The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring

Medical imaging of the nervous system is the methodology used to achieve pictures of parts of the nervous system for therapeutic uses to recognize the ailments. Magnetic resonance imaging (MRI) is a kind of medical imaging tool that utilizes solid magnetic fields and radio waves to deliver point-by-point pictures of the inside of the body. There are large number of imaging methodologies done each week around the world. Medical imaging is developing rapidly due to developments in image acquisition tools including functional MRI and hybrid imaging modalities. This chapter abridged the role of magnetic resonance imaging (MRI) in autonomic nervous system monitoring. This chapter also summarizes the image interpretation challenges in diagnosing autonomic nervous system disorders

Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging.

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an imaging methodology that uses blood as an endogenous contrast agent to quantify flow. One limitation of this method of capillary blood quantification when applied in the lung is the contribution of signals from non-capillary blood. Intensity thresholding is one approach that has been proposed for minimizing the non-capillary blood signal. This method has been tested in previous in silico modeling studies; however, it has only been tested under a restricted set of physiological conditions (supine posture and a cardiac output of 5 L/min). This study presents an in silico approach that extends previous intensity thresholding analysis to estimate the optimal "per-slice" intensity threshold value using the individual components of the simulated ASL signal (signal arising independently from capillary blood as well as pulmonary arterial and pulmonary venous blood). The aim of this study was to assess whether the threshold value should vary with slice location, posture, or cardiac output. We applied an in silico modeling approach to predict the blood flow distribution and the corresponding ASL quantification of pulmonary perfusion in multiple sagittal imaging slices. There was a significant increase in ASL signal and heterogeneity (COV = 0.90 to COV = 1.65) of ASL signals when slice location changed from lateral to medial. Heterogeneity of the ASL signal within a slice was significantly lower (P = 0.03) in prone (COV = 1.08) compared to in the supine posture (COV = 1.17). Increasing stroke volume resulted in an increase in ASL signal and conversely an increase in heart rate resulted in a decrease in ASL signal. However, when cardiac output was increased via an increase in both stroke volume and heart rate, ASL signal remained relatively constant. Despite these differences, we conclude that a threshold value of 35% provides optimal removal of large vessel signal independent of slice location, posture, and cardiac output

Dynamic And Quantitative Radiomics Analysis In Interventional Radiology

Interventional Radiology (IR) is a subspecialty of radiology that performs invasive procedures driven by diagnostic imaging for predictive and therapeutic purpose. The development of artificial intelligence (AI) has revolutionized the industry of IR. Researchers have created sophisticated models backed by machine learning algorithms and optimization methodologies for image registration, cellular structure detection and computer-aided disease diagnosis and prognosis predictions. However, due to the incapacity of the human eye to detect tiny structural characteristics and inter-radiologist heterogeneity, conventional experience-based IR visual evaluations may have drawbacks. Radiomics, a technique that utilizes machine learning, offers a practical and quantifiable solution to this issue. This technology has been used to evaluate the heterogeneity of malignancies that are difficult to detect by the human eye by creating an automated pipeline for the extraction and analysis of high throughput computational imaging characteristics from radiological medical pictures. However, it is a demanding task to directly put radiomics into applications in IR because of the heterogeneity and complexity of medical imaging data. Furthermore, recent radiomics studies are based on static images, while many clinical applications (such as detecting the occurrence and development of tumors and assessing patient response to chemotherapy and immunotherapy) is a dynamic process. Merely incorporating static features cannot comprehensively reflect the metabolic characteristics and dynamic processes of tumors or soft tissues. To address these issues, we proposed a robust feature selection framework to manage the high-dimensional small-size data. Apart from that, we explore and propose a descriptor in the view of computer vision and physiology by integrating static radiomics features with time-varying information in tumor dynamics. The major contributions to this study include: Firstly, we construct a result-driven feature selection framework, which could efficiently reduce the dimension of the original feature set. The framework integrates different feature selection techniques to ensure the distinctiveness, uniqueness, and generalization ability of the output feature set. In the task of classification hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) in primary liver cancer, only three radiomics features (chosen from more than 1, 800 features of the proposed framework) can obtain an AUC of 0.83 in the independent dataset. Besides, we also analyze features’ pattern and contributions to the results, enhancing clinical interpretability of radiomics biomarkers. Secondly, we explore and build a pulmonary perfusion descriptor based on 18F-FDG whole-body dynamic PET images. Our major novelties include: 1) propose a physiology-and-computer-vision-interpretable descriptor construction framework by the decomposition of spatiotemporal information into three dimensions: shades of grey levels, textures, and dynamics. 2) The spatio-temporal comparison of pulmonary descriptor intra and inter patients is feasible, making it possible to be an auxiliary diagnostic tool in pulmonary function assessment. 3) Compared with traditional PET metabolic biomarker analysis, the proposed descriptor incorporates image’s temporal information, which enables a better understanding of the time-various mechanisms and detection of visual perfusion abnormalities among different patients. 4) The proposed descriptor eliminates the impact of vascular branching structure and gravity effect by utilizing time warping algorithms. Our experimental results showed that our proposed framework and descriptor are promising tools to medical imaging analysis

