Radiology Research Labs
The Advanced Imaging Lab (AIL) engages basic science and clinical faculty to provide a platform for cutting edge non-invasive translational imaging research. Housed in Children’s Hospital Colorado, we focus on pediatric imaging; however, research activities often span the lifetime of the patient. AIL faculty and staff include experts in MRI pulse sequence programming, cardiac and vascular MRI, image reconstruction and postprocessing. Additional services include the operation of an imaging core, which supports clinical and research image acquisition and postprocessing.
The AI Medical Imaging Lab at the University of Colorado Anschutz is a multidisciplinary group at the intersection of radiology, biomedical engineering, and machine learning. Our mission is to create reliable AI systems that make imaging interpretation faster, more consistent, and more predictive—and to deliver those systems safely into real clinical use. Methodologically, we focus on foundation and vision-language models that align images with radiology reports and clinical data, enabling automated structured reporting, robust lesion detection/segmentation, longitudinal response assessment, and survival/risk prediction.
We emphasize scale and generalization through multi-institutional datasets, data harmonization, rigorous benchmarks, and external validation. Clinically, our programs span FDG and PSMA PET/CT in oncology, CT for pulmonary embolism risk stratification, and multimodal pipelines that combine imaging with EHR signals. Translationally, we collaborate with Brown University and Johns Hopkins and partner with Siemens Healthineers to integrate models into syngo.via and teamplay for evaluation, QA, and eventual deployment.
Our culture is hands-on and collaborative: physicians, engineers, and data scientists co-design studies, annotate data, ship code, and iterate with clinicians. We mentor trainees across levels and share practical tools whenever possible. Ultimately, our goal is simple: AI that is accurate, reliable, and useful—improving reports, decisions, and outcomes for patients.
We are interested in better understanding how brain function relates to an individual’s likelihood for engaging in risky patterns of substance use. We are currently studying adolescents in the transition period after high school to determine whether brain imaging metrics can help predict an individual’s likelihood of developing problematic patterns of use in the future. Future interests include examining the endogenous cannabinoid system to better understand how its function relates to substance use. We are also interested in acute drug administration methods to examine how neurotransmitter systems like dopamine and serotonin impact behavior and overall brain function.
The iQ Lab is dedicated to the development and implementation of novel image analysis algorithms for the identification and quantification of relevant features in medical images for musculoskeletal assessments in osteoporosis, osteoarthritis and HIV.
We work with magnetic resonance imaging (MRI), quantitative computed tomography (QCT), high-resolution peripheral quantitative computed tomography (HR-pQCT) and dual X-ray absorptiometry (DXA).
