MMD-Net: multi-material decomposition network for high quality dual-energy CT imaging

Zisheng Li; Jiongtao Zhu; Ting Su; Dong Liang; Yongshuai Ge

doi:10.1117/12.2654158

7 April 2023 MMD-Net: multi-material decomposition network for high quality dual-energy CT imaging

Zisheng Li, Jiongtao Zhu, Ting Su, Dong Liang, Yongshuai Ge

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 1246334 (2023) https://doi.org/10.1117/12.2654158
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Dual-energy computed tomography (DECT) is a promising imaging modality. It has the potential to quantify different material densities and plays an important role in many clinical applications. To enable multiple material decomposition (MMD), the conventional analytical MMD algorithm assumes the presence of at most three materials in each image pixel, and each pixel is decomposed into a certain basis material triplet. However, the MMD algorithm requires strong prior knowledge of the mixture composition, and the decomposition performance is compromised around the boundaries of different compositions. In this work, we developed an analytical model based deep neural network MMD-Net to achieve multi-material decomposition in DECT. In particular, the type of the basis material triplet in each image pixel and the attenuation coefficients of each material are learned by dedicated convolution neural network modules, and the material-specific density maps are obtained from the analytical MMD algorithm. Physical experiments of a pig leg and a pork backbone specimen with inserted iodine concentrations were acquired to evaluate the performance of the MMD-Net. Results show that the proposed MMD-Net could provide high decomposition accuracy, and reduce the decomposition artifacts.

Citation Download Citation

Zisheng Li, Jiongtao Zhu, Ting Su, Dong Liang, and Yongshuai Ge "MMD-Net: multi-material decomposition network for high quality dual-energy CT imaging", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 1246334 (7 April 2023); https://doi.org/10.1117/12.2654158

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