Skip to main content
Top
Published in: European Journal of Nuclear Medicine and Molecular Imaging 9/2022

Open Access 21-03-2022 | Positron Emission Tomography | Review Article

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement

Authors: Cameron Dennis Pain, Gary F. Egan, Zhaolin Chen

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 9/2022

Login to get access

Abstract

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.
Literature
1.
go back to reference Phelps M, Hoffman E, Mullani N, Ter-Pogossian M. Application of annihilation coincidence detection to transaxial reconstruction tomography. J Nucl Med. 1975;16:210–24.PubMed Phelps M, Hoffman E, Mullani N, Ter-Pogossian M. Application of annihilation coincidence detection to transaxial reconstruction tomography. J Nucl Med. 1975;16:210–24.PubMed
2.
go back to reference Chen H, Zhang Y, Kalra MK, Lin F, Chen Y, Liao P, Zhou J, Wang G. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging. 2017;36(12):2524–35.PubMedPubMedCentralCrossRef Chen H, Zhang Y, Kalra MK, Lin F, Chen Y, Liao P, Zhou J, Wang G. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging. 2017;36(12):2524–35.PubMedPubMedCentralCrossRef
3.
go back to reference Chen Y, Xie Y, Zhou Z, Shi F, Christodoulou AG, Li D. Brain MRI super resolution using 3D deep densely connected neural networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 739–742, IEEE, 2018. Chen Y, Xie Y, Zhou Z, Shi F, Christodoulou AG, Li D. Brain MRI super resolution using 3D deep densely connected neural networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 739–742, IEEE, 2018.
4.
go back to reference Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep learning MR imaging–based attenuation correction for PET/MR imaging. Radiology. 2018;286(2):676–84.PubMedCrossRef Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep learning MR imaging–based attenuation correction for PET/MR imaging. Radiology. 2018;286(2):676–84.PubMedCrossRef
5.
go back to reference Pawar K, Chen Z, Shah NJ, Egan GF. Motion correction in MRI using deep convolutional neural network. In: Proceedings of the ISMRM Scientific Meeting & Exhibition, Paris, vol. 1174, 2018. Pawar K, Chen Z, Shah NJ, Egan GF. Motion correction in MRI using deep convolutional neural network. In: Proceedings of the ISMRM Scientific Meeting & Exhibition, Paris, vol. 1174, 2018.
6.
go back to reference Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, et al. Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging. 2018;37(7):1562–73.PubMedCrossRef Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, et al. Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging. 2018;37(7):1562–73.PubMedCrossRef
7.
go back to reference Teuho J, Torrado-Carvajal A, Herzog H, Anazodo U, Klen R, Iida H, Teräs M. Magnetic resonance-based attenuation correction and scatter correction in neurological positron emission tomography/magnetic resonance imaging—current status with emerging applications. Front Phys. 2020;7:243.CrossRef Teuho J, Torrado-Carvajal A, Herzog H, Anazodo U, Klen R, Iida H, Teräs M. Magnetic resonance-based attenuation correction and scatter correction in neurological positron emission tomography/magnetic resonance imaging—current status with emerging applications. Front Phys. 2020;7:243.CrossRef
8.
go back to reference Lee JS. A review of deep-learning-based approaches for attenuation correction in positron emission tomography. IEEE Trans Radiat Plasma Med Sci. 2020;5(2):160–84.CrossRef Lee JS. A review of deep-learning-based approaches for attenuation correction in positron emission tomography. IEEE Trans Radiat Plasma Med Sci. 2020;5(2):160–84.CrossRef
9.
go back to reference Gillman A, Smith J, Thomas P, Rose S, Dowson N. PET motion correction in context of integrated PET/MR: current techniques, limitations, and future projections. Med Phys. 2017;44(12):e430–45.PubMedCrossRef Gillman A, Smith J, Thomas P, Rose S, Dowson N. PET motion correction in context of integrated PET/MR: current techniques, limitations, and future projections. Med Phys. 2017;44(12):e430–45.PubMedCrossRef
10.
