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19 March 2008 Toward quantification of breast tomosynthesis imaging
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Due to the high prevalence of breast cancer among women, much is being done to detect breast cancer earlier and more accurately. In current clinical practice, the most widely-used mode of breast imaging is mammography. Its main advantages are high sensitivity and low patient dose, although it is still merely a two-dimensional projection of a three-dimensional object. In digital breast tomosynthesis, a three-dimensional image of the breast can be reconstructed, but x-ray projection images of the breast are taken over a limited angular span. However, the breast tomosynthesis device itself is more similar to a digital mammography system and thus is a feasible replacement for mammography. Because of the angular undersampling in breast tomosynthesis, the reconstructed images are not considered quantitative, so a worthwhile question to answer would be whether the voxel values (VVs) in breast tomosynthesis images can be made to indicate tissue type as Hounsfield units do in CT. through some image processing scheme. To investigate this, simple phantoms were imaged consisting of layers of uniform, tissue-equivalent plastic for the background sandwiching a layer of interest containing multiple, small cuboids of tissue-equivalent plastic. After analyzing the reconstructed tomosynthesis images, it was found that the VV in each lesion increases linearly with tissue glandularity. However, for the two different x-ray tube energies and for the two different beam exposure levels tested, the trend-lines all have different slopes and y-intercepts. Thus, breast tomosynthesis has a definite potential to be quantitative, and it would be worthwhile to study other possible dependent parameters (phantom thickness, overall density, etc.) as well as alternative reconstruction algorithms.
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Christina M. Shafer, Ehsan Samei, Robert S. Saunders, Moustafa Zerhouni, and Joseph Y. Lo "Toward quantification of breast tomosynthesis imaging", Proc. SPIE 6913, Medical Imaging 2008: Physics of Medical Imaging, 69134N (19 March 2008);

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