A framework for flexible comparison and optimization of x-ray digital tomosynthesis

Frank Smith; Ying Chen

doi:10.1117/12.2511901

1 March 2019 A framework for flexible comparison and optimization of x-ray digital tomosynthesis

Frank Smith, Ying Chen

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109483Z (2019) https://doi.org/10.1117/12.2511901
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

In this paper, we developed a framework for comparison and optimization of x-ray imaging configurations for x-ray digital tomosynthesis. Digital tomosynthesis is a novel technology to reconstruct three-dimensional information with limited number of low-dose two-dimensional projection images. Breast cancer is the most common cancer among American women. Early breast cancer detection is the best hope to decrease breast cancer mortality. Breast cancer is sometimes found after symptoms appear. Many women with breast cancer have no symptoms. Mammography has long been the leading technology for breast cancer detection. Although mammography has been the primary technology for breast cancer detection, digital tomosynthesis has become increasingly popular for breast cancer detection. In digital breast tomosynthesis imaging fields, most current breast tomosynthesis systems utilize a design where a single x-ray tube moves along an arc above objects over a certain angular range. Parallel imaging configurations is utilized in a few tomosynthesis imaging area such as digital chest tomosynthesis, and multi-beam stationary breast tomosynthesis imaging field as well. In this paper we present the preliminary investigation on computational analysis of impulse response and wire simulation characterization for optimization of digital tomosynthesis imaging configurations using our framework.

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Frank Smith and Ying Chen "A framework for flexible comparison and optimization of x-ray digital tomosynthesis", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109483Z (1 March 2019); https://doi.org/10.1117/12.2511901

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KEYWORDS

Reconstruction algorithms

Computer simulations

Expectation maximization algorithms

X-rays

Breast cancer

Imaging systems

Sensors

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