Abstract
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.
Highlights
Under normal conditions the brain’s energy needs are met via a continuous supply of oxygen and glucose for the local production of ATP via aerobic metabolism (Verweij et al, 2007)
In this paper we present an analysis pipeline comprised of an extremely randomized trees regressor and a multi-layer perceptron (MLP), cascaded to infer resting CBF and CMRO2 from dual-calibrated fMRI data
Analysis of the simulated data demonstrated a substantial reduction in the RMS error of machine learning OEF estimates compared to regularized non-linear least squares (rNLS) estimates
Summary
Under normal conditions the brain’s energy needs are met via a continuous supply of oxygen and glucose for the local production of ATP via aerobic metabolism (Verweij et al, 2007). The cerebral metabolic rate of oxygen consumption (CMRO2) has traditionally been measured with positron emission tomography (Frackowiak et al, 1980) This method has some substantial limitations including the use of ionizing radiation and the need for local production of 15-oxygen labeled tracers. One promising technique of non-invasively mapping CMRO2 is the so-called dual-calibrated fMRI (dcfMRI) method (Bulte et al, 2012; Gauthier et al, 2012) This method is finding growing adoption in the research setting, and has already been applied in Alzheimer’s disease (Lajoie et al, 2017), carotid artery occlusion (De Vis et al, 2015), and studies of pharmacological modulation (Merola et al, 2017). Despite the promise shown by this technique, the reported between-session repeatability is relatively low (Merola et al, 2018) and improvements in the data acquisition and/or analysis are required if individualized assessment is to be made possible
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