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Multiplexed MRI methods for rapid estimation of global cerebral metabolic rate of oxygen consumption.

ABSTRACT: The global cerebral metabolic rate of oxygen (CMRO2), which reflects metabolic activity of the brain under various physiologic conditions, can be quantified using a method, referred to as 'OxFlow', which simultaneously measures hemoglobin oxygen saturation in a draining vein (Yv) and total cerebral blood flow (tCBF). Conventional OxFlow (Conv-OxFlow) entails four interleaves incorporated in a single pulse sequence - two for phase-contrast based measurement of tCBF in the supplying arteries of the neck, and two to measure the intra- to extravascular phase difference in the superior sagittal sinus to derive Yv [Jain et al., JCBFM 2010]. However, this approach limits achievable temporal resolution thus precluding capture of rapid changes of brain metabolic states such as the response to apneic stimuli. Here, we developed a time-efficient, multiplexed OxFlow method and evaluated its potential for measuring dynamic alterations in global CMRO2 during a breath-hold challenge. Two different implementations of multiplexed OxFlow were investigated: 1) simultaneous-echo-refocusing based OxFlow (SER-OxFlow) and 2) simultaneous-multi-slice imaging-based dual-band OxFlow (DB-OxFlow). The two sequences were implemented on 3T scanners (Siemens TIM Trio and Prisma) and their performance was evaluated in comparison to Conv-OxFlow in ten healthy subjects for baseline CMRO2 quantification. Comparison of measured parameters (Yv, tCBF, CMRO2) revealed no significant bias of SER-OxFlow and DB-OxFlow, with respect to the reference Conv-OxFlow while improving temporal resolution two-fold (12.5 versus 25s). Further acceleration shortened scan time to 8 and 6s for SER and DB-OxFlow, respectively, for time-resolved CMRO2 measurement. The two sequences were able of capturing smooth transitions of Yv, tCBF, and CMRO2 over the time course consisting of 30s of normal breathing, 30s of volitional apnea, and 90s of recovery. While both SER- and DB-OxFlow techniques provide significantly improved temporal resolution (by a factor of 3 - 4 relative to Conv-OxFlow), DB-OxFlow was found to be superior for the study of short physiologic stimuli.

SUBMITTER: Lee H

PROVIDER: S-EPMC5377447 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Multiplexed MRI methods for rapid estimation of global cerebral metabolic rate of oxygen consumption.

Lee Hyunyeol H Langham Michael C MC Rodriguez-Soto Ana E AE Wehrli Felix W FW

NeuroImage 20170206

The global cerebral metabolic rate of oxygen (CMRO<sub>2</sub>), which reflects metabolic activity of the brain under various physiologic conditions, can be quantified using a method, referred to as 'OxFlow', which simultaneously measures hemoglobin oxygen saturation in a draining vein (Y<sub>v</sub>) and total cerebral blood flow (tCBF). Conventional OxFlow (Conv-OxFlow) entails four interleaves incorporated in a single pulse sequence - two for phase-contrast based measurement of tCBF in the su ...[more]

PMID: 28179195

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Project description:The cerebral metabolic rate of oxygen (CMRO2) is the rate of oxygen consumption by the brain, and is thought to be a direct index of energy homeostasis and brain health. However, in vivo measurement of CMRO2 is challenging, in particular for the neonatal population, in whom conventional radiotracer methods are not applicable because of safety concerns. In this study, we propose a method to quantify global CMRO2 in neonates based on arteriovenous differences in oxygen content, and employ separate measurements of oxygenation and cerebral blood flow (CBF) parameters. Specifically, arterial and venous oxygenation levels were determined with pulse oximetry and the novel T2 relaxation under spin tagging (TRUST) MRI, respectively. Global CBF was measured with phase contrast (PC) flow velocity MRI. The proposed method was implemented on a standard 3-T MRI scanner without the need for any exogenous tracers, and the total scan duration was less than 5?min. We demonstrated the feasibility of this method in 12 healthy neonates within an age range of 35-42 gestational weeks. CMRO2 values were successfully obtained from 10 neonates. It was found that the average CMRO2 in this age range was 38.3?±?17.7?µmol/100?g/min and was positively correlated with age (p?=?0.007; slope, 5.2?µmol/100?g/min per week), although the highest CMRO2 value in this age range was still less than half of the adult level. Test-retest studies showed a coefficient of variation of 5.8?±?2.2% between repeated CMRO2 measurements. In addition, given the highly variable blood flow velocity within this age range, it is recommended that the TRUST labeling thickness and position should be determined on a subject-by-subject basis, and an automatic algorithm was developed for this purpose. Although this method provides a global CMRO2 measure only, the clinical significance of an energy consumption marker and the convenience of this technique may make it a useful tool in the functional assessment of the neonatal population.

Project description:Magnetic resonance imaging (MRI) offers the possibility to non-invasively map the brain's metabolic oxygen consumption (CMRO2), which is essential for understanding and monitoring neural function in both health and disease. However, in depth study of oxygen metabolism with MRI has so far been hindered by the lack of robust methods. One MRI method of mapping CMRO2 is based on the simultaneous acquisition of cerebral blood flow (CBF) and blood oxygen level dependent (BOLD) weighted images during respiratory modulation of both oxygen and carbon dioxide. Although this dual-calibrated methodology has shown promise in the research setting, current analysis methods are unstable in the presence of noise and/or are computationally demanding. In this paper, we present a machine learning implementation for the multi-parametric assessment of dual-calibrated fMRI data. The proposed method aims to address the issues of stability, accuracy, and computational overhead, removing significant barriers to the investigation of oxygen metabolism with MRI. The method utilizes a time-frequency transformation of the acquired perfusion and BOLD-weighted data, from which appropriate feature vectors are selected for training of machine learning regressors. The implemented machine learning methods are chosen for their robustness to noise and their ability to map complex non-linear relationships (such as those that exist between BOLD signal weighting and blood oxygenation). An extremely randomized trees (ET) regressor is used to estimate resting blood flow and a multi-layer perceptron (MLP) is used to estimate CMRO2 and the oxygen extraction fraction (OEF). Synthetic data with additive noise are used to train the regressors, with data simulated to cover a wide range of physiologically plausible parameters. The performance of the implemented analysis method is compared to published methods both in simulation and with in-vivo data (n=30). The proposed method is demonstrated to significantly reduce computation time, error, and proportional bias in both CMRO2 and OEF estimates. The introduction of the proposed analysis pipeline has the potential to not only increase the detectability of metabolic difference between groups of subjects, but may also allow for single subject examinations within a clinical context.

Project description:BackgroundThe importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data.ResultsThree parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality.ConclusionsThe parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.

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Multiplexed MRI methods for rapid estimation of global cerebral metabolic rate of oxygen consumption.

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Multiplexed MRI methods for rapid estimation of global cerebral metabolic rate of oxygen consumption.

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