go back to reference Wang T, Lei Y, Fu Y, Curran WJ, Liu T, Nye JA, Yang X. Machine learning in quantitative PET: a review of attenuation correction and low-count image reconstruction methods. Physica Med. 2020;76:294–306.CrossRef Wang T, Lei Y, Fu Y, Curran WJ, Liu T, Nye JA, Yang X. Machine learning in quantitative PET: a review of attenuation correction and low-count image reconstruction methods. Physica Med. 2020;76:294–306.CrossRef
11.
go back to reference Domingues I, Pereira G, Martins P, Duarte H, Santos J, Abreu PH. Using deep learning techniques in medical imaging: a systematic review of applications on ct and pet. Artif Intell Rev. 2020;53(6):4093–160.CrossRef Domingues I, Pereira G, Martins P, Duarte H, Santos J, Abreu PH. Using deep learning techniques in medical imaging: a systematic review of applications on ct and pet. Artif Intell Rev. 2020;53(6):4093–160.CrossRef
12.
go back to reference Reader AJ, Corda G, Mehranian A, da Costa-Luis C, Ellis S, Schnabel JA. Deep learning for pet image reconstruction. IEEE Trans Radiat Plasma Med Sci. 2020;5(1):1–25.CrossRef Reader AJ, Corda G, Mehranian A, da Costa-Luis C, Ellis S, Schnabel JA. Deep learning for pet image reconstruction. IEEE Trans Radiat Plasma Med Sci. 2020;5(1):1–25.CrossRef
13.
go back to reference Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging. 1982;1(2):113–22.PubMedCrossRef Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging. 1982;1(2):113–22.PubMedCrossRef
14.
go back to reference Panin V, Kehren F, Michel C, Casey M. Fully 3-D PET reconstruction with system matrix derived from point source measurements. IEEE Trans Med Imaging. 2006;25(7):907–21.PubMedCrossRef Panin V, Kehren F, Michel C, Casey M. Fully 3-D PET reconstruction with system matrix derived from point source measurements. IEEE Trans Med Imaging. 2006;25(7):907–21.PubMedCrossRef
15.
go back to reference Defrise M, Townsend D, Bailey D, Geissbuhler A, Jones T. A normalization technique for 3d pet data. Phys Med Biol. 1991;36(7):939.PubMedCrossRef Defrise M, Townsend D, Bailey D, Geissbuhler A, Jones T. A normalization technique for 3d pet data. Phys Med Biol. 1991;36(7):939.PubMedCrossRef
17.
go back to reference Qi J, Leahy RM. Iterative reconstruction techniques in emission computed tomography. Phys Med Biol. 2006;51(15):R541.PubMedCrossRef Qi J, Leahy RM. Iterative reconstruction techniques in emission computed tomography. Phys Med Biol. 2006;51(15):R541.PubMedCrossRef
18.
go back to reference Levitan E, Herman GT. A maximum a posteriori probability expectation maximization algorithm for image reconstruction in emission tomography. IEEE Trans Med Imaging. 1987;6(3):185–92.PubMedCrossRef Levitan E, Herman GT. A maximum a posteriori probability expectation maximization algorithm for image reconstruction in emission tomography. IEEE Trans Med Imaging. 1987;6(3):185–92.PubMedCrossRef
19.
go back to reference Lange K, Bahn M, Little R. A theoretical study of some maximum likelihood algorithms for emission and transmission tomography. IEEE Trans Med Imaging. 1987;6(2):106–14.PubMedCrossRef Lange K, Bahn M, Little R. A theoretical study of some maximum likelihood algorithms for emission and transmission tomography. IEEE Trans Med Imaging. 1987;6(2):106–14.PubMedCrossRef
20.
go back to reference Huber PJ. Robust statistics, vol. 523. Wiley; 2004. Huber PJ. Robust statistics, vol. 523. Wiley; 2004.
