Development of MRI methods to map cerebral metabolic oxygen consumption in humans

The quantification of brain activity has been one of the main goals of neuroimaging since the earliest applications. In functional magnetic resonance imaging (fMRI) such an aim has been pursued indirectly by studying changes of the blood oxygenation dependent signal triggered by alterations in blood flow following changes in energy metabolism. Such approach is limited because of the complex relationship between the vascular and neural systems in brain tissue. Therefore methods have been proposed to assess oxygen metabolism, which directly underlies energy supply to brain tissue and therefore brain activity. Investigating existing and novel MRI methods, the thesis aims to improve the assessment of oxygen metabolism for a fully quantitative measurement of this biomarker. A simulation study has been carried out to optimise one of the mathematical (fMRI calibration) models used to relate the measured signal to the underlying physiology. As a result we are able to define a new model, less complex and more accurate for estimation of oxygen extraction fraction. Following this, an estimation approach recently developed in our centre is applied to carbon dioxide and oxygen calibrated fMRI data in an experimental setting firstly for a repeatability study and then for a drug study looking at the acute effects of caffeine on brain metabolism and haemodynamics. The precision of the novel approach shows values consistent with previous methods, but with much higher spatial resolution. Exploiting this, acute caffeine effects are characterized with a voxel-wise level of detail, showing results consistent with literature electrophysiological findings. Finally, an innovative method for estimating oxygen extraction fraction, based on velocity spectral imaging and estimation of transverse relaxation time, is introduced and tested at a proof-of-concept level. The performance and limits are examined through simulation and experimentation, suggesting that it might be a viable alternative to the calibration techniques previously introduced

Assessing the repeatability of absolute CMRO 2 , OEF and haemodynamic measurements from calibrated fMRI

As energy metabolism in the brain is largely oxidative, the measurement of cerebral metabolic rate of oxygen consumption (CMRO2) is a desirable biomarker for quantifying brain activity and tissue viability. Currently, PET techniques based on oxygen isotopes are the gold standard for obtaining whole brain CMRO2 maps. Among MRI techniques that have been developed as an alternative are dual calibrated fMRI (dcFMRI) methods, which exploit simultaneous measurements of BOLD and ASL signals during a hypercapnic-hyperoxic experiment to modulate brain blood flow and oxygenation. In this study we quantified the repeatability of a dcFMRI approach developed in our lab, evaluating its limits and informing its application in studies aimed at characterising the metabolic state of human brain tissue over time. Our analysis focussed on the estimates of oxygen extraction fraction (OEF), cerebral blood flow (CBF), CBF-related cerebrovascular reactivity (CVR) and CMRO2 based on a forward model that describes analytically the acquired dual echo GRE signal. Indices of within- and between-session repeatability are calculated from two different datasets both at a bulk grey matter and at a voxel-wise resolution and finally compared with similar indices obtained from previous MRI and PET measurements. Within- and between-session values of intra-subject coefficient of variation (CVintra) calculated from bulk grey matter estimates 6.7 ± 6.6% (mean ± std.) and 10.5 ± 9.7% for OEF, 6.9 ± 6% and 5.5 ± 4.7% for CBF, 12 ± 9.7% and 12.3 ± 10% for CMRO2. Coefficient of variation (CV) and intraclass correlation coefficient (ICC) maps showed the spatial distribution of the repeatability metrics, informing on the feasibility limits of the method. In conclusion, results show an overall consistency of the estimated physiological parameters with literature reports and a satisfactory level of repeatability considering the higher spatial sensitivity compared to other MRI methods, with varied performance depending on the specific parameter under analysis, on the spatial resolution considered and on the study design

Pure autonomic failure versus prodromal dysautonomia in Parkinson’s disease: Insights from the bedside

Autonomic failure may include orthostatic hypotension, supine hypertension, bowel and bladder disturbances, impaired thermal regulation, and sexual dysfunction, all of which can be features of Parkinson's disease (PD) and other a‐synucleinopathies. All patients with pure autonomic failure, most patients with multiple system atrophy, and 18% of patients with PD will develop symptomatic orthostatic hypotension. However, the extent of central and peripheral norepinephrine deficiency, parasympathetic nuclei degeneration, and arterial baroreflex failure may be differentially impaired in these disorders. Consequently, clinical features and prognostic implications of autonomic dysfunction in a‐synucleinopathies may be more complex than previously envisioned. The case described in this report highlights the clinical similarities between PD and pure autonomic failure, raising the question of whether pure autonomic failure represents a restricted Lewy body synucleinopathy or an early manifestation of PD

Domain specific cues improve robustness of deep learning based segmentation of ct volumes

Machine Learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular Computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs), mostly due to the broad dynamic range of intensities and the varying number of recorded slices of CT volumes. In this paper, we address these issues with a framework that combines domain-specific data preprocessing and augmentation with state-of-the-art CNN architectures. The focus is not limited to optimise the score, but also to stabilise the prediction performance since this is a mandatory requirement for use in automated and semi-automated workflows in the clinical environment. The framework is validated with an architecture comparison to show CNN architecture-independent effects of our framework functionality. We compare a modified U-Net and a modified Mixed-Scale Dense Network (MS-D Net) to compare dilated convolutions for parallel multi-scale processing to the U-Net approach based on traditional scaling operations. Finally, we propose an ensemble model combining the strengths of different individual methods. The framework performs well on a range of tasks such as liver and kidney segmentation, without significant differences in prediction performance on strongly differing volume sizes and varying slice thickness. Thus our framework is an essential step towards performing robust segmentation of unknown real-world samples

A forward modelling approach for the estimation of oxygen extraction fraction by calibrated fMRI

The measurement of the absolute rate of cerebral metabolic oxygen consumption (CMRO2) is likely to offer a valuable biomarker in many brain diseases and could prove to be important in our understanding of neural function. As such there is significant interest in developing robust MRI techniques that can quantify CMRO2 non-invasively. One potential MRI method for the measurement of CMRO2 is via the combination of fMRI and cerebral blood flow (CBF) data acquired during periods of hypercapnic and hyperoxic challenges. This method is based on the combination of two, previously independent, signal calibration techniques. As such analysis of the data has been approached in a stepwise manner, feeding the results of one calibration experiment into the next. Analysing the data in this manner can result in unstable estimates of the output parameter (CMRO2), due to the propagation of errors along the analysis pipeline. Here we present a forward modeling approach that estimates all the model parameters in a one-step solution. The method is implemented using a regularized non-linear least squares approach to provide a robust and computationally efficient solution. The proposed framework is compared with previous analytical approaches using modeling studies and in-vivo acquisitions in healthy volunteers (n = 10). The stability of parameter estimates is demonstrated to be superior to previous methods (both in-vivo and in simulation). In-vivo estimates made with the proposed framework also show better agreement with expected physiological variation, demonstrating a strong negative correlation between baseline CBF and oxygen extraction fraction. It is anticipated that the proposed analysis framework will increase the reliability of absolute CMRO2 measurements made with calibrated BOLD

Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to efficiently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions