Joseph Lo, Ph.D.

  • Professor and Associate Vice Chair for Research of Radiology
  • Professor of Biomedical Engineering and Electrical & Computer Engineering
  • Faculty of Medical Physics Graduate Program
  • Director of Carl E. Ravin Advanced Imaging Laboratories
Department/Division
  • Research
Address 2424 Erwin Road Suite 302
Durham, NC 27705
Telephone 919-684-7763
Training
  • PhD, Duke University, 1993
  • BSEE, Duke University, 1988
Research Interests 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 comparable dose as conventional mammography. Tomosynthesis may improve sensitivity of breast cancer diagnosis by helping radiologists to detect subtle lesions which would otherwise be obscured. In addition, tomosynthesis can also improve specificity since radiologists can better characterize benign cases and thus avoid unnecessary follow-up studies and 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. We have concluded an NIH-sponsored clinical trial that accrued nearly 400 subjects, and are now participating in a multi-center trial for Siemens to collect data for their FDA submission.

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 and head & neck cancer. Our goal is to improve the efficiency and safety of treatment plans. The idea is simple - to match a new patient against a large database of existing patients based on similarities in their CT data, and then to use the existing treatment parameters from those matching cases to develop a new treatment plan with acceptable clinical quality. We have developed a database of several hundred cases from Duke as well as two outside independent clinics, and preliminary results have been very promising.
  • Chen Y, Lo JY, Dobbins JT 3rd. Impulse response and Modulation Transfer Function analysis for Shift-And-Add and Back Projection image reconstruction algorithms in Digital Breast Tomosynthesis (DBT). Int J Funct Inform Personal Med. 2008;1(2):189-204.
    Abstract
  • Baker JA, Lo JY. Breast tomosynthesis: state-of-the-art and review of the literature. Acad Radiol. 2011 Oct;18(10):1298-310.
    Abstract
  • Chanyavanich V, Das SK, Lee WR, Lo JY. Knowledge-based IMRT treatment planning for prostate cancer. Med Phys. 2011 May;38(5):2515-22.
    Abstract
  • Webb LJ, Samei E, Lo JY, Baker JA, Ghate SV, Kim C, Soo MS, Walsh R. Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection. Med Phys. 2011 Apr;38(4):1972-80.
    Abstract
  • Mazurowski MA, Lo JY, Harrawood BP, Tourassi GD. Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis. J Biomed Inform. 2011 Oct;44(5):815-23.
    Abstract
  • Singh S, Maxwell J, Baker JA, Nicholas JL, Lo JY. Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents. Radiology. 2011 Jan;258(1):73-80.
    Abstract
  • Shafer CM, Samei E, Lo JY. The quantitative potential for breast tomosynthesis imaging. Med Phys. 2010 Mar;37(3):1004-16.
    Abstract
  • Ranger NT, Lo JY, Samei E. A technique optimization protocol and the potential for dose reduction in digital mammography. Med Phys. 2010 Mar;37(3):962-9.
    Abstract
  • Chawla AS, Lo JY, Baker JA, Samei E. Optimized image acquisition for breast tomosynthesis in projection and reconstruction space. Med Phys. 2009 Nov;36(11):4859-69.
    Abstract
  • Jesneck JL, Mukherjee S, Yurkovetsky Z, Clyde M, Marks JR, Lokshin AE, Lo JY. Do serum biomarkers really measure breast cancer? BMC Cancer. 2009 May 28;9:164.
    Abstract
  • Saunders RS Jr, Samei E, Lo JY, Baker JA. Can compression be reduced for breast tomosynthesis? Monte carlo study on mass and microcalcification conspicuity in tomosynthesis. Radiology. 2009 Jun;251(3):673-82.
    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
  • Williams MB, Raghunathan P, More MJ, Seibert JA, Kwan A, Lo JY, Samei E, Ranger NT, Fajardo LL, McGruder A, McGruder SM, Maidment AD, Yaffe MJ, Bloomquist A, Mawdsley GE. Optimization of exposure parameters in full field digital mammography. Med Phys. 2008 Jun;35(6):2414-23.
    Abstract
  • Xia JQ, Lo JY, Yang K, Floyd CE Jr, Boone JM. Dedicated breast computed tomography: volume image denoising via a partial-diffusion equation based technique. Med Phys. 2008 May;35(5):1950-8.
    Abstract