Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images

no code implementations • 20 May 2020 • Yixing Huang, Alexander Preuhs, Michael Manhart, Guenter Lauritsch, Andreas Maier

For example, for truncated data, DCR achieves a mean root-mean-square error of 24 HU and a mean structure similarity index of 0. 999 inside the field-of-view for different patients in the noisy case, while the state-of-the-art U-Net method achieves 55 HU and 0. 995 respectively for these two metrics.

Computed Tomography (CT) Image Reconstruction

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior

no code implementations • 19 Aug 2019 • Yixing Huang, Alexander Preuhs, Guenter Lauritsch, Michael Manhart, Xiaolin Huang, Andreas Maier

Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise.

Scale-Space Anisotropic Total Variation for Limited Angle Tomography

no code implementations • 19 Dec 2017 • Yixing Huang, Oliver Taubmann, Xiaolin Huang, Viktor Haase, Guenter Lauritsch, Andreas Maier

Hence, the main purpose of this paper is to reduce streak artifacts at various scales.