21.
go back to reference Green PJ. Bayesian reconstructions from emission tomography data using a modified EM algorithm. IEEE Trans Med Imaging. 1990;9(1):84–93.PubMedCrossRef Green PJ. Bayesian reconstructions from emission tomography data using a modified EM algorithm. IEEE Trans Med Imaging. 1990;9(1):84–93.PubMedCrossRef
22.
go back to reference Bouman CA, Sauer K. A unified approach to statistical tomography using coordinate descent optimization. IEEE Trans Image Process. 1996;5(3):480–92.PubMedCrossRef Bouman CA, Sauer K. A unified approach to statistical tomography using coordinate descent optimization. IEEE Trans Image Process. 1996;5(3):480–92.PubMedCrossRef
24.
go back to reference Novosad P, Reader AJ. Mr-guided dynamic pet reconstruction with the kernel method and spectral temporal basis functions. Phys Med Biol. 2016;61(12):4624.PubMedCrossRef Novosad P, Reader AJ. Mr-guided dynamic pet reconstruction with the kernel method and spectral temporal basis functions. Phys Med Biol. 2016;61(12):4624.PubMedCrossRef
25.
go back to reference Chen S, Liu H, Shi P, Chen Y. Sparse representation and dictionary learning penalized image reconstruction for positron emission tomography. Phys Med Biol. 2015;60(2):807.PubMedCrossRef Chen S, Liu H, Shi P, Chen Y. Sparse representation and dictionary learning penalized image reconstruction for positron emission tomography. Phys Med Biol. 2015;60(2):807.PubMedCrossRef
26.
go back to reference Tang J, Yang B, Wang Y, Ying L. Sparsity-constrained pet image reconstruction with learned dictionaries. Phys Med Biol. 2016;61(17):6347.PubMedCrossRef Tang J, Yang B, Wang Y, Ying L. Sparsity-constrained pet image reconstruction with learned dictionaries. Phys Med Biol. 2016;61(17):6347.PubMedCrossRef
27.
go back to reference Wang Y, Ma G, An L, Shi F, Zhang P, Lalush DS, Wu X, Pu Y, Zhou J, Shen D. Semisupervised tripled dictionary learning for standard-dose PET image prediction using low-dose PET and multimodal MRI. IEEE Trans Biomed Eng. 2016;64(3):569–79.PubMedPubMedCentralCrossRef Wang Y, Ma G, An L, Shi F, Zhang P, Lalush DS, Wu X, Pu Y, Zhou J, Shen D. Semisupervised tripled dictionary learning for standard-dose PET image prediction using low-dose PET and multimodal MRI. IEEE Trans Biomed Eng. 2016;64(3):569–79.PubMedPubMedCentralCrossRef
28.
go back to reference J. E. Bowsher, H. Yuan, L. W. Hedlund, T. G. Turkington, G. Akabani, A. Badea, W. C. Kurylo, C. T. Wheeler, G. P. Cofer, M. W. Dewhirst, et al., Utilizing MRI information to estimate F18-FDG distributions in rat flank tumors. IEEE Symposium Conference Record Nuclear Science 2004.,vol. 4, pp. 2488–2492, IEEE, 2004. J. E. Bowsher, H. Yuan, L. W. Hedlund, T. G. Turkington, G. Akabani, A. Badea, W. C. Kurylo, C. T. Wheeler, G. P. Cofer, M. W. Dewhirst, et al., Utilizing MRI information to estimate F18-FDG distributions in rat flank tumors. IEEE Symposium Conference Record Nuclear Science 2004.,vol. 4, pp. 2488–2492, IEEE, 2004.
29.
go back to reference Gindi G, Lee M, Rangarajan A, Zubal IG. Bayesian reconstruction of functional images using anatomical information as priors. IEEE Trans Med Imaging. 1993;12(4):670–80.PubMedCrossRef Gindi G, Lee M, Rangarajan A, Zubal IG. Bayesian reconstruction of functional images using anatomical information as priors. IEEE Trans Med Imaging. 1993;12(4):670–80.PubMedCrossRef
30.
