Machine Learning and Quantitative Neuroimaging in Epilepsy and Low Field MRI

Medical imaging plays a key role in the diagnosis and management of neurological disorders. Magnetic resonance imaging (MRI) has proven particularly useful, as it produces high resolution images with excellent tissue contrast, permitting clinicians to identify lesions and select appropriate treatments. However, demand for MRI services has outpaced the availability of qualified experts to operate, maintain, and interpret images from these devices. Radiologists often rely on time-consuming manual analyses, which further limits throughput. Moreover, a large portion of the world’s population cannot currently access MRI, and demand for medical imaging services will continue to increase as healthcare quality improves globally. To address these challenges, we must find innovative ways to automate medical processing and produce lower-cost medical imaging devices. Recent advances in deep learning and low-field MRI hardware offer potential solutions, providing lower-cost methods for processing and collecting images, respectively. This thesis aims to develop and validate lower-cost methods for collecting and interpreting neuroimaging using machine learning algorithms and portable, low-field MRI technology. In the first section, I develop a deep learning algorithm that automatically segments resection cavities in epilepsy surgery patients and quantifies removed tissues. I also compare the impacts of epilepsy surgery on remote brain regions, demonstrating that more selective procedures minimize postoperative cortical thinning. In the second section, I explore and validate clinical applications for a new portable, low-field MRI device. Using open-source imaging and machine learning, I propose a low-cost method for simulating diagnostic performance for novel imaging devices when only sparse data is available. Additionally, I validate device performance in multiple sclerosis by directly comparing the low-field device to standard-of-care imaging using a range of manual and automated analyses. My hope is that machine learning and low-field MRI will increase medical imaging access and improve patient care worldwide