Research in the Wojciech Zbijewski Lab — a component of the Imaging for Surgery, Therapy and Radiology (I-STAR) Lab — focuses on system modeling techniques to optimize the x-ray CT imaging chain. We’re specifically interested in: 1) using numerical models to improve the task-based optimization of image quality; 2) exploring advanced modeling of physics in statistical reconstruction; 3) using accelerated Monte Carlo methods in CT imaging; and 4) conducting experimental validation of such approaches and applying them to the development of new imaging methods.

The Connor Laboratory focuses on understanding the neural algorithms that make object vision possible. The goal of our research is to explain the neural basis of visual experience and contribute to designs for more powerful machine vision systems and brain-machine interfaces.

The Linda Smith-Resar Lab primarily investigates hematologic malignancy and molecular mechanisms that lead to cancer as well as sickle cell anemia. Recent studies suggest that education is an important and effective component of a patient blood management program and that computerized provider order entry algorithms may serve to maintain compliance with evidence-based transfusion guidelines. Another recent study indicated that colonic epithelial cells undergo metabolic reprogramming during their evolution to colorectal cancer, and the distinct metabolites could serve as diagnostic tools or potential targets in therapy or primary prevention.

Research in the Quantitative Imaging Technologies lab — a component of the Imaging for Surgery, Therapy and Radiology (I-STAR) Lab — focuses on novel technologies to derive accurate structural and physiological measurements from medical images. Our team works on optimization of imaging systems and algorithms to support a variety of quantitative applications, with recent focus on orthopedics and bone health. For example, we have developed an ultra-high resolution imaging chain for an orthopedic CT system to enable in-vivo measurements of bone microstructure. Our interests also include automated methods to extract quantitative information from images, including anatomical and micro-structural measurements, and shape analysis.

The Mathioudakis lab is focused on developing and evaluating clinical decision support systems, technology, and mHealth for diabetes prevention and management. Our lab leverages large electronic medical record databases and uses machine learning algorithms and artificial intelligence to identify patterns in clinical care associated with optimal clinical outcomes. We are interested in understanding the role that advanced diabetes technologies can play in improving health outcomes for patients with diabetes. Our lab has published extensively on outcomes related to diabetes prevention and diabetes management and outcomes. Based on data from our long-term (over 10 year) clinic-based prospective cohort study from the Johns Hopkins Multidisciplinary Diabetic Foot and Wound Clinic, we have published extensively on clinical predictors and outcomes of patients with diabetic foot ulcers, focusing specifically on the role that glycemic control plays in patients with this complication. Healthcare disparities exist throughout medicine, but are particularly prominent in diabetes; our lab has evaluated healthcare inequities in diabetes outcomes and is developing and evaluating strategies to overcome them. In addition to identify optimal management approach to diabetes and its complications, our lab is interested in development and evaluation of innovative technology approaches to diabetes prevention.