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Session: Poster session I
Session starts: Thursday 24 January, 15:00

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N. Özmen-Eryilmaz ()
Nicole Ruiter ()
Koen van Dongen ()

Abstract:
Breast cancer is the most frequently diagnosed cancer and the leading cause of death for women worldwide. Fortunately, death rates are decreasing as a result of early detection via screening large populations and improved treatment. Today, mammography is the most common technique for breast cancer examination. However, it can miss cancers for women with dense breasts. The main reason is that both dense tissue and cancerous lumps show up white on mammograms, making it difficult to differentiate between them. Breast ultrasound is gaining interest as an alternative to mammography due to its potential to detect cancer in dense breasts. In addition, it is safe, fast and cost-effective. Recently, researchers have been working on building fully automated three-dimensional breast ultrasound scanning systems [1] and new ultrasound imaging methods. In principle, breast imaging is a nonlinear inverse problem. However, the problem is often linearized by applying approximations such as the Born approximation. Unfortunately, as a result of these approximations, breast images get blurred and show incorrect reconstructions. Our research project aims to develop advanced ultrasound imaging techniques that allow for accurate three-dimensional reconstruction of the breast in terms of acoustic medium parameters, i.e., speed of sound and attenuation. We overcome the problems coming along with the linearization by going beyond the Born approximation, and solve the actual nonlinear inverse problem using a Contrast Source Inversion (CSI) method [2]. In this conjugate gradient based numerical method, a cost functional is minimized iteratively and contrast sources and contrast functions are updated, simultaneously. Results using synthetic measured data show that our method indeed yields sharper reconstructions as compared to the common applied methods. Moreover, it provides accurate speed of sound profiles indicating the location and dimension of a tumour. REFERENCES [1] N.V. Ruiter, G.F. Schwarzenberg, M. Zapf, R. Liu, R. Stotzka and H. Gemmeke, “3D Ultrasound Computer Tomography: Results with a Clinical Breast Phantom”, IEEE Ultrasonics Symposium, (2006). [2] P.M. van den Berg and R.E. Kleinman, “A contrast source inversion method”, Inverse Problems, Vol. 13, pp. 1607–1620, (1997).