Estimating scatter from sparsely measured primary signal

Gongting Wu; Christina R. Inscoe; Jabari Calliste; Jing Shan; Yueh Z. Lee; Otto Zhou; Jianping Lu

doi:10.1117/1.JMI.4.1.013508

29 March 2017 Estimating scatter from sparsely measured primary signal

Gongting Wu, Christina R. Inscoe, Jabari Calliste, Jing Shan, Yueh Z. Lee, Otto Zhou, Jianping Lu

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 1, 013508 (March 2017). https://doi.org/10.1117/1.JMI.4.1.013508

Abstract

Scatter radiation severely degrades the image quality. Measurement-based scatter correction methods sample the scatter signal at sparsely distributed points, from which the scatter profile is estimated and deterministically removed from the projection image. The estimation of the scatter profile is generally done through a spline interpolation and the resulting scatter profile is quite smooth. Consequently, the noise is intact and the signal-to-noise ratio is reduced in the projection image after scatter correction, leading to image artifacts and increased noise in the reconstruction images. We propose a simple and effective method, referred to as filtered scatter-to-primary ratio (f-SPR) estimation, to estimate the scatter profile using the sparsely sampled scatter signal. Using the primary sampling device and the stationary digital tomosynthesis systems previously developed in our lab, we evaluated and compared the f-SPR method in comparison with existing methods in terms of contrast ratio, signal difference-to-noise ratio, and modulation transfer function. A significant improvement in image quality is observed in both the projection and the reconstruction images using the proposed method.

Citation Download Citation

Gongting Wu, Christina R. Inscoe, Jabari Calliste, Jing Shan, Yueh Z. Lee, Otto Zhou, and Jianping Lu "Estimating scatter from sparsely measured primary signal," Journal of Medical Imaging 4(1), 013508 (29 March 2017). https://doi.org/10.1117/1.JMI.4.1.013508

Received: 19 June 2016; Accepted: 13 March 2017; Published: 29 March 2017

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