Among American women breast cancer is the most common cancer and the 2nd leading cause of death from cancer. Breast density, which is defined as the ratio of fibroglandular tissue to the total fibroglandular and adipose tissue, is an important risk factor in the development of breast cancer. A mammographically dense breast refers to a breast which attenuates a greater proportion of x-ray photons due to its significant volume fraction of fibroglandular tissue relative to adipose tissue. There are currently no accepted standards to reliably measure breast density. Subjective visual assessment alone is inadequate to quantify breast density. Other more quantitative attempts have suffered from a number of limitations such as providing only an area as opposed to volumetric measure of breast density or assuming a constant breast thickness. Previous studies have shown that the relative risk for developing breast cancer increases by 2% for every 1% increase in mammographic density, indicating the need for an accurate method to measure breast density. The goal of this proposal is to develop a method to objectively and quantitatively measure the density of a woman's breast using dual energy mammography. Dual energy imaging exploits the differential energy dependence in x-ray photon attenuation between fibroglandular and adipose tissue. Image pixels are decomposed into separate thickness measurements for each of the two primary breast tissues.
The specific aims are: (1) Development of an analytical simulation model to investigate the feasibility of accurate breast density measurements using dual energy mammography. (2) Investigation of the hypothesis that breast density can be accurately measured in breast phantoms using dual energy mammography. (3) Validation of the dual energy technique for breast density quantification with magnetic resonance imaging (MRI) and cone-beam computed tomography in postmortem breast tissue. (4) To test the hypothesis that breast density measured in human volunteers with dual energy mammography and breast density measured with MRI have a correlation coefficient higher than 0.95. The proposed method provides a volumetric measure of breast density on a pixel by pixel basis and it has the potential to be incorporated into routine screening mammography. These advantages should translate into a more accurate assessment of breast cancer risk. The long-range plan for this project is to develop a clinically feasible method to measure the density of a woman's breast with dual energy x-ray mammography.
Breast density has been identified as an important yet underutilized risk factor in the development of breast cancer. Subjective visual assessment alone is inadequate to quantify breast density. The goal of this proposal is to develop a method to objectively and quantitatively measure the density of a woman's breast using dual energy mammography. The proposed method provides a volumetric measure of breast density and it has the potential to be incorporated into routine screening mammography.
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|Liu, Jiulong; Ding, Huanjun; Molloi, Sabee et al. (2016) TICMR: Total Image Constrained Material Reconstruction via Nonlocal Total Variation Regularization for Spectral CT. IEEE Trans Med Imaging 35:2578-2586|
|Molloi, Sabee; Ding, Huanjun; Feig, Stephen (2015) Breast density evaluation using spectral mammography, radiologist reader assessment, and segmentation techniques: a retrospective study based on left and right breast comparison. Acad Radiol 22:1052-9|
|Ding, Huanjun; Gao, Hao; Zhao, Bo et al. (2014) A high-resolution photon-counting breast CT system with tensor-framelet based iterative image reconstruction for radiation dose reduction. Phys Med Biol 59:6005-17|
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|Johnson, Travis; Ding, Huanjun; Le, Huy Q et al. (2013) Breast density quantification with cone-beam CT: a post-mortem study. Phys Med Biol 58:8573-91|
|Ding, Huanjun; Molloi, Sabee (2012) Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study. Phys Med Biol 57:4719-38|
|Zhao, Bo; Ding, Huanjun; Lu, Yang et al. (2012) Dual-dictionary learning-based iterative image reconstruction for spectral computed tomography application. Phys Med Biol 57:8217-29|
|Le, Huy Q; Molloi, Sabee (2011) Segmentation and quantification of materials with energy discriminating computed tomography: a phantom study. Med Phys 38:228-37|
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