Material decomposition in photon-counting-detector CT: threshold or bin images?

Liqiang Ren; Shengzhen Tao; Cynthia H. McCollough; Lifeng Yu

doi:10.1117/12.2513463

1 March 2019 Material decomposition in photon-counting-detector CT: threshold or bin images?

Liqiang Ren, Shengzhen Tao, Cynthia H. McCollough, Lifeng Yu

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109482S (2019) https://doi.org/10.1117/12.2513463
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Energy-resolved photon-counting-detector CT (PCD-CT) is promising for material-specific imaging of multiple contrast agents. In each PCD-CT scan, two groups of images can be reconstructed, namely threshold images and bin images, and both can be directly used for material decomposition. The performance may differ for different energy thresholds and imaging tasks and it remains unclear which group of images should be used. The purpose of this work is to evaluate the imaging performance of threshold images and bin images when they are used for a three-material decomposition task (iodine, gadolinium, and water) in PCD-CT. Material decomposition was performed in image-space by using both an ordinary least squares (OLS) method and a generalized least squares (GLS) method. Both numerical analysis and phantom experiments were conducted, which demonstrated that: 1) compared with OLS, GLS provided improved noise properties using either threshold or bin images; 2) for the GLS method, when the covariances among images are taken into account, threshold and bin images showed almost identical material-specific imaging performance. This work suggested that, when correlations among images are incorporated into material decomposition, threshold and bin images perform equivalently well.

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Liqiang Ren, Shengzhen Tao, Cynthia H. McCollough, and Lifeng Yu "Material decomposition in photon-counting-detector CT: threshold or bin images?", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109482S (1 March 2019); https://doi.org/10.1117/12.2513463

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KEYWORDS

Iodine

X-ray computed tomography

Gadolinium

Computed tomography

Medical physics

X-rays

Image processing

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