Lag-Net: Lag correction for cone-beam CT via a convolutional neural network

Abstract

Background and objective:

Due to the presence of charge traps in amorphous silicon flat-panel detectors, lag signals are generated in consecutively captured projections. These signals lead to ghosting in projection images and severe lag artifacts in cone-beam computed tomography (CBCT) reconstructions. Traditional Linear Time-Invariant (LTI) correction need to measure lag correction factors (LCF) and may leave residual lag artifacts. This incomplete correction is partly attributed to the lack of consideration for exposure dependency.

Methods:

To measure the lag signals more accurately and suppress lag artifacts, we develop a novel hardware correction method. This method requires two scans of the same object, with adjustments to the operating timing of the CT instrumentation during the second scan to measure the lag signal from the first. While this hardware correction significantly mitigates lag artifacts, it is complex to implement and imposes high demands on the CT instrumentation. To enhance the process, We introduce a deep learning method called Lag-Net to remove lag signal, utilizing the nearly lag-free results from hardware correction as training targets for the network.

Results:

Qualitative and quantitative analyses of experimental results on both simulated and real datasets demonstrate that deep learning correction significantly outperforms traditional LTI correction in terms of lag artifact suppression and image quality enhancement. Furthermore, the deep learning method achieves reconstruction results comparable to those obtained from hardware correction while avoiding the operational complexities associated with the hardware correction approach.

Conclusion:

The proposed hardware correction method, despite its operational complexity, demonstrates superior artifact suppression performance compared to the LTI algorithm, particularly under low-exposure conditions. The introduced Lag-Net, which utilizes the results of the hardware correction method as training targets, leverages the end-to-end nature of deep learning to circumvent the intricate operational drawbacks associated with hardware correction. Furthermore, the network’s correction efficacy surpasses that of the LTI algorithm in low-exposure scenarios.