Research
Mission
The 3D and Quantitative Imaging Laboratory is dedicated to exploring and developing innovative visualization and post-processing techniques of medical imaging to aid in the quantitative assessment and analysis of disease diagnosis, treatment planning and treatment evaluation. We strive to apply the most advanced image processing technologies and systems to maximize the diagnostic information obtained from each imaging study to ensure clinicians provide excellent care for our patients.
Our research endeavors include developing unique post-processing protocols, spearheading interdisciplinary clinical programs, and implementing emerging technologies to help visualize the most complex of cases. Such work is possible only through close collaborations between the Department of Radiology and clinicians from other specialties, including cardiothoracic surgery, neurology, oncology, and pediatrics, as well as faculty and researchers from other school, outside institutions, and industry.
The 3DQ Lab uses additive manufacturing technologies to generate three-dimensional anatomical models that demonstrate areas of a patient’s body. Our lab provides a variety of 3D printing modalities such as FDM, SLA, and DLP; with materials spanning opaque ABS to semi-transparent photopolymers. We utilize DICOM data to reproduce patient-specific anatomy that can be used for education, pre-surgical planning, and patient consent. 3D Print orders can currently be placed through EPIC.
Patients with aortic diseases require life-long surveillance with imaging to detect and prevent catastrophic outcomes, such as aneurysm formation and rupture. Surgical decisions are based on sizes at various locations of the aorta and the growth rate. Identifying and measuring the exact locations along the aorta between cohorts of patients is challenging but can be addressed through standardized measurement locations. Communicating this information is possible with our database and custom graphing tools.
The 3DQ Lab performs computational fluid dynamic simulations of patient-specific aortic dissections. In collaboration with the Marsden Lab, we utilize Fluid-Structure-Interaction methods on Arbitrary-Eulerian-Lagrangian meshes to obtain realistic pressure and velocity information in patient-specific models. Learning about the interaction of deformation, wall shear stress, pressure differences, and oscillatory shear stress assists with understanding the driving factors in disease progression.