3D Breast Imaging
Although conventional mammography has been very successful in reducing breast cancer mortality, 20–26% of cancers are still missed and approximately 70% of biopsies performed to evaluate suspicious breast lesions turn out to be unnecessary. One of the limiting problems with mammography is that the recorded image represents the 3D breast superimposed onto a 2D image plane, with normal anatomical breast structure overlapping with important diagnostic information. Two 3D breast imaging methods, digital breast tomosynthesis (DBT) and dedicated breast CT (BCT), are new emerging technologies that allow for 3D visualization of breast and have shown great promise for improving the detection and diagnosis of early-stage breast cancer.
Compressible breast phantom under development for use in studying the effect of breast compression on image quality.
A benchtop system developed to emulate varying digital breast tomosynthesis geometries.
Methods for the evaluation of image quality and radiation dose in mammography are well established; however, not enough research has been performed on developing comparable methods for objectively assessing image quality of 3D breast imaging methods. Such methods that are being developed in this project will facilitate the pre-market review of 3D breast imaging systems, and are also important for expediting the review of device modifications by minimizing the need for additional clinical data. In the future, these methods could provide the technical basis for reducing the amount of clinical data needed for approval of these devices. Such evaluation methods are also needed for the routine QC/QA evaluation of these devices (as required by the MQSA program). Our group is exploring the development of both in silico modeling methods for conducting virtual clinical trials, as well as experimental methods for objectively assessing image quality of DBT and BCT systems. The group is experimenting with anthropomorphic 3D breast phantoms, and using these phantoms to conduct analysis on CDRH/DIDSR benchtop systems, as well as an experimental BCT system. Using in silico and phantom measurements, the group is developing approaches for task-based performance assessment of DBT and BCT systems that can be used as alternatives to clinical trials. The group is using these methods to evaluate optimal acquisition strategies, develop standards for selecting the optimal radiation dose and to explore advantages of statistical based iterative reconstruction algorithms.
The group is also investigating new detector technologies to improve 3D breast imaging. In particular, we are evaluating the feasibility of photon counting detectors for breast CT.
Current funding sources
Stephen Glick, Ph.D.
Robert J Jennings, Ph.D.
Andrey Makeev, Ph.D.
Subok Park, Ph.D.
Rongping Zeng, Ph.D.
Lynda Okejimba, Ph.D.
University of Massachusetts Medical School
CT benchtop: Varian 4030 CsI based flat-panel detector, Varian Rad71 x-ray tube, linear and rotary stages
DBT benchtop: Anrad a-Se based flat-panel detector, Thales Pixium 4343 flat-panel detector, DECTRIS Pilatus 300, Varian RAD71SP x-ray tube, three rotary and four linear stages with stepper motors
Selected peer-review publications
- Zeng et al., Evaluating the sensitivity of the optimization of acquisition geometry to the choice of reconstruction algorithm in digital breast tomosynthesis through a simulation study, Phys Med Biol,, 2015.
- Makeev et al., Evaluation of position-estimation methods applied to CZT-based photon-counting detectors for dedicated breast CT, J Med Imag, 2015.
- Park, Spatial-domain model observers for optimizing tomosynthesis, Tomosynthesis Imaging, 2014.
- Young et al., A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data, Med Phys, 2013.
- O'Connor et al., Generation of voxelized breast phantoms from surgical mastectomy specimens, Med Phys, 2013.
- Makeev et al., Investigation of statistical iterative reconstruction for dedicated breast CT, Med Phys, 2013.
- Kalluri et al., Investigation of energy weighting using an energy discriminating photon counting detector for breast CT, Med Phys, 2013.