Joseph Y Lo
Professor of RadiologyMy 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.
Appointments and Affiliations
- Professor of Radiology
- Professor of Biomedical Engineering
- Member of the Duke Cancer Institute
- Office Location: Ravin Advanced Imaging Labs, 2424 Erwin Road, Suite 302, Durham, NC 27705
- Office Phone: (919) 684-7763
- Email Address: firstname.lastname@example.org
- Duke University, 1995
- Duke University, 1993
- Duke University, 1990
- Ph.D. Duke University, 1993
- B.S.E.E. Duke University, 1988
My lab investigates three areas in the advanced imaging of breast cancer: (1) digital mammography and breast tomosynthesis, (2) radiogenomics for improved management of breast cancer using computer vision and machine learning models, and (3) computational and physical breast phantoms to facilitate virtual clinical trials of new imaging technology.
Cancer diagnostics and therapy
- BIMP 301B: RESEARCH IN BIMP
- BME 394: Projects in Biomedical Engineering (GE)
- BME 493: Projects in Biomedical Engineering (GE)
- BME 494: Projects in Biomedical Engineering (GE)
- EGR 393: Research Projects in Engineering
- MEDPHY 751: Seminars in Medical Physics
- MEDPHY 791: Independent Study in Medical Physics
- RROMP 301B: RADIOLOGY, RADIATION ONCOLOGY & MEDICAL PHYSICS
- Sturgeon, GM; Kiarashi, N; Lo, JY; Samei, E; Segars, WP, Finite-element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation., Medical physics, vol 43 no. 5 (2016) [abs].
- 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, vol 43 no. 4 (2016) [abs].
- 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, vol 43 no. 1 (2016) [abs].
- Schmidt, M; Lo, JY; Grzetic, S; Lutzky, C; Brizel, DM; Das, SK, Semiautomated head-and-neck IMRT planning using dose warping and scaling to robustly adapt plans in a knowledge database containing potentially suboptimal plans., Medical physics, vol 42 no. 8 (2015), pp. 4428-4434 [abs].
- 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, vol 42 no. 7 (2015), pp. 4116-4126 [abs].
- Zhang, J; Grimm, LJ; Lo, JY; Johnson, KS; Ghate, SV; Walsh, R; Mazurowski, MA, Does Breast Imaging Experience During Residency Translate Into Improved Initial Performance in Digital Breast Tomosynthesis?, Journal of the American College of Radiology, vol 12 no. 7 (2015), pp. 728-732 [abs].
- Ikejimba, LC; Kiarashi, N; Ghate, SV; Samei, E; Lo, JY, Task-based strategy for optimized contrast enhanced breast imaging: Analysis of six imaging techniques for mammography and tomosynthesis, Medical physics, vol 41 no. 6 (2014), pp. 061908-061908 [10.1118/1.4873317] [abs].
- 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, vol 8668 (2013) [10.1117/12.2008406] [abs].
- Huo, Z; Summers, RM; Paquerault, S; Lo, J; Hoffmeister, J; III, SGA; Freedman, MT; Lin, J; Lo, SCB; Petrick, N; Sahiner, B; Fryd, D; Yoshida, H; Chan, HP, Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use, Medical physics, vol 40 no. 7 (2013) [10.1118/1.4807642] [abs].