RAI Labs Duke
Joseph Lo, PhD
Associate Professor in the Department of Radiology
Contact Information
Department: Radiology
Division: Radiology - General
Address: Carl E. Ravin Advanced Imaging Laboratories
2424 Erwin Rd, Ste 302
Durham, NC 27705
Office Phone: (919) 684-7763  
Fax: (919) 684-1491
Email: joseph.lo@duke.edu
Web: http://railabs.duhs.duke.edu/~jyl
Research Interests
Last Modified: July 23, 2009

The lab focuses on the diagnosis and treatment of cancer using advanced imaging techniques. There are 3 main projects: breast tomosynthesis, computer aided diagnosis, and improved treatment planning for radiation therapy.

First, while mammography remains the gold standard in breast cancer screening, it has many well known limitations. Dr. Lo leads a team from the Ravin Advanced Imaging Laboratories (see website above) which collaborates closely with Siemens Healthcare to develop breast tomosynthesis, a form of limited-angle tomography using a modified digital mammography system. Tomosynthesis can acquire a 3D image quickly, easily, and at the same dose as a conventional mammogram. Tomosynthesis will improve sensitivity of breast cancer diagnosis by helping radiologists to detect subtle lesions which would otherwise be obscured. In addition, tomosynthesis will also improve specificity since radiologists can better characterize benign cases and thus avoid unnecessary follow-up imaging studies and surgical procedures. For these reasons, tomosynthesis is the most exciting recent development in breast imaging, and the only technology that can actually replace mammography in the near future. Duke is now conducting clinical trials using the first ever Siemens breast tomosynthesis prototype.

Second, for over a decade, we have been a leader in computer aided diagnosis (CAD), which is an interdisciplinary field combining elements of medical physics, engineering, statistics, and bioinformatics. We have developed automated detection algorithms which use computer vision techniques to localize suspicious mammographic lesions. We have also designed predictive models which use machine learning and statistical analysis in order to classify mammograms or sonograms as benign versus malignant. During these studies, we compiled one of the largest multi-institution breast cancer databases with approximately 5000 cases.

Finally, we are extending CAD techniques from radiology toward the problem of intensity modulated radiation therapy (IMRT), specifically to improve treatment planning for prostate cancer. Our goal is to improve the efficiency and safety of treatment plans.

Publications Representative Publications | All Publications
Last Modified: July 3, 2009

Jesneck JL, Mukherjee S, Yurkovetsky Z, Clyde M, Marks JR, Lokshin AE, Lo JY. Do serum biomarkers really measure breast cancer? BMC Cancer. 2009;9:164. Abstract

Chawla AS, Samei E, Saunders RS, Lo JY, Baker JA. A mathematical model platform for optimizing a multiprojection breast imaging system. Med Phys. 2008 Apr;35(4):1337-45. Abstract

Karellas A, Lo JY, Orton CG. Point/Counterpoint. Cone beam x-ray CT will be superior to digital x-ray tomosynthesis in imaging the breast and delineating cancer. Med Phys. 2008 Feb;35(2):409-11. Abstract

Singh S, Tourassi GD, Baker JA, Samei E, Lo JY. Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach. Med Phys. 2008 Aug;35(8):3626-36. Abstract

Jesneck JL, Nolte LW, Baker JA, Floyd CE, Lo JY. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Med Phys. 2006 Aug;33(8):2945-54. Abstract