA) during the application of oscillatory lower body negative pressure (OLBNP). In 18 volunteers we recorded BP and middle cerebral artery blood flow velocity (MCAv) and examined the relationships between BP and MCAv fluctuations during 0.03, 0.05 and 0.07Hz OLBNP. dCA was characterised using project pursuit regression (PPR) and locally weighted scatterplot smoother (LOWESS) plots. Additionally, we proposed a piecewise regression method to statistically determine the presence of a dCA curve, which was defined as the presence of a restricted autoregulatory plateau shouldered by pressure-passive regions. Results show that LOWESS has similar explanatory power to that of PPR. However, we observed heterogeneous patterns of dynamic BP-MCAv relations with few individuals demonstrating clear evidence of a dCA central plateau. Thus, although BP explains a significant proportion of variance, dCA does not manifest as any single characteristic BP-MCAv function.

Modelling cochlear mechanics.

The cochlea plays a crucial role in mammal hearing. The basic function of the cochlea is to map sounds of different frequencies onto corresponding characteristic positions on the basilar membrane (BM). Sounds enter the fluid-filled cochlea and cause deflection of the BM due to pressure differences between the cochlear fluid chambers. These deflections travel along the cochlea, increasing in amplitude, until a frequency-dependent characteristic position and then decay away rapidly. The hair cells can detect these deflections and encode them as neural signals. Modelling the mechanics of the cochlea is of help in interpreting experimental observations and also can provide predictions of the results of experiments that cannot currently be performed due to technical limitations. This paper focuses on reviewing the numerical modelling of the mechanical and electrical processes in the cochlea, which include fluid coupling, micromechanics, the cochlear amplifier, nonlinearity, and electrical coupling.

Modelling the generation of the cochlear microphonic.

The cochlear microphonic (CM) is one of the electrical signals generated by the human ear in response to sound stimulus. Difficulty in recording this signal and inadequate understanding of its origin have restricted its use for human auditory research. Modelling can help to improve our understanding of this signal. In this paper, an electromechanical model for the generation of the cochlear microphonic is proposed. The results of the model can also explain discrepancies between the basilar membrane and CM tuning curves.

A non-invasive Cochlear Microphonic measurement system.

The Cochlear Microphonic is one of the electrical potentials generated by the ear in response to audible stimuli. It is very difficult to measure the CM non-invasively because it has a very small magnitude (less than 1 µV). A high Common Mode Rejection Ratio (CMRR) and very large bandwidth (5 Hz-20 kHz) biomedical amplifier system is presented to measure the signal. The system also uses a driven right leg circuit to increase the CMRR.