Prof Louise Thomas
For the past 30 years, my research has been driven by the intriguing question of why people with similar weight and height have varying risks of metabolic disease. This pursuit has led to the creation and implementation of innovative techniques based on whole-body magnetic resonance imaging (MRI) and spectroscopy (MRS). These techniques serve as non-invasive tools for developing precision phenotyping as a means of determining optimal health.
An essential aspect of my work has been my involvement in creating appropriate methods for data analysis. This process has evolved from manual analysis to implementing state-of-the-art deep learning methodologies, enabling us to conduct automated analysis on large-scale population datasets.
I became a part of the University of Westminster in October 2014. Since then, I have played a significant role in establishing and managing the Research Centre for Optimal Health (ReCOH), and more recently, the Health Data Sciences Research Group. In 2022, I took on the responsibility of Director of Research and Knowledge Exchange for the School of Life Sciences.
A substantial part of my research career has been dedicated to industrial collaborations, with projects sponsored by a wide array of companies such as Perspectum Diagnostics, Health Concierge, AMRA, Calico, GSK, GW Pharma, Tanita, Select Research, AstraZeneca, Numico, Quest Diagnostics, and Herbal Life.
My research has primarily focused on innovating MR techniques to characterize total, regional, and ectopic fat in obesity and NAFLD. Concurrently, I have been validating and developing cost-effective alternatives to MRI, including cutting-edge technology, blood marker-derived indices, and algorithmic approaches to serve as markers of visceral and liver fat content. This work has fostered a close association with the UK Biobank imaging study and led to successful industrial partnerships to enhance methods for measuring tissue and organ volumes and composition from abdominal MR images; to further enrich the depth and quantity of health metrics that can be derived from abdominal imaging.
