Feasibility of generating quantitative composition images in dual energy mammography: a simulation study

Donghoon Lee; Ye-seul Kim; Sunghoon Choi; Haenghwa Lee; Seungyeon Choi; Hee-Joung Kim

doi:10.1117/12.2216577

22 March 2016 Feasibility of generating quantitative composition images in dual energy mammography: a simulation study

Donghoon Lee, Ye-seul Kim, Sunghoon Choi, Haenghwa Lee, Seungyeon Choi, Hee-Joung Kim

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97832I (2016) https://doi.org/10.1117/12.2216577
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Breast cancer is one of the most common malignancies in women. For years, mammography has been used as the gold standard for localizing breast cancer, despite its limitation in determining cancer composition. Therefore, the purpose of this simulation study is to confirm the feasibility of obtaining tumor composition using dual energy digital mammography. To generate X-ray sources for dual energy mammography, 26 kVp and 39 kVp voltages were generated for low and high energy beams, respectively. Additionally, the energy subtraction and inverse mapping functions were applied to provide compositional images. The resultant images showed that the breast composition obtained by the inverse mapping function with cubic fitting achieved the highest accuracy and least noise. Furthermore, breast density analysis with cubic fitting showed less than 10% error compare to true values. In conclusion, this study demonstrated the feasibility of creating individual compositional images and capability of analyzing breast density effectively.

Citation Download Citation

Donghoon Lee, Ye-seul Kim, Sunghoon Choi, Haenghwa Lee, Seungyeon Choi, and Hee-Joung Kim "Feasibility of generating quantitative composition images in dual energy mammography: a simulation study", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97832I (22 March 2016); https://doi.org/10.1117/12.2216577

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KEYWORDS

Breast

Dual energy imaging

Mammography

Breast cancer

Proteins

Error analysis

Tissues

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