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A theoretical framework for estimating cerebral oxygen metabolism changes using the calibrated-BOLD method: modeling the effects of blood volume distribution, hematocrit, oxygen extraction fraction, and tissue signal properties on the BOLD signal.

ABSTRACT: Calibrated blood oxygenation level dependent (BOLD) imaging, a technique used to measure changes in cerebral O(2) metabolism, depends on an accurate model of how the BOLD signal is affected by the mismatch between changes in cerebral blood flow (CBF) and cerebral metabolic rate of O(2) (CMRO(2)). However, other factors such as the cerebral blood volume (CBV) distribution at rest and with activation also affect the BOLD signal. The Davis model originally proposed for calibrated BOLD studies (Davis et al., 1998) is widely used because of its simplicity, but it assumes CBV changes are uniformly distributed across vascular compartments, neglects intravascular signal changes, and ignores blood-tissue signal exchange effects as CBV increases and supplants tissue volume. More recent studies suggest that venous CBV changes are smaller than arterial changes, and that intravascular signal changes and CBV exchange effects can bias estimated CMRO(2). In this paper, recent experimental results for the relationship between deoxyhemoglobin and BOLD signal changes are integrated in order to simulate the BOLD signal in detail by expanding a previous model to include a tissue compartment and three blood compartments rather than only the venous blood compartment. The simulated data were then used to test the accuracy of the Davis model of calibrated BOLD, demonstrating that the errors in estimated CMRO(2) responses across the typical CBF-CMRO(2) coupling range are modest despite the simplicity of the assumptions underlying the original derivation of the model. Nevertheless, the accuracy of the model can be improved by abandoning the original physical meaning of the two parameters ? and ? and treating them as adjustable parameters that capture several physical effects. For a 3Tesla field and a dominant arterial volume change with activation, the accuracy of the Davis model is improved with new values of ?=0.14 and ?=0.91.

SUBMITTER: Griffeth VE

PROVIDER: S-EPMC3187858 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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A theoretical framework for estimating cerebral oxygen metabolism changes using the calibrated-BOLD method: modeling the effects of blood volume distribution, hematocrit, oxygen extraction fraction, and tissue signal properties on the BOLD signal.

Griffeth Valerie E M VE Buxton Richard B RB

NeuroImage 20110606 1

Calibrated blood oxygenation level dependent (BOLD) imaging, a technique used to measure changes in cerebral O(2) metabolism, depends on an accurate model of how the BOLD signal is affected by the mismatch between changes in cerebral blood flow (CBF) and cerebral metabolic rate of O(2) (CMRO(2)). However, other factors such as the cerebral blood volume (CBV) distribution at rest and with activation also affect the BOLD signal. The Davis model originally proposed for calibrated BOLD studies (Davi ...[more]

PMID: 21669292

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Project description:Magnetic resonance imaging (MRI)-based quantification of the blood-oxygenation-level-dependent (BOLD) effect allows oxygen extraction fraction (OEF) mapping. The multi-parametric quantitative BOLD (mq-BOLD) technique facilitates relative OEF (rOEF) measurements with whole brain coverage in clinically applicable scan times. Mq-BOLD requires three separate scans of cerebral blood volume and transverse relaxation rates measured by gradient-echo (1/T2∗) and spin-echo (1/T2). Although the current method is of clinical merit in patients with stroke, glioma and internal carotid artery stenosis (ICAS), there are relaxation measurement artefacts that impede the sensitivity of mq-BOLD and artificially elevate reported rOEF values. We posited that T2-related biases caused by slice refocusing imperfections during rapid 2D-GraSE (Gradient and Spin Echo) imaging can be reduced by applying 3D-GraSE imaging sequences, because the latter requires no slice selective pulses. The removal of T2-related biases would decrease overestimated rOEF values measured by mq-BOLD. We characterized effects of T2-related bias in mq-BOLD by comparing the initially employed 2D-GraSE and two proposed 3D-GraSE sequences to multiple single spin-echo reference measurements, both in vitro and in vivo. A phantom and 25 participants, including young and elderly healthy controls as well as ICAS-patients, were scanned. We additionally proposed a procedure to reliably identify and exclude artefact affected voxels. In the phantom, 3D-GraSE derived T2 values had 57% lower deviation from the reference. For in vivo scans, the formerly overestimated rOEF was reduced by -27% (p < 0.001). We obtained rOEF = 0.51, which is much closer to literature values from positron emission tomography (PET) measurements. Furthermore, increased sensitivity to a focal rOEF elevation in an ICAS-patient was demonstrated. In summary, the application of 3D-GraSE improves the mq-BOLD-based rOEF quantification while maintaining clinically feasible scan times. Thus, mq-BOLD with non-slice selective T2 imaging is highly promising to improve clinical diagnostics of cerebrovascular diseases such as ICAS.

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.

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Project description:Sickle cell anaemia is a monogenetic disorder with a high incidence of stroke. While stroke screening procedures exist for children with sickle cell anaemia, no accepted screening procedures exist for assessing stroke risk in adults. The purpose of this study is to use novel magnetic resonance imaging methods to evaluate physiological relationships between oxygen extraction fraction, cerebral blood flow, and clinical markers of cerebrovascular impairment in adults with sickle cell anaemia. The specific goal is to determine to what extent elevated oxygen extraction fraction may be uniquely present in patients with higher levels of clinical impairment and therefore may represent a candidate biomarker of stroke risk. Neurological evaluation, structural imaging, and the non-invasive T2-relaxation-under-spin-tagging magnetic resonance imaging method were applied in sickle cell anaemia (n = 34) and healthy race-matched control (n = 11) volunteers without sickle cell trait to assess whole-brain oxygen extraction fraction, cerebral blood flow, degree of vasculopathy, severity of anaemia, and presence of prior infarct; findings were interpreted in the context of physiological models. Cerebral blood flow and oxygen extraction fraction were elevated (P < 0.05) in participants with sickle cell anaemia (n = 27) not receiving monthly blood transfusions (interquartile range cerebral blood flow = 46.2-56.8 ml/100 g/min; oxygen extraction fraction = 0.39-0.50) relative to controls (interquartile range cerebral blood flow = 40.8-46.3 ml/100 g/min; oxygen extraction fraction = 0.33-0.38). Oxygen extraction fraction (P < 0.0001) but not cerebral blood flow was increased in participants with higher levels of clinical impairment. These data provide support for T2-relaxation-under-spin-tagging being able to quickly and non-invasively detect elevated oxygen extraction fraction in individuals with sickle cell anaemia with higher levels of clinical impairment. Our results support the premise that magnetic resonance imaging-based assessment of elevated oxygen extraction fraction might be a viable screening tool for evaluating stroke risk in adults with sickle cell anaemia.

Dataset Information

A theoretical framework for estimating cerebral oxygen metabolism changes using the calibrated-BOLD method: modeling the effects of blood volume distribution, hematocrit, oxygen extraction fraction, and tissue signal properties on the BOLD signal.

Publications

A theoretical framework for estimating cerebral oxygen metabolism changes using the calibrated-BOLD method: modeling the effects of blood volume distribution, hematocrit, oxygen extraction fraction, and tissue signal properties on the BOLD signal.

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