Biographic Information
Education:
1991, Ph.D., Biomedical Engineering, Worcester Polytechnic Institute,
1988, M.S., Biomedical Engineering, Worcester Polytechnic Institute,
1982, B.S., Electrical Engineering, University of Vermont
Professional Experience:
2001-present, Associate Professor, Department of Radiology, University of Massachusetts Medical School, Worcester, MA
1997-2001, Associate Professor, Department of Nuclear Medicine, University of Massachusetts Medical School, Worceseter, MA
1991-1997, Assistant Professor, Department of Nuclear Medicine, University of Massachusetts Medical School, Worcester, MA.
1986-1991, Research Associate, Department of Nuclear Medicine, University of Massachusetts Medical School, Worcester, MA.
1985-86, Research Assistant, Department of Surgery, University of Massachusetts Medical Center, Worcester, MA.
Current Research Interests
Positron Emission Tomographic (PET) Imaging Systems
- Monte Carlo modeling of PET systems
- Optimization of PET systems
- 3D tomographic reconstruction methods for PET
- Time-of-flight PET
- Evaluation of image quality
Volumetric X-ray Imaging of the Breast
- Flat-panel, cone-beam CT breast imaging
- Breast tomosynthesis
- Optimization of breast imaging systems
Current Grant Funding
Title: "Feasibility of CT Mammography Using Flat-Panel Detectors" – NIH/NIBIB – EB02133 ,
Detection of lesions in planar mammograms is a difficult task, predominantly due to the masking effect of superimposed parenchymal breast patterns. Tomographic imaging can provide the radiologist with image slices through the three-dimensional (3D) breast, possibly reducing this masking effect. The goal of the proposed research is to investigate the feasibility of using an amorphous silicon, flat-panel imager for volumetric computed tomography (CT) of the breast. Our hypothesis is that dedicated CT mammography using state-of-the-art digital detectors can provide high quality images and three-dimensional visualization of breast tissue, with a radiation dose approximately equivalent to that given in screening mammography. We propose to investigate the characteristics of such a system by integrating a commercial prototype, flat-panel imager, with an optical bench plate containing precision rotational and translational stages. This would allow the acquisition of projection images by rotating phantoms in angular steps over 360o. We also propose to theoretically investigate optimal CT mammography system configurations using mathematical models of signal and noise propagation through the flat-panel detector, and realistic models of the lesion detection task in breast imaging. Design and acquisition parameters such as tomographic sampling requirements, imaging geometry, x-ray converter characteristics, and x-ray energy spectrum incident on the breast will be investigated. Previous reports have suggested great potential for tomographic breast imaging. To evaluate improvements in tomographic mammography, if any, we plan to compare lesion detection accuracy using human observer studies and simulated images generated with planar mammography, tomosynthesis, and CT mammography. An important component of these observer studies will be the use of realistic models for lesions and breast tissue. These models will be determined based on the statistical characterization of surgically removed lesion and breast tissue
Title: "Iterative Reconstruction for Breast Tomosynthesis" NIH/NCI - CA102758
The detection of lesions in conventional mammography is a difficult task, predominantly due to the masking effect of superimposed parenchymal breast patterns. Limited angle, tomographic mammography, also referred to as breast tomosynthesis, is a technique that has been proposed to reduce this masking effect, by providing the radiologist with tomographic image slices through the breast. The goal of the proposed research is to investigate the use of statistically based iterative reconstruction (IR) methods for breast tomosynthesis. Statistical IR methods have a number of potential advantages over some previously proposed tomosynthesis methods including; 1) a more accurate modeling of the noise in the data, 2) the capability for modeling the physics of x-ray transport, thus providing an integrated approach for compensation of scatter and detector blur, and 3) the capability of incorporating a priori information on the object to be reconstructed. Our hypothesis is that breast tomosynthesis using statistical IR methods can provide improved detection of malignant lesions as compared to backprojection tomosynthesis, as well as to conventional two-view digital mammography. To test this hypothesis, human observer psychophysical studies will be performed comparing conventional two-view digital mammography and tomosynthesis. We also propose to investigate a number of issues related to the acquisition process of breast tomosynthesis including; 1) alternative acquisition geometries, 2) the impact of varying levels of breast compression, 3) the impact of scatter, and 4) the optimal anti-scatter grid. Evaluation and optimization of different imaging system designs and acquisition processes will be conducted by evaluating lesion detection accuracy using realistically simulated tomosynthesis breast images.