Development of non-invasive MRI to measure water permeability across the blood-brain interface

The blood-brain interface (BBI) is a physical and biochemical barrier that protects and maintains healthy brain function. Disruption of the BBI is indicative of the early stages of certain neurodegenerative diseases, such as Alzheimer’s Disease. However, there is currently a lack of sensitive tools available to accurately quantify the early alterations to the integrity of the BBI. This thesis describes the development and implementation of multiple echo time arterial spin labelling (multi-TE ASL) MRI technique in the mouse brain to measure vascular water permeability across the BBI. The technique was implemented in two high-field MRI system to demonstrate the consistency of the imaging protocols and the sensitivity of the measures of BBI water permeability. The multi-TE ASL technique was used to probe the function of aquaporin-4 (AQP4) water channels, which play a key role in the clearance of the deleterious proteins from the brain. This non-invasive technique was able to demonstrate its sensitivity to targeting AQP4 by measuring a 31% slowing of cortical BBI water permeability with the removal of the AQP4 water channels. The technique also measured a 34% slowing in the BBI water permeability in the cerebellum brain region with a reduction of AQP4 channels at the BBI. Finally, the technique measured a 32% increase in cortical BBI permeability to water in a mouse model of ageing. The non-invasive imaging measurements were 7 associated with a 2-fold increase in mRNA expression of pericytes, while other BBI markers such as tight junction proteins were maintained. Overall, this work has demonstrated the scope of novel MRI technique to target changes to BBI water permeability, with potential for clinical translation for the early detection and understanding of neurodegenerative disease

Development and Application of MRI Techniques for Non-Invasive Assessment of Blood-Cerebrospinal Fluid Barrier Function

The choroid plexus (CP) tissue forms the blood-cerebrospinal fluid barrier (BCSFB) - a unique interface which plays a critical role in effective homeostasis of the central nervous system. To date, exploration of the BCSFB’s role in health and disease has been hindered by a lack of non-invasive, translatable methodologies. The recent development of BCSFB-ASL MRI by Evans et al. has permitted the non-invasive, surrogate measurement of BCSFB function. The work presented herein develops and applies the BCSFB-ASL method to investigate BCSFB function in rodent models of ageing and disease. Chapter 2 describes a novel platform for simultaneous recording of BCSFB function and brain tissue perfusion using interleaved echo-time ASL, which provided insight into alterations of vessel tone at the BBB and BCSFB under the influence of pharmacological agents, as well as how reactivity towards a vasopressin challenge is impaired in the aged mouse brain. In Chapter 3, I reproduce, optimise, and characterise the BCSFB-ASL MRI approach on a Bruker 9.4T system, that was heretofore applied only on an Agilent 9.4T MRI system. This work seeks to utilise the improved hardware and software on the Bruker system to increase measurement precision with minimised scan times. Chapter 4 describes efforts to further characterise the contributing sources and kinetics of ultra-long echo-time ASL signals arising from brain-wide CSF regions. These experiments seek to determine the reliability of the estimated labelled blood water delivery rates, alongside potential factors which may contribute to the appearance of these signals, in regions distal to the caudal lateral ventricles. In Chapter 5, BCSFB function was then investigated in the context of systemic hypertension. Spontaneously hypertensive rats displayed a reduction in BCSFB function, which highlights the potential for such measures to serve as a sensitive early biomarker for hypertension-driven neurodegeneration. Overall, we demonstrate the scope of BCSFB-ASL to capture changes to BCSFB function, which not only has value in providing a useful biomarker for downstream neurodegeneration, but also provides an insight into mechanisms which may increase the brain’s susceptibility towards neurodegenerative outcomes