The Department would like to recognize Yueh Z. Lee, MD, PhD, for multiple significant honors spanning late 2018 through early-year 2019.
A March 2019 Physics World article spotlights the development of a first-generation, stationary digital breast tomosynthesis (sDBT) system by lead author Lee and co-investigators. In a paired-image study published in Academic Radiology*, Lee’s team modified a standard commercial DBT system through replacing single rotating X-ray tubes with a fixed array of carbon nanotube-enabled (CNT) X-ray sources. Results showed the addition of sDBT to a standard system produced rapid, motion-free collection of multiple projection views over a wide-angle span, enabling a greater number of radiologists to identify a malignancy correctly when interpreting sDBT images, versus standard mammography.
Also in March, Dr. Lee was among 30 early-career scholars who received a 2019 travel award from the Academy for Radiology & Biomedical Imaging Research Academic Council (ARRAC), of the Academy for Radiology & Biomedical Imaging Research (ARBIR). All recipients will be welcomed as new members of the Academy Council of Early Career Investigators in Imaging (CECI2) in traveling to Washington, DC, on award support to 10th Annual Medical Imaging Technology Showcase in April. The Academy formed CECI2 to ensure early-career imaging scientists can engage and further understand the importance of advocacy for their field on Capitol Hill. As one of CECI2 ‘s new members learning the work of Academy’s advocacy branch, at this event Dr. Lee will present current innovative imaging research at UNC to a Capitol Hill lay audience, as well as visit Congressional offices and leadership at various NIH branches.
In February, Dr. Lee received a $50K Pilot Award from the UNC School of Medicine’s Computational Medicine Program. The award extends one-year (2.1.2019 – 2.1.2020) funding to support Lee’s proposed project – “Breast Cancer Molecular Subtype Prediction from Stationary Digital Breast Tomosynthesis Imaging” – as Co-Investigator. Over the study period, the project aims to use machine learning to evaluate breast cancer subtypes based on tomosynthesis imaging. This study is a retrospective study to see if these subtypes can be derived from the recently published study on stationary digital breast tomosynthesis.
In late 2018, Dr. Lee and his multidisciplinary team received NIH funding with Kitware, Inc., for a two-year (9/30/18-8/31/20) project developing automated brain collateral vessel analysis software. The team’s goal is to improve patient selection for intra-arterial thrombolytic therapy for acute stroke, as well as to better predict patient outcomes. Kitware, Inc., a software R&D company, offers expertise in such areas as computer vision, high-performance computing and visualization, and medical computing. Their funding is through a Phase II National Institute of Neurological Disorders and Stroke (NINDS) R42 Small Business Technology Transfer (STTR) project to produce automated software pipeline. Former UNC Radiology faculty member Stephen Aylward, PhD, leads the technical development at Kitware. This project will combine Computer Science, Neurology and Neuroradiology faculty expertise under Dr. Lee’s leadership in neuroradiology. Dr. Lee will work closely with faculty from UNC’s Departments of Computer Science (Dr. Marc Niethammer) and Neurology (Dr. David Huang).
* Lee YZ, Puett C, Inscoe CR, PhDc, Beilin J, Kim C, Walsh R, Yoon S, Kim SJ, Kuzmiak CM, Zeng D, Lu J, Zhou O. Acad Radiol. 2019 Jan 16. pii: S1076-6332(19)30019-4. doi: 10.1016/j.acra.2018.12.026. [Epub ahead of print]