Texture enhanced optimization-based image reconstruction (TxE-OBIR) from sparse projection views

Huiqiao Xie; Tianye Niu; Yi Yang; Yi Ren; Xiangyang Tang

doi:10.1117/12.2216097

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29 March 2016 Texture enhanced optimization-based image reconstruction (TxE-OBIR) from sparse projection views

Huiqiao Xie, Tianye Niu, Yi Yang, Yi Ren, Xiangyang Tang

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Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97834R (2016) https://doi.org/10.1117/12.2216097
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

The optimization-based image reconstruction (OBIR) has been proposed and investigated in recent years to reduce radiation dose in X-ray computed tomography (CT) through acquiring sparse projection views. However, the OBIR usually generates images with a quite different noise texture compared to the clinical widely used reconstruction method (i.e. filtered back-projection – FBP). This may make the radiologists/physicians less confident while they are making clinical decisions. Recognizing the fact that the X-ray photon noise statistics is relatively uniform across the detector cells, which is enabled by beam forming devices (e.g. bowtie filters), we propose and evaluate a novel and practical texture enhancement method in this work. In the texture enhanced optimization-based image reconstruction (TxEOBIR), we first reconstruct a texture image with the FBP algorithm from a full set of synthesized projection views of noise. Then, the TxE-OBIR image is generated by adding the texture image into the OBIR reconstruction. As qualitatively confirmed by visual inspection and quantitatively by noise power spectrum (NPS) evaluation, the proposed method can produce images with textures that are visually identical to those of the gold standard FBP images.

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Huiqiao Xie, Tianye Niu, Yi Yang, Yi Ren, and Xiangyang Tang "Texture enhanced optimization-based image reconstruction (TxE-OBIR) from sparse projection views", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97834R (29 March 2016); https://doi.org/10.1117/12.2216097

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