1 research outputs found
Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks
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.Comment: 9 pages, 6 figures, 2 table