3 research outputs found
4D X-Ray CT Reconstruction using Multi-Slice Fusion
There is an increasing need to reconstruct objects in four or more dimensions
corresponding to space, time and other independent parameters. The best 4D
reconstruction algorithms use regularized iterative reconstruction approaches
such as model based iterative reconstruction (MBIR), which depends critically
on the quality of the prior modeling. Recently, Plug-and-Play methods have been
shown to be an effective way to incorporate advanced prior models using
state-of-the-art denoising algorithms designed to remove additive white
Gaussian noise (AWGN). However, state-of-the-art denoising algorithms such as
BM4D and deep convolutional neural networks (CNNs) are primarily available for
2D and sometimes 3D images. In particular, CNNs are difficult and
computationally expensive to implement in four or more dimensions, and training
may be impossible if there is no associated high-dimensional training data.
In this paper, we present Multi-Slice Fusion, a novel algorithm for 4D and
higher-dimensional reconstruction, based on the fusion of multiple
low-dimensional denoisers. Our approach uses multi-agent consensus equilibrium
(MACE), an extension of Plug-and-Play, as a framework for integrating the
multiple lower-dimensional prior models. We apply our method to the problem of
4D cone-beam X-ray CT reconstruction for Non Destructive Evaluation (NDE) of
moving parts. This is done by solving the MACE equations using
lower-dimensional CNN denoisers implemented in parallel on a heterogeneous
cluster. Results on experimental CT data demonstrate that Multi-Slice Fusion
can substantially improve the quality of reconstructions relative to
traditional 4D priors, while also being practical to implement and train.Comment: 8 pages, 8 figures, IEEE International Conference on Computational
Photography 2019, Toky