go back to reference Sastry S, Carson RE. Multimodality Bayesian algorithm for image reconstruction in positron emission tomography: a tissue composition model. IEEE Trans Med Imaging. 1997;16(6):750–61.PubMedCrossRef Sastry S, Carson RE. Multimodality Bayesian algorithm for image reconstruction in positron emission tomography: a tissue composition model. IEEE Trans Med Imaging. 1997;16(6):750–61.PubMedCrossRef
31.
go back to reference Leahy R, Yan X. Incorporation of anatomical MR data for improved functional imaging with PET. In: Biennial International Conference on Information Processing in Medical Imaging. Springer; 1991. p. 105–20.CrossRef Leahy R, Yan X. Incorporation of anatomical MR data for improved functional imaging with PET. In: Biennial International Conference on Information Processing in Medical Imaging. Springer; 1991. p. 105–20.CrossRef
32.
go back to reference V. P. Sudarshan, G. F. Egan, Z. Chen, and S. P. Awate, “Joint PET-MRI image reconstruction using a patch-based joint-dictionary prior,” Medical Image Analysis, vol. 62, p. 101669, 2020. V. P. Sudarshan, G. F. Egan, Z. Chen, and S. P. Awate, “Joint PET-MRI image reconstruction using a patch-based joint-dictionary prior,” Medical Image Analysis, vol. 62, p. 101669, 2020.
33.
go back to reference Buades A, Coll B, Morel J-M. A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 60–65, IEEE, 2005. Buades A, Coll B, Morel J-M. A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, pp. 60–65, IEEE, 2005.
35.
go back to reference Maggioni M, Katkovnik V, Egiazarian K, Foi A. Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans Image Process. 2012;22(1):119–33.PubMedCrossRef Maggioni M, Katkovnik V, Egiazarian K, Foi A. Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans Image Process. 2012;22(1):119–33.PubMedCrossRef
36.
go back to reference Peltonen S, Tuna U, Sánchez-Mong E, Ruotsalainen U. PET sinogram denoising by block-matching and 3D filtering. In: 2011 IEEE Nuclear Science Symposium Conference Record, pp. 3125–3129, 2011. Peltonen S, Tuna U, Sánchez-Mong E, Ruotsalainen U. PET sinogram denoising by block-matching and 3D filtering. In: 2011 IEEE Nuclear Science Symposium Conference Record, pp. 3125–3129, 2011.
37.
go back to reference Millardet M, Moussaoui S, Mateus D, Idier J, Carlier T. Local-mean preserving post-processing step for non-negativity enforcement in PET imaging: application to ¡sup¿90¡/sup¿y-pet. IEEE Trans Med Imaging. 2020;39(11):3725–36.PubMedCrossRef Millardet M, Moussaoui S, Mateus D, Idier J, Carlier T. Local-mean preserving post-processing step for non-negativity enforcement in PET imaging: application to ¡sup¿90¡/sup¿y-pet. IEEE Trans Med Imaging. 2020;39(11):3725–36.PubMedCrossRef
38.
go back to reference Teo B-K, Seo Y, Bacharach SL, Carrasquillo JA, Libutti SK, Shukla H, Hasegawa BH, Hawkins RA, Franc BL. Partial-volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data. J Nucl Med. 2007;48(5):802–10.PubMed Teo B-K, Seo Y, Bacharach SL, Carrasquillo JA, Libutti SK, Shukla H, Hasegawa BH, Hawkins RA, Franc BL. Partial-volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data. J Nucl Med. 2007;48(5):802–10.PubMed
39.
go back to reference Tohka J, Reilhac A. Deconvolution-based partial volume correction in Raclopride-PET and Monte Carlo comparison to MR-based method. Neuroimage. 2008;39(4):1570–84.PubMedCrossRef Tohka J, Reilhac A. Deconvolution-based partial volume correction in Raclopride-PET and Monte Carlo comparison to MR-based method. Neuroimage. 2008;39(4):1570–84.PubMedCrossRef
40.
go back to reference Golla SS, Lubberink M, van Berckel BN, Lammertsma AA, Boellaard R. Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising. EJNMMI Res. 2017;7(1):1–12.CrossRef Golla SS, Lubberink M, van Berckel BN, Lammertsma AA, Boellaard R. Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising. EJNMMI Res. 2017;7(1):1–12.CrossRef
41.
