Medical Imaging

La resonancia magnética nuclear está basada en el fenómeno del magnetismo nuclear que más aplicaciones ha encontrado para el estudio de enfermedades humanas. Usualmente la señal de RM es recibida y transmitida a distancias cercanas al objeto del que se quiere obtener una imagen. Otra alternativa es emitir y recibir la misma señal de manera remota haciendo uso de guías de onda. Este enfoque tiene la ventaja que se puede aplicar a altos campos magnéticos, la absorción de energía es menor, además es posible cubrir mayores regiones de interés y comodidad para el paciente. Por otro lado, sufre de baja calidad de imagen en algunos casos. En esta ocasión hablaremos de nuestra experiencia haciendo uso de este enfoque empleando una guía de ondas abierta y metamateriales tanto para sistemas clínicos como preclínicos de IRM.

Human brain is the most dynamic and varied system of the body. The brain is composed of neuron and glia. But how do they interact to generate emergent properties like memory, learning, emotion and sleep is little understood. Many such complex systems that exist in non-linear dynamics are characterized by the fractal nature. The fractal dimension (FD) is a quantitative parameter that has been extensively used to analyse the complexity of structural and functional patterns of the human brain. The fractal dimension (FD) of the human brain quantifies the inherent complexity. Evidences strongly suggest that fractal properties of a biologic system might be related to entropy and metabolism. In several pathologies of the brain such as Alzheimer’s, Epilepsy and Stroke, fractal dimension (FD) is altered. FD in combination with other features is emerging as a powerful diagnostic approach at the hands of a clinician. Alzheimer disease (AD) is a progressive neurodegenerative disease that destroys memory and cognitive skills. Aging is the biggest risk factor for AD. The central quest of research on AD is to identify the steps in its pathogenesis that, if inhibited, would slow or prevent the disease. All AD patients develop neuritic plaques in brain areas subserving memory and cognition. These plaques consist of extracellular masses of Aβ filaments intimately associated with dystrophic dendrites and axons, activated microglia, and reactive astrocytes. In 1983, Benoit Mandelbrot, the founder of fractal geometry, presented the amazing world of fractals to the world. Fractals are infinitely complex objects which are self similar in different scales. In this study we are focused to understand the changes in fractal properties (FD) of human brain as a whole in glioma during the states of AD. A non-linear analysis called the Fractal Dimension (FD) has been performed to quantify the fractal complexity of AD. Our primary goal is to investigate FD to assess whether it can discriminate between different states of AD. From FD analysis, we noticed that the fractal dimension increases with aging the AD, i.e. the complexity and self similarity of brain structure increases. We perform multi-fractal analysis to discover whether AD and its states belong to class of multi-fractal object for which a large number of scaling exponents are required to characterize their scaling structures. We plot the multi-fractal spectra of the fMRI images to compare the width of the scaling exponent for each spectrum. According to our analysis, we have a wide range of exponents for AD fMRI images, which indicates different states of Alzheimer’s disease have multi-fractal structure. As a result, fractal geometry can be considered as a computational framework to characterize different stages of AD and with further analysis, it can be used as a diagnostic tool to fight against Alzheimer’s disease.

Among all prevalent diseases with a central neurodegeneration, AMD can be considered the most promising in terms of prevention and early intervention, due to several factors surrounding the neural geometry of the foveal singularity. • Steep gradients of cell density, deployed in a radially symmetric fashion, can be modeled with a difference of Gaussian curves. • These steep gradients give rise to huge, spatially aligned biologic effects, summarized as the Center of Cone Resilience, Surround of Rod Vulnerability. • Widely used clinical imaging technology provides cellular and subcellular level information. • Data are now available at all timelines: clinical, lifespan, evolutionary • Snapshots are available from tissues (histology, analytic chemistry, gene expression) • A viable biogenesis model exists for drusen, the largest population-level intraocular risk factor for progression. • The biogenesis model shares molecular commonality with atherosclerotic cardiovascular disease, for which there has been decades of public health success. • Animal and cell model systems are emerging to test these ideas.

The neurovascular unit (NVU) consists of cerebral blood vessels, neurons, astrocytes, microglia, and pericytes. It plays a vital role in regulating blood flow and ensuring the proper functioning of neural circuits. Among other, this is made possible by the blood-brain barrier (BBB), which acts as both a physical and functional barrier. Previous studies have shown that dysfunction of the BBB is common in most neurological disorders and is associated with neural dysfunction. Our studies have demonstrated that BBB dysfunction results in the transformation of astrocytes through transforming growth factor beta (TGFβ) signaling. This leads to activation of the innate neuroinflammatory system, changes in the extracellular matrix, and pathological plasticity. These changes ultimately result in dysfunction of the cortical circuit, lower seizure threshold, and spontaneous seizures. Blocking TGFβ signaling and its associated pro-inflammatory pathway can prevent this cascade of events, reduces neuroinflammation, repairs BBB dysfunction, and prevents post-injury epilepsy, as shown in experimental rodents. To further understand and assess BBB integrity in human epilepsy, we developed a novel imaging technique that quantitatively measures BBB permeability. Our findings have confirmed that BBB dysfunction is common in patients with drug-resistant epilepsy and can assist in identifying the ictal-onset zone prior to surgery. Current clinical studies are ongoing to explore the potential of targeting BBB dysfunction as a novel treatment approach and investigate its role in drug resistance, the spread of seizures, and comorbidities associated with epilepsy.

Identifying a structural brain lesion on MRI is the most important factor that correlates with seizure freedom after surgery in patients suffering from drug-resistant focal epilepsy. By providing better image contrast and higher spatial resolution, structural MRI at 7 Tesla (7T) can lead to lesion detection in about 25% of patients presenting with negative MRI at lower fields. In addition to a better detection/delineation/phenotyping of epileptogenic lesions, higher signal at ultra-high field also facilitates more detailed analyses of several functional and molecular alterations of tissues, susceptible to detect epileptogenic properties even in absence of visible lesions. These advantages but also the technical challenges of 7T MRI in practice will be presented and discussed.

Epilepsy surgery is a safe but underutilised treatment for drug-resistant focal epilepsy. One challenge in the presurgical evaluation of patients with drug-resistant epilepsy are patients considered “MRI negative”, i.e. where a structural brain abnormality has not been identified on MRI. A major pathology in “MRI negative” patients is focal cortical dysplasia (FCD), where lesions are often small or subtle and easily missed by visual inspection. In recent years, there has been an explosion in artificial intelligence (AI) research in the field of healthcare. Automated FCD detection is an area where the application of AI may translate into significant improvements in the presurgical evaluation of patients with focal epilepsy. I will provide an overview of our automated FCD detection work, the Multicentre Epilepsy Lesion Detection (MELD) project and how AI algorithms are beginning to be integrated into epilepsy presurgical planning at Great Ormond Street Hospital and elsewhere around the world. Finally, I will discuss the challenges and future work required to bring AI to the forefront of care for patients with epilepsy.