A generalized supervised training approach for adaptive spectral harmonization in photon-counting CT

Wenying Wang; Xi Zhang; Wenda Zhang; Xiaojuan Liu; Jinglu Ma; Guotao Quan

doi:10.1117/12.3006889

3 April 2024 A generalized supervised training approach for adaptive spectral harmonization in photon-counting CT

Wenying Wang, Xi Zhang, Wenda Zhang, Xiaojuan Liu, Jinglu Ma, Guotao Quan

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129252X (2024) https://doi.org/10.1117/12.3006889
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Photon-counting CT (PCCT) has the potential to provide superior spectral separation and improve material decomposition accuracy. However, PCDs face challenges of pixel-level inhomogeneity and instability, necessitating frequent spectral calibration and specialized processing adaptive to different spectral responses. In this work, we introduce a generalized supervised training method for adaptive spectral harmonization in PCCT. A multi-layer perceptron (MLP) network is trained to achieve material decomposition regardless of individual PCD pixel responses, resulting in artifact-free images and accurate material quantification, as validated in simulations and experiments.

Citation Download Citation

Wenying Wang, Xi Zhang, Wenda Zhang, Xiaojuan Liu, Jinglu Ma, and Guotao Quan "A generalized supervised training approach for adaptive spectral harmonization in photon-counting CT", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129252X (3 April 2024); https://doi.org/10.1117/12.3006889

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