Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior
This addresses robustness issues in medical imaging for limited angle tomography, though it is incremental as it builds on existing deep learning and iterative methods.
The paper tackles the problem of limited angle tomography reconstruction by proposing a data consistent artifact reduction (DCAR) method that integrates deep learning priors with iterative reconstruction to ensure data integrity in measured areas, resulting in over 10% RMSE reduction in noise-free cases and over 24% RMSE reduction in noisy cases compared to a state-of-the-art U-Net method.
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. Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120-degree cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method.