Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks
This incremental improvement addresses robustness in X-ray sparse-view and phase tomography for applications like material characterization and dynamic imaging.
The paper tackles the problem of overfitting in CNN-based X-ray CT reconstruction by introducing a hierarchical synthesis approach that separates training data into bands to isolate sampling biases, achieving comparable or improved performance with reduced network complexity and computational cost.
Convolutional Neural Networks (CNN) based image reconstruction methods have been intensely used for X-ray computed tomography (CT) reconstruction applications. Despite great success, good performance of this data-based approach critically relies on a representative big training data set and a dense convoluted deep network. The indiscriminating convolution connections over all dense layers could be prone to over-fitting, where sampling biases are wrongly integrated as features for the reconstruction. In this paper, we report a robust hierarchical synthesis reconstruction approach, where training data is pre-processed to separate the information on the domains where sampling biases are suspected. These split bands are then trained separately and combined successively through a hierarchical synthesis network. We apply the hierarchical synthesis reconstruction for two important and classical tomography reconstruction scenarios: the spares-view reconstruction and the phase reconstruction. Our simulated and experimental results show that comparable or improved performances are achieved with a dramatic reduction of network complexity and computational cost. This method can be generalized to a wide range of applications including material characterization, in-vivo monitoring and dynamic 4D imaging.