Refined locally linear transform based spectral-domain gradient sparsity and its applications in spectral CT reconstruction

Qian Wang; Morteza Salehjahromi; Hengyong Yu

doi:10.1117/12.2508064

1 March 2019 Refined locally linear transform based spectral-domain gradient sparsity and its applications in spectral CT reconstruction

Qian Wang, Morteza Salehjahromi, Hengyong Yu

Author Affiliations +

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109482L (2019) https://doi.org/10.1117/12.2508064
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

By extending the conventional single-energy computed tomography (SECT) along the energy dimension, spectral CT achieves superior energy resolution and material distinguishability. However, for the state-of-the-art photon counting detector (PCD) based spectral CT, because the emitted photons with a fixed total number for one X-ray beam are divided into several different energy bins, the noise level is increased in each reconstructed channel image, and it further leads to an inaccurate material decomposition. To improve the reconstruction quality and decomposition accuracy, in this work, we first employ a refined locally linear transform to convert the structural similarity among two-dimensional (2D) spectral CT images to a spectral-dimension gradient sparsity. By combining the gradient sparsity in the spatial domain, a global three-dimensional (3D) gradient sparse representation is constructed and measured by L₁, L₀- and trace-norm, respectively. For each sparsity measurement, we propose the corresponding optimization model, develop the corresponding iterative algorithm, and verify the effectiveness and superiority with both simulated and real datasets.

Citation Download Citation

Qian Wang, Morteza Salehjahromi, and Hengyong Yu "Refined locally linear transform based spectral-domain gradient sparsity and its applications in spectral CT reconstruction", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109482L (1 March 2019); https://doi.org/10.1117/12.2508064

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