go back to reference Mignotte M, Meunier J. Three-dimensional blind deconvolution of SPECT images. IEEE Trans Biomed Eng. 2000;47(2):274–80.PubMedCrossRef Mignotte M, Meunier J. Three-dimensional blind deconvolution of SPECT images. IEEE Trans Biomed Eng. 2000;47(2):274–80.PubMedCrossRef
42.
go back to reference Thielemans K, Asma E, Ahn S, Manjeshwar R, Deller T, Ross S, Stearns C, Ganin A. Impact of PSF modelling on the convergence rate and edge behaviour of EM images in PET. In: IEEE Nuclear Science Symposuim & Medical Imaging Conference, pp. 3267–3272, IEEE; 2010. Thielemans K, Asma E, Ahn S, Manjeshwar R, Deller T, Ross S, Stearns C, Ganin A. Impact of PSF modelling on the convergence rate and edge behaviour of EM images in PET. In: IEEE Nuclear Science Symposuim & Medical Imaging Conference, pp. 3267–3272, IEEE; 2010.
43.
go back to reference Sudarshan VP, Li S, Jamadar SD, Egan GF, Awate SP, Chen Z. Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET. NeuroImage. 2021;233:117928.PubMedCrossRef Sudarshan VP, Li S, Jamadar SD, Egan GF, Awate SP, Chen Z. Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET. NeuroImage. 2021;233:117928.PubMedCrossRef
44.
go back to reference Tahaei MS, Reader AJ, Collins DL. Two novel PET image restoration methods guided by PET-MR kernels: application to brain imaging. Med Phys. 2019;46(5):2085–102.PubMedCrossRef Tahaei MS, Reader AJ, Collins DL. Two novel PET image restoration methods guided by PET-MR kernels: application to brain imaging. Med Phys. 2019;46(5):2085–102.PubMedCrossRef
45.
go back to reference Haggstrom I, Schmidtlein C, Campanella G, Fuchs T. DeepPET: a deep encoder–decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal. 2019;54:253–62.PubMedPubMedCentralCrossRef Haggstrom I, Schmidtlein C, Campanella G, Fuchs T. DeepPET: a deep encoder–decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal. 2019;54:253–62.PubMedPubMedCentralCrossRef
46.
go back to reference Huang Y, Zhu H, Duan X, Hong X, Sun H, Lv W, Lu L, Feng Q. Gapfill-recon net: a cascade network for simultaneously pet gap filling and image reconstruction. Comput Methods Programs Biomed. 2021;208:106271.PubMedCrossRef Huang Y, Zhu H, Duan X, Hong X, Sun H, Lv W, Lu L, Feng Q. Gapfill-recon net: a cascade network for simultaneously pet gap filling and image reconstruction. Comput Methods Programs Biomed. 2021;208:106271.PubMedCrossRef
47.
go back to reference Z. Liu, H. Chen, and H. Liu, “Deep learning based framework for direct reconstruction of pet images,” in Medical Image Computing and Computer Assisted Intervention MICCAI 2019, pp. 48–56, Springer International Publishing, 2019. Z. Liu, H. Chen, and H. Liu, “Deep learning based framework for direct reconstruction of pet images,” in Medical Image Computing and Computer Assisted Intervention MICCAI 2019, pp. 48–56, Springer International Publishing, 2019.
48.
go back to reference Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks. In International conference on machine learning, pp. 214–223, PMLR, 2017. Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks. In International conference on machine learning, pp. 214–223, PMLR, 2017.
49.
go back to reference Zhu J-Y, Park T, Isola P, Efros A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp. 2223–2232, 2017. Zhu J-Y, Park T, Isola P, Efros A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp. 2223–2232, 2017.
