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Principles and Applications for Positron Emission Tomography (PET)

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PET_Principles_and_Applications

Principles and Applications for Positron Emission Tomography (PET)

Principles

Applications

Attenuation Correction

  • [Survey] A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography, Jae Sung Lee, [Paper]

PET Image Reconstruction Libraries

PET Image Estimation/Reconstruction

  • OSEM: Hudson, H. Malcolm, and Richard S. Larkin. "Accelerated image reconstruction using ordered subsets of projection data." IEEE transactions on medical imaging 13.4 (1994): 601-609. [Paper]

  • Wang, Yan, et al. "3D conditional generative adversarial networks for high-quality PET image estimation at low dose." Neuroimage 174 (2018): 550-562. [Paper]

  • 3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis, Yang Zhou*, Zhiwen Yang*, Hui Zhang, Eric I-Chao Chang, Yubo Fan, and Yan Xu [Paper]

  • Deep Kernel Representation for Image Reconstruction in PET, Siqi Li and Guobao Wang, IEEE transactions on medical imaging 2022, [Paper]

  • Positronium Lifetime Image Reconstruction for TOF PET, Jinyi Qi, Bangyan Huang, IEEE transactions on medical imaging 2022, [Paper]

  • Yang, Bao, Leslie Ying, and Jing Tang. "Artificial neural network enhanced Bayesian PET image reconstruction." IEEE transactions on medical imaging 37.6 (2018): 1297-1309. [Paper]

  • Wang, Bo, and Huafeng Liu. "FBP-Net for direct reconstruction of dynamic PET images." Physics in Medicine & Biology 65.23 (2020): 235008. [Paper]

  • Ote, Kibo, et al. "List-Mode PET Image Reconstruction Using Deep Image Prior." arXiv preprint arXiv:2204.13404 (2022). [Paper]

  • Li, Tiantian, et al. "Deep Learning Based Joint PET Image Reconstruction and Motion Estimation." IEEE transactions on medical imaging (2021). [Paper]

  • Yokota, Tatsuya, et al. "Dynamic PET image reconstruction using nonnegative matrix factorization incorporated with deep image prior." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019. [Paper]

  • "Model-Based Deep Learning PET Image Reconstruction Using Forward–Backward Splitting Expectation–Maximization." Mehranian, Abolfazl, and Andrew J. Reader. IEEE transactions on radiation and plasma medical sciences 5.1 (2020): 54-64. [Paper] [Code]

  • "3D Transformer-GAN for High-Quality PET Reconstruction." International Conference on Medical Image Computing and Computer-Assisted Intervention. Luo, Yanmei, et al. Springer, Cham, 2021. [Paper]

  • Quantitative PET in the 2020s: a roadmap, Steven R Meikle et al. [Paper]

  • "Artificial Intelligence for PET Image Reconstruction.", Reader, Andrew J., and Georg Schramm. , Journal of Nuclear Medicine 62.10 (2021): 1330-1333. [Paper]

  • "Efficient neural network image reconstruction from raw data using a radon inversion layer." Whiteley, William, and Jens Gregor. 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2019. [Paper]

  • "Deep learning based framework for direct reconstruction of PET images." Liu, Zhiyuan, Huai Chen, and Huafeng Liu. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019. [Paper]

  • "DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem." Häggström, Ida, et al. Medical image analysis 54 (2019): 253-262. [Paper]

  • "Image reconstruction by domain-transform manifold learning." Zhu, Bo, et al. Nature 555.7697 (2018): 487-492. [Paper]

  • DPIR-Net: Direct PET image reconstruction based on the Wasserstein generative adversarial network[J]. Hu Z, Xue H, Zhang Q, et al. IEEE Transactions on Radiation and Plasma Medical Sciences, 2020, 5(1): 35-43. [Paper]

  • [Survey] Deep Learning for PET Image Reconstruction, Reader, Andrew J., et al. IEEE Transactions on Radiation and Plasma Medical Sciences 5.1 (2020): 1-25. [Paper]

  • "Full-dose PET image estimation from low-dose PET image using deep learning: a pilot study." Kaplan, Sydney, and Yang-Ming Zhu. Journal of digital imaging 32.5 (2019): 773-778.

  • Gong, Kuang, et al. "Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior." IEEE Transactions on Medical Imaging (2021). [Paper]

  • Lim, Hongki, et al. "Improved low-count quantitative PET reconstruction with an iterative neural network." IEEE transactions on medical imaging 39.11 (2020): 3512-3522. [Paper]

  • Yang, Bao, Leslie Ying, and Jing Tang. "Artificial neural network enhanced Bayesian PET image reconstruction." IEEE transactions on medical imaging 37.6 (2018): 1297-1309. [Paper]

  • Hong, Xiang, et al. "Enhancing the image quality via transferred deep residual learning of coarse PET sinograms." IEEE transactions on medical imaging 37.10 (2018): 2322-2332. [Paper]

  • Gong, Kuang, et al. "Iterative PET image reconstruction using convolutional neural network representation." IEEE transactions on medical imaging 38.3 (2018): 675-685. [Paper]

  • Wang, Guobao. "High temporal-resolution dynamic PET image reconstruction using a new spatiotemporal kernel method." IEEE transactions on medical imaging 38.3 (2018): 664-674. [Paper]

  • Wang, Yan, et al. "3D auto-context-based locality adaptive multi-modality GANs for PET synthesis." IEEE transactions on medical imaging 38.6 (2018): 1328-1339. [Paper]

Co-Register and fusion using PET + Others

  • VS, Vibashan, et al. "Image Fusion Transformer." arXiv preprint arXiv:2107.09011 (2021). [Paper]

  • Das, Manisha, et al. "Optimized Multimodal Neurological Image Fusion based on Low-rank Texture Prior Decomposition and Super-pixel Segmentation." IEEE Transactions on Instrumentation and Measurement (2022). [Paper]

  • Xu, Han, and Jiayi Ma. "EMFusion: An unsupervised enhanced medical image fusion network." Information Fusion 76 (2021): 177-186. [Paper]

  • Ding, Yao, et al. "18F-FDG PET and high-resolution MRI co-registration for pre-surgical evaluation of patients with conventional MRI-negative refractory extra-temporal lobe epilepsy." European Journal of Nuclear Medicine and Molecular Imaging 45.9 (2018): 1567-1572. [Paper]

Top-Conference and Journals for Medical Image Processing

Journal

  • TMI: IEEE Transactions on Medical Imaging [homepage]
  • MIA: Medical Image Analysis [homepage]
  • NEUROIMAGE [homepage]
  • JBHI: IEEE Journal of Biomedical and Health Informatics [homepage]
  • JMRI: Journal of Magnetic Resonance Imaging [homepage]
  • TBE: IEEE Transactions on Biomedical Engineering [homepage]
  • CMIG: Computerized Medical Imaging and Graphics [homepage]
  • BSPC: Biomedical Signal Processing and Control [homepage]
  • CMPB: Computer Methods and Programs in Biomedicine [homepage]

Conference

  • MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention
  • IPMI: Information Processing in Medical Imaging
  • ISBI: IEEE International Symposium on Biomedical Imaging
  • SPIE Medical Imaging
  • MIDL: The International Conference on Medical Imaging with Deep Learning

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Principles and Applications for Positron Emission Tomography (PET)