Contact Info:

Office Location:	Ravin Advanced Imaging Labs (RAI Labs) Hock Plaza
Email Address:
Web Page:	http://railabs.duhs.duke.edu/~jyl/ Research Home Page for Joseph Lo

Teaching (Fall 2009):

• MEDPHY 251.02, SEMINARS IN MEDICAL PHYSICS

  Hock Plaza AUDITORIUM, Th 02:50 PM-04:05 PM

• BIMP 301B.16, RESEARCH IN BIMP

• BIMP 301B.16-S, RESEARCH IN BIMP

• BIMP 301B.16, RESEARCH IN BIMP

• BIMP 301B.16-S, RESEARCH IN BIMP

Education:

Ph.D., Duke University, 1993

Specialties:

Medical Imaging
Cancer diagnostics and therapy

Research Interests: Breast cancer imaging and diagnosis

Current projects: 3D Tomosynthesis Imaging of the Breast, Computer-aided Detection/Diagnosis of Breast Cancer, Optimization of Radiation Therapy

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.

Areas of Interest:

Breast imaging
Breast tomosynthesis
computed tomography
computer-aided diagnosis
radiation therapy

Curriculum Vitae

Representative Publications   (More Publications)   (search)

1. JL Jesneck, S Mukherjee, Z Yurkovetsky, M Clyde, JR Marks, AE Lokshin, JY Lo, Do serum biomarkers really measure breast cancer?, BMC cancer, England, vol. 9 (2009), pp. 164  [abs].
2. S Singh, GD Tourassi, JA Baker, E Samei, JY Lo, Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach., Medical physics, United States, vol. 35 no. 8 (August, 2008), pp. 3626-36  [abs].
3. CE Floyd, AJ Kapadia, JE Bender, AC Sharma, JQ Xia, BP Harrawood, GD Tourassi, JY Lo, AS Crowell, MR Kiser, CR Howell, Neutron-stimulated emission computed tomography of a multi-element phantom., Physics in medicine and biology, England, vol. 53 no. 9 (May, 2008), pp. 2313-26  [abs].
4. AS Chawla, E Samei, RS Saunders, JY Lo, JA Baker, A mathematical model platform for optimizing a multiprojection breast imaging system., Medical physics, United States, vol. 35 no. 4 (April, 2008), pp. 1337-45  [abs].
5. A Karellas, JY Lo, CG Orton, Point/Counterpoint. Cone beam x-ray CT will be superior to digital x-ray tomosynthesis in imaging the breast and delineating cancer., Medical physics, United States, vol. 35 no. 2 (February, 2008), pp. 409-11 .
6. JL Jesneck, LW Nolte, JA Baker, CE Floyd, JY Lo, Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis., Medical physics, United States, vol. 33 no. 8 (August, 2006), pp. 2945-54  [abs].
7. AO Bilska-Wolak, CE Floyd, JY Lo, JA Baker, Computer aid for decision to biopsy breast masses on mammography: validation on new cases., Academic radiology, United States, vol. 12 no. 6 (June, 2005), pp. 671-80  [abs].
8. JA Baker, EL Rosen, MM Crockett, JY Lo, Accuracy of segmentation of a commercial computer-aided detection system for mammography., Radiology, United States, vol. 235 no. 2 (May, 2005), pp. 385-90  [abs].
9. RS Saunders, E Samei, JL Jesneck, JY Lo, Physical characterization of a prototype selenium-based full field digital mammography detector., Medical physics, United States, vol. 32 no. 2 (February, 2005), pp. 588-99  [abs].
10. MK Markey, JY Lo, GD Tourassi, CE Floyd, Self-organizing map for cluster analysis of a breast cancer database., Artificial intelligence in medicine, Netherlands, vol. 27 no. 2 (February, 2003), pp. 113-27  [abs].
11. MA Gavrielides, JY Lo, CE Floyd, Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms., Medical physics, United States, vol. 29 no. 4 (April, 2002), pp. 475-83  [abs].
12. JY Lo, MK Markey, JA Baker, CE Floyd, Cross-institutional evaluation of BI-RADS predictive model for mammographic diagnosis of breast cancer., AJR. American journal of roentgenology, United States, vol. 178 no. 2 (February, 2002), pp. 457-63  [abs].