Highlights
- •The U-ConformerNet structure is used to exploit the potential of DIP algorithm.
- •The feature distance regularization is used to enhance image quality and detail.
- •The algorithm can be freely switched between supervised and unsupervised modes.
- •The regularization based on image feature is better than traditional TV constraint.
Abstract
Purpose
Methods
Results
Conclusions
Keywords
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Subscribe to Physica Medica: European Journal of Medical PhysicsReferences
- Iterative reconstruction methods in X-ray CT.Phys Med. 2012; 28: 94-108https://doi.org/10.1016/j.ejmp.2012.01.003
Jin X, Li L, Chen Z, Zhang L, Xing Y, Anisotropic total variation for limited-angle CT reconstruction. In IEEE Nuclear Science Symposuim & Medical Imaging Conference. 2010. IEEE. 10.1109/NSSMIC.2010.5874180.
- Deep learning for biomedical image reconstruction: a survey.Artif Intell Rev. 2021; 54: 215-251https://doi.org/10.1007/s10462-020-09861-2
- A review of GPU-based medical image reconstruction.Phys Med. 2017; 42: 76-92https://doi.org/10.1016/j.ejmp.2017.07.024
- Artificial intelligence in image reconstruction: The change is here.Phys Med. 2020; 79: 113-125https://doi.org/10.1016/j.ejmp.2020.11.012
- Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.Phys Med. 2020; 76: 294-306https://doi.org/10.1016/j.ejmp.2020.07.028
- AI applications to medical images: From machine learning to deep learning.Phys Med. 2021; 83: 9-24https://doi.org/10.1016/j.ejmp.2021.02.006
- Basic of machine learning and deep learning in imaging for medical physicists.Phys Med. 2021; 83: 194-205https://doi.org/10.1016/j.ejmp.2021.03.026
- Artificial intelligence and machine learning for medical imaging: A technology review.Phys Med. 2021; 83: 242-256https://doi.org/10.1016/j.ejmp.2021.04.016
- Requirements and reliability of AI in the medical context.Phys Med. 2021; 83: 72-78https://doi.org/10.1016/j.ejmp.2021.02.024
- Current challenges of implementing artificial intelligence in medical imaging.Phys Med. 2022; 100: 12-17https://doi.org/10.1016/j.ejmp.2022.06.003
- Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging.Phys Med Biol. 2020; 65155010https://doi.org/10.1088/1361-6560/ab9066
- Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising.IEEE Access. 2018; 6: 41839-41855https://doi.org/10.1109/ACCESS.2018.2858196
- Segmentation of renal tumors in CT images by 3D U-Net preserving rotational symmetry in axial slices.Opt Continuum. 2022; 1: 297-305https://doi.org/10.1364/OPTCON.451024
- Image and video restoration and compression artefact removal using a NoGAN approach.in: Proceedings of the 28th ACM International Conference on Multimedia. 2020https://doi.org/10.1145/3394171.3414451
- Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.Phys Med. 2020; 76: 28-37https://doi.org/10.1016/j.ejmp.2020.06.004
- and Öktem O, Solving ill-posed inverse problems using iterative deep neural networks.Inverse Prob. 2017; 33124007https://doi.org/10.1088/1361-6420/aa9581
- Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems.IEEE Trans Med Imaging. 2018; 37: 1454-1463https://doi.org/10.1109/TMI.2018.2833499
- ADMM-based deep reconstruction for limited-angle CT.Phys Med Biol. 2019; 64115011https://doi.org/10.1088/1361-6560/ab1aba
- Disentangled generative adversarial network for low-dose CT.EURASIP J Adv Signal Process. 2021; 2021: 34https://doi.org/10.1186/s13634-021-00749-z
- LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT.IEEE Trans Med Imaging. 2018; 37: 1333-1347https://doi.org/10.1109/TMI.2018.2805692
- Learned Primal-Dual Reconstruction.IEEE Trans Med Imaging. 2018; 37: 1322-1332https://doi.org/10.1109/TMI.2018.2799231
Ronneberger O, Fischer P, and Brox T. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. 2015. Springer. 10.1007/978-3-319-24574-4_28.
- Deep image prior.in: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). 2018: 9446-9454
- Computed tomography reconstruction using deep image prior and learned reconstruction methods.Inverse Prob. 2020; 36094004https://doi.org/10.1088/1361-6420/aba415
Chakrabarty P. and Maji S, The spectral bias of the deep image prior. arXiv preprint arXiv:1912.08905; 2019. doi:10.48550/arXiv.1912.08905.
- Taming transformers for high-resolution image synthesis.in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021: 12873-12883
- Dual-domain sparse-view CT reconstruction with Transformers.Phys Med. 2022; 101: 1-7https://doi.org/10.1016/j.ejmp.2022.07.001
- Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT.IEEE Trans Med Imaging. 2018; 37: 1418-1429https://doi.org/10.1109/TMI.2018.2823768
- Conformer: Local Features Coupling Global Representations for Visual Recognition.in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2021: 367-376
- Image reconstruction by domain-transform manifold learning.Nature. 2018; 555: 487-492https://doi.org/10.1038/nature25988
- Intelligent Parameter Tuning in Optimization-Based Iterative CT Reconstruction via Deep Reinforcement Learning.IEEE Trans Med Imaging. 2018; 37: 1430-1439https://doi.org/10.1109/TMI.2018.2823679
Dranoshchuk AD, and Veselov AI. About perceptual quality estimation for image compression. 2019 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF). IEEE, 2019. Doi: 10.1109/WECONF.2019.8840116.
- The Beer-Lambert Law.J Chem Educ. 1962; 39: 333https://doi.org/10.1021/ed039p333
- Sparse-view x-ray CT reconstruction via total generalized variation regularization.Phys Med Biol. 2014; 59: 2997-3017https://doi.org/10.1088/0031-9155/59/12/2997
- The unreasonable effectiveness of deep features as a perceptual metric.in: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). 2018: 586-595
- LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.Sci Data. 2021; 8: 109https://doi.org/10.1038/s41597-021-00893-z
- Deep Convolutional Neural Network for Inverse Problems in Imaging.IEEE Trans Image Process. 2017; 26: 4509-4522https://doi.org/10.1109/TIP.2017.2713099