Multidisciplinary research to beat cancer

The Davenport Lab is a branch of the ANSIR Lab at UTSW that focuses on quantitative methods for human brain imaging, primarily using MRI and Magnetoencephalography (MEG).

Zaman’s Lab focuses on the design and development of novel cutting-edge multi-mode imaging systems to overcome current limitations in clinical systems. Most recent research project is involved with the design and developed of a multimode catheter-based imaging system called a Circumferential Intravascular Radioluminescence Photoacoustic Imaging (CIRPI) for early detection of thin-cap-fibro-atheroma (TCFA), the underlying causes of coronary artery disease, one of the leading causes of morbidity and mortality in the USA and worldwide. Further, the CIRPI system characterizes the plaques based on disease tissue compositions to unravel their complex structures. This CIRPI system integrates optical, photoacoustic, radioluminescence and ultrasound imaging. We seek to better understand the underlying causes of the disease mechanisms. We are dissecting the role of TCFA perturbations on vascular wall processes during atherosclerosis progression. Our lab also studying novel molecular imaging methods to study coronary arterial disease, carotid stenosis, and myocardial ischemia in subcellular level.

The Sharma Lab is interested in investigating intermediary metabolism utilizing carbon-13 stable isotope tracers in conjunction with magnetic resonance spectroscopy (MRS), magnetic resonance imaging (MRI), and mass spectrometry (MS).

Translational biophotonics for noninvasive detection of systemic disease.

Our research endeavors are dedicated to pioneering the development of virtual reality simulators tailored for surgical training, harnessing the power of cutting-edge technologies such as artificial intelligence, automated systems, and advanced deep learning algorithms. Central to our work is the creation of AI-driven solutions for automated surgical video segmentation, precise identification of anatomical structures, surgical tool recognition, and comprehensive skills assessment.

I am interested in developing computational models and algorithms for big data to predict patients' outcomes, which can help clinicians to tailor treatment plans for individual patients.

We aim to globally understand how the physical and chemical properties of materials affect interactions with biological systems in the context of improving therapies.

We develop the theory and application of deep learning to improve diagnoses, prognoses and therapy decision making.

Translational Research in UltraSound Theranostics (TRUST) Lab at UT Southwestern