One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging.

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Abstract

Sparse-view dual-energy spectral computed tomography (DECT) imaging
is a challenging inverse problem. Due to the incompleteness of the collected data,
the presence of streak artifacts can result in the degradation of reconstructed spectral
images. The subsequent material decomposition task in DECT can further lead to
the amplification of artifacts and noise.To address this problem, we
propose a novel one-step inverse generation network (OIGN) for sparse-view dual-
energy CT imaging, which can achieve simultaneous imaging of spectral images and
materials. The entire OIGN consists of five sub-networks that form four modules,
including the pre-reconstruction module, the pre-decomposition module, and the
following residual filtering module and residual decomposition module. The residual
feedback mechanism is introduced to synchronize the optimization of spectral CT
images and materials.Numerical simulation experiments show that the
OIGN has better performance on both reconstruction and material decomposition than
other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high
imaging efficiency by completing two high-quality imaging tasks in just 50 seconds.
Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN.
Significance. These findings have great potential in high-quality multi-task spectral
CT imaging in clinical diagnosis.© 2024 Institute of Physics and Engineering in Medicine.