50.
go back to reference Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
51.
go back to reference Kandarpa VSS, Bousse A, Benoit D, Visvikis D. Dug-recon: a framework for direct image reconstruction using convolutional generative networks. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):44–53.CrossRef Kandarpa VSS, Bousse A, Benoit D, Visvikis D. Dug-recon: a framework for direct image reconstruction using convolutional generative networks. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):44–53.CrossRef
52.
go back to reference Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 2018;555(7697):487–92.PubMedCrossRef Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 2018;555(7697):487–92.PubMedCrossRef
53.
go back to reference Wang B, Liu H. FBP-Net for direct reconstruction of dynamic PET images. Phys Med Biol. 2020;65(23). Wang B, Liu H. FBP-Net for direct reconstruction of dynamic PET images. Phys Med Biol. 2020;65(23).
54.
go back to reference Zhang Q, Gao J, Ge Y, Zhang N, Yang Y, Liu X, Zheng H, Liang D, Hu Z. PET image reconstruction using a cascading back-projection neural network. IEEE J Sel Top Sign Proces. 2020;14(6):1100–11.CrossRef Zhang Q, Gao J, Ge Y, Zhang N, Yang Y, Liu X, Zheng H, Liang D, Hu Z. PET image reconstruction using a cascading back-projection neural network. IEEE J Sel Top Sign Proces. 2020;14(6):1100–11.CrossRef
55.
go back to reference Xue H, Zhang Q, Zou S, Zhang W, Zhou C, Tie C, Wan Q, Teng Y, Li Y, Liang D, Liu X, Yang Y, Zheng H, Zhu X, Hu Z. LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks. Quant Imaging Med Surg. 2021;11(2):749–62.PubMedPubMedCentralCrossRef Xue H, Zhang Q, Zou S, Zhang W, Zhou C, Tie C, Wan Q, Teng Y, Li Y, Liang D, Liu X, Yang Y, Zheng H, Zhu X, Hu Z. LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks. Quant Imaging Med Surg. 2021;11(2):749–62.PubMedPubMedCentralCrossRef
56.
go back to reference Whiteley W, Whiteley W, Luk W, Gregor J. DirectPET: full-size neural network PET reconstruction from sinogram data. J Med Imaging. 2020;7(3). Whiteley W, Whiteley W, Luk W, Gregor J. DirectPET: full-size neural network PET reconstruction from sinogram data. J Med Imaging. 2020;7(3).
57.
go back to reference Whiteley W, Panin V, Zhou C, Cabello J, Bharkhada D, Gregor J. FastPET: near real-time reconstruction of PET histo-image data using a neural network. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):65–77.CrossRef Whiteley W, Panin V, Zhou C, Cabello J, Bharkhada D, Gregor J. FastPET: near real-time reconstruction of PET histo-image data using a neural network. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):65–77.CrossRef
58.
go back to reference Feng T, Yao S, Xi C, Zhao Y, Wang R, Wu S, Li C, Xu B. Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format. Phys Med Biol. 2021;66(16):165007.CrossRef Feng T, Yao S, Xi C, Zhao Y, Wang R, Wu S, Li C, Xu B. Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format. Phys Med Biol. 2021;66(16):165007.CrossRef
59.
go back to reference Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 2019;31(7):1235–70.PubMedCrossRef Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 2019;31(7):1235–70.PubMedCrossRef
60.
go back to reference Gong K, Guan J, Kim K, Zhang X, Yang J, Seo Y, El Fakhri G, Qi J, Li Q. Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans Med Imaging. 2019;38(3):675–85.CrossRef Gong K, Guan J, Kim K, Zhang X, Yang J, Seo Y, El Fakhri G, Qi J, Li Q. Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans Med Imaging. 2019;38(3):675–85.CrossRef
61.
go back to reference Gong K, Catana C, Qi J, Li Q. Direct patlak reconstruction from dynamic PET using unsupervised deep learning. In: 15th International meeting on fully three-dimensional image reconstruction in radiology and nuclear medicine, vol. 11072, p. 110720R, International Society for Optics and Photonics; 2019. Gong K, Catana C, Qi J, Li Q. Direct patlak reconstruction from dynamic PET using unsupervised deep learning. In: 15th International meeting on fully three-dimensional image reconstruction in radiology and nuclear medicine, vol. 11072, p. 110720R, International Society for Optics and Photonics; 2019.
63.
go back to reference Xie Z, Baikejiang R, Li T, Zhang X, Gong K, Zhang M, Qi W, Asma E, Qi J. Generative adversarial network based regularized image reconstruction for PET. Phys Med Biol. 2020;65(12). Xie Z, Baikejiang R, Li T, Zhang X, Gong K, Zhang M, Qi W, Asma E, Qi J. Generative adversarial network based regularized image reconstruction for PET. Phys Med Biol. 2020;65(12).
64.
go back to reference Xie Z, Li T, Zhang X, Qi W, Asma E, Qi J. Anatomically aided PET image reconstruction using deep neural networks. Med Phys. 2021. Xie Z, Li T, Zhang X, Qi W, Asma E, Qi J. Anatomically aided PET image reconstruction using deep neural networks. Med Phys. 2021.
65.
go back to reference Kim K, Wu D, Gong K, Dutta J, Kim J, Son Y, Kim H, El Fakhri G, Li Q. Penalized PET reconstruction using deep learning prior and local linear fitting. IEEE Trans Med Imaging. 2018;37(6):1478–87.PubMedPubMedCentralCrossRef Kim K, Wu D, Gong K, Dutta J, Kim J, Son Y, Kim H, El Fakhri G, Li Q. Penalized PET reconstruction using deep learning prior and local linear fitting. IEEE Trans Med Imaging. 2018;37(6):1478–87.PubMedPubMedCentralCrossRef
66.
go back to reference Wang X, Zhou L, Wang Y, Jiang H, Ye H. Improved low-dose positron emission tomography image reconstruction using deep learned prior. Phys Med Biol. 2021;66(11):115001.CrossRef Wang X, Zhou L, Wang Y, Jiang H, Ye H. Improved low-dose positron emission tomography image reconstruction using deep learned prior. Phys Med Biol. 2021;66(11):115001.CrossRef
67.
go back to reference Lv Y, Xi C. PET image reconstruction with deep progressive learning. Phys Med Biol. 2021;66(10):105016.CrossRef Lv Y, Xi C. PET image reconstruction with deep progressive learning. Phys Med Biol. 2021;66(10):105016.CrossRef
68.
go back to reference Mehranian A, Reader AJ. Model-based deep learning PET image reconstruction using forward–backward splitting expectation–maximization. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):54–64.CrossRef Mehranian A, Reader AJ. Model-based deep learning PET image reconstruction using forward–backward splitting expectation–maximization. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):54–64.CrossRef
69.
go back to reference Lim H, Huang Z, Fessler JA, Dewaraja YK, Chun IY. Application of trained Deep BCD-net to iterative low-count PET image reconstruction. In 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), pp. 1–4, IEEE, 2018. Lim H, Huang Z, Fessler JA, Dewaraja YK, Chun IY. Application of trained Deep BCD-net to iterative low-count PET image reconstruction. In 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), pp. 1–4, IEEE, 2018.
70.
go back to reference Chun Y, Fessler JA. Deep BCD-net using identical encoding-decoding CNN structures for iterative image recovery. In 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pp. 1–5, 2018. Chun Y, Fessler JA. Deep BCD-net using identical encoding-decoding CNN structures for iterative image recovery. In 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pp. 1–5, 2018.
71.
go back to reference Gong K, Wu D, Kim K, Yang J, Sun T, El Fakhri G, Seo Y, Li Q. MAPEM-Net: an unrolled neural network for fully 3D PET image reconstruction. In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, vol. 11072, p. 110720O, International Society for Optics and Photonics; 2019. Gong K, Wu D, Kim K, Yang J, Sun T, El Fakhri G, Seo Y, Li Q. MAPEM-Net: an unrolled neural network for fully 3D PET image reconstruction. In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, vol. 11072, p. 110720O, International Society for Optics and Photonics; 2019.
72.
go back to reference Corda-D’Incan G, Schnabel JA, Reader AJ. Memory-efficient training for fully unrolled deep learned PET image reconstruction with iteration-dependent targets. IEEE Transactions on Radiation and Plasma Medical Sciences. 2021;1–1. Corda-D’Incan G, Schnabel JA, Reader AJ. Memory-efficient training for fully unrolled deep learned PET image reconstruction with iteration-dependent targets. IEEE Transactions on Radiation and Plasma Medical Sciences. 2021;1–1.
73.
go back to reference Xiang L, Qiao Y, Nie D, An L, Lin W, Wang Q, Shen D. Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI. Neurocomputing. 2017;267:406–16.PubMedPubMedCentralCrossRef Xiang L, Qiao Y, Nie D, An L, Lin W, Wang Q, Shen D. Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI. Neurocomputing. 2017;267:406–16.PubMedPubMedCentralCrossRef
74.
go back to reference Xu J, Gong E, Pauly J, Zaharchuk . 200x low-dose PET reconstruction using deep learning. arXiv preprint arXiv:1712.04119, 2017. Xu J, Gong E, Pauly J, Zaharchuk . 200x low-dose PET reconstruction using deep learning. arXiv preprint arXiv:1712.04119, 2017.
75.
go back to reference Chen K, Gong E, de Carvalho Macruz F, Xu J, Boumis A, Khalighi M, et al. Ultra-low-dose 18f-florbetaben amyloid pet imaging using deep learning with multi-contrast mri inputs. Radiology. 2019;290(3):649–56.PubMedCrossRef Chen K, Gong E, de Carvalho Macruz F, Xu J, Boumis A, Khalighi M, et al. Ultra-low-dose 18f-florbetaben amyloid pet imaging using deep learning with multi-contrast mri inputs. Radiology. 2019;290(3):649–56.PubMedCrossRef
76.
go back to reference Ladefoged C, Hasbak P, Hornnes C, Højgaard L, Andersen F. Low-dose PET image noise reduction using deep learning: application to cardiac viability FDG imaging in patients with ischemic heart disease. Phys Med Biol. 2021;66(5). Ladefoged C, Hasbak P, Hornnes C, Højgaard L, Andersen F. Low-dose PET image noise reduction using deep learning: application to cardiac viability FDG imaging in patients with ischemic heart disease. Phys Med Biol. 2021;66(5).
77.
go back to reference Wang Y-R, Baratto L, Hawk K, Theruvath A, Pribnow A, Thakor A, Gatidis S, Lu R, Gummidipundi S, Garcia-Diaz J, Rubin D, Daldrup-Link H. Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure. Eur J Nucl Med Mol Imaging. 2021. Wang Y-R, Baratto L, Hawk K, Theruvath A, Pribnow A, Thakor A, Gatidis S, Lu R, Gummidipundi S, Garcia-Diaz J, Rubin D, Daldrup-Link H. Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure. Eur J Nucl Med Mol Imaging. 2021.
79.
go back to reference Spuhler K, Serrano-Sosa M, Cattell R, DeLorenzo C, Huang C. Full-count PET recovery from low-count image using a dilated convolutional neural network. Med Phys. 2020;47(10):4928–38.PubMedCrossRef Spuhler K, Serrano-Sosa M, Cattell R, DeLorenzo C, Huang C. Full-count PET recovery from low-count image using a dilated convolutional neural network. Med Phys. 2020;47(10):4928–38.PubMedCrossRef
80.
go back to reference Sanaat A, Arabi H, Mainta I, Garibotto V, Zaidi H. Projection space implementation of deep learning–guided low-dose brain PET imaging improves performance over implementation in image space. J Nucl Med. 2020;61(9):1388–96.PubMedPubMedCentralCrossRef Sanaat A, Arabi H, Mainta I, Garibotto V, Zaidi H. Projection space implementation of deep learning–guided low-dose brain PET imaging improves performance over implementation in image space. J Nucl Med. 2020;61(9):1388–96.PubMedPubMedCentralCrossRef
81.
go back to reference Wang X, Yang B, Moody J, Tang J, Wang X. Improved myocardial perfusion PