Established in 1990, Carl E. Ravin Advanced Imaging Laboratories (RAI Labs) is a coalition of inter-dependent laboratories pursuing research in translational and quantitative imaging. Its faculties are affiliated with the Departments of Radiology, Biomedical Engineering, Electrical and Computer Engineering, and Physics, the Medical Physics Graduate Program, and the Clinical Imaging Physics Group (CIPG) at Duke University. The group is characterized by projects having rigorous quantitative components and well-defined clinical ends to ensure scientific rigor and a robust clinical outcome toward optimal patient care. The research pursued by the group has a comprehensive scope that encompasses several key aspects of medical imaging from bench to bedside:
A. Imaging physics and image formation
B. Advanced engineering design
C. Virtual clinical trials
D. Image performance informatics
E. Perceptual and computerized image analysis
The projects in RAI Labs, primarily supported by federal grants with active collaboration with leading medical imaging companies, include the development of novel imaging modalities, optimization using advanced patient modeling algorithms, clinical trials of new imaging methods, development of innovative image analysis algorithms to quantitatively characterize the disease and its progression, and development of image-based decision support systems for disease detection, diagnosis, treatment planning, and prognosis. One of the unique assets of the group is its integrated identity, which enables the pursuit of independent projects while drawing from a close resource infrastructure of expertise and equipment. Through its efforts, RAI Labs is committed to advancing scientific knowledge in quantitative medical imaging, enabling its clinical translation, and maximizing its clinical impact. To date, the research undertaken by the group has been the basis of over 1000 scientific papers, hundreds of conference presentations, and multiple patents.
ABOUT THE LOGO
The RAI Labs logo is based on the “histogram” of the Duke Chapel, the most iconic symbol of Duke University situated at the heart of the campus. The histogram is modified with added uncorrelated noise with a signal-to-noise ratio (SNR) of 5. Assuming Rose model, a SNR threshold of 3-5 is required to detect a radiographic lesion in a noisy background. A SNR value at the high end of this range was chosen for the logo to preserve some of the architectural features of the chapel.