Abstract
Image reconstruction in sparse view CT is a challenging ill-posed inverse problem, which aims at reconstructing a high-quality image from few and noisy measurements. As a prominent tool in the recent development of CT reconstruction, deep neural network (DNN) is mostly used as a denoising post-process or a regularization sub-module in some optimization unrolling method. As the problem of CT reconstruction essentially is about how to convert discrete Fourier transform in polar coordinates to its counterpart in Cartesian coordinates, this paper proposed to directly learn an interpolation scheme, modeled by a multi-scale DNN, for predicting 2D Fourier coefficients in Cartesian coordinates from the available ones in polar coordinates. The experiments showed that, in comparison to existing DNN-based solutions, the proposed DNN-based Fourier interpolation method not only provided the state-of-the-art performance, but also is much more computationally efficient.
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Acknowledgment
This work was supported by NSFC (No.11771288, No.12090024), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102) and Singapore MOE Academic Research Fund (MOE2017-T2-2-156, R-146-000-315-114).
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Ding, Q., Ji, H., Gao, H., Zhang, X. (2021). Learnable Multi-scale Fourier Interpolation for Sparse View CT Image Reconstruction. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_28
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