Sparse-view computed tomography (CT) has been attracting attention for its reduced radiation dose and scanning time. However, analytical image reconstruction methods such as filtered back-projection (FBP) suffer from streak artifacts due to sparse-view sampling. Because the streak artifacts are deterministic errors, we argue that the same artifacts can be reasonably estimated using a prior image (i.e., smooth image of the same patient) with known imaging system parameters. Based on this idea, we reconstruct an FBP image with sparse-view projection data, regenerate the streak artifacts by forward and back-projection of a prior image with sparse views, and then subtract them from the original FBP image. For the success of this approach, the prior image needs to be patient specific and easily obtained from given sparse-view projection data. Therefore, we introduce a new concept of implicit neural representations for modeling attenuation coefficients. In the implicit neural representations, neural networks output a patient-specific attenuation coefficient value for an input pixel coordinate. In this way, network's parameters serve as an implicit representation of a CT image. Unlike conventional deep learning approaches that utilize a large, labeled dataset, an implicit neural representation is optimized using only sparse-view projection data of a single patient. This avoids having a bias toward a group of patients in the dataset and helps to capture unique characteristics of the individual properly. We validated the proposed method using fan-beam CT simulation data of an extended cardiac-torso phantom and compared the results with total variation-based iterative reconstruction and an image-based convolutional neural network.
|Title of host publication||Medical Imaging 2022|
|Subtitle of host publication||Physics of Medical Imaging|
|Editors||Wei Zhao, Lifeng Yu|
|Publication status||Published - 2022|
|Event||Medical Imaging 2022: Physics of Medical Imaging - Virtual, Online|
Duration: 2022 Mar 21 → 2022 Mar 27
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Medical Imaging 2022: Physics of Medical Imaging|
|Period||22/3/21 → 22/3/27|
Bibliographical noteFunding Information:
This research was supported by the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (NRF2019R1A2C2084936 and 2020R1A4A1016619) and the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health Welfare, Republic of Korea, the Ministry of Food and Drug Safety) (202011A03).
© 2022 SPIE.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging