|Division:||Radiology - General|
Ravin Advanced Imaging Labs
2424 Erwin Road, Suite 302
Durham, NC 27705
|Office Phone:||(919) 684-7763|
Research InterestsMy lab focuses on the diagnosis and treatment of breast cancer using advanced imaging techniques. There are 3 main projects: tomosynthesis imaging, radiomics, and breast modeling.
First, I lead a team from the Ravin Advanced Imaging Laboratories (RAI Labs) that collaborated closely with Siemens Healthcare to develop digital breast tomosynthesis (DBT) imaging, a form of limited-angle tomography also known as "3D mammography." DBT can acquire a 3D image quickly, easily, and at comparable dose to conventional mammography. By improving both sensitivity and specificity of breast cancer diagnosis, DBT has become the most exciting recent development in breast cancer screening, and the only technology with the potential to replace mammography in the near future. This work led to the FDA approval of the Siemens DBT system. We continue to investigate DBT in terms of clinical protocols and physics optimization.
Second, radiomics is an interdisciplinary field combining computer vision, machine learning, and informatics. We developed computer vision algorithms to detect suspicious mammographic lesions. We also created predictive models that use machine learning and statistical analysis in order to classify mammograms as benign versus malignant. In ongoing studies funded by NIH and DOD, we are addressing the clinically significant challenge of over-diagnosis of DCIS. By exploring the relationship between imaging findings and genomic markers, we hope to predict which cases of DCIS are likely to be indolent vs. aggressive, thus providing women with more personalized risk assessment to inform their treatment decisions.
Finally, we are designing new virtual breast models that are based on actual patient data. These models go far beyond conventional phantoms in portraying realistic breast anatomy. Furthermore, we can transform these virtual models into physical form using the latest 3D printing technology. Such physical phantoms can be scanned on actual mammography and DBT systems, allowing us to measure image quality in new ways that are not only quantitative but also clinically relevant. We continue to refine the realism of these physical phantoms, and seek to develop new procedures for quality control, system evaluation, and the long term goal of virtual clinical trials.
Shi, B; Grimm, LJ; Mazurowski, MA; Baker, JA; Marks, JR; King, LM; Maley, CC; Hwang, ES; Lo, JY. Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?. Academic Radiology. 2017;24:1139-1147. Abstract
Erickson, DW; Wells, JR; Sturgeon, GM; Samei, E; Dobbins, JT; Segars, WP; Lo, JY. Population of 224 realistic human subject-based computational breast phantoms. Medical physics. 2016;43:23. Abstract
Ikejimba, LC; Glick, SJ; Choudhury, KR; Samei, E; Lo, JY. Assessing task performance in FFDM, DBT, and synthetic mammography using uniform and anthropomorphic physical phantoms. Medical physics. 2016;43:5593. Abstract
Ikejimba, L; Lo, JY; Chen, Y; Oberhofer, N; Kiarashi, N; Samei, E. A quantitative metrology for performance characterization of five breast tomosynthesis systems based on an anthropomorphic phantom. Medical physics. 2016;43:1627. Abstract
Kiarashi, N; Nolte, AC; Sturgeon, GM; Segars, WP; Ghate, SV; Nolte, LW; Samei, E; Lo, JY. Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data. Medical physics. 2015;42:4116-4126. Abstract
Kiarashi, N; Sturgeon, GM; Nolte, LW; Lo, JY; III, JTD; Segars, WP; Samei, E. Development of matched virtual and physical breast phantoms based on patient data. Proceedings of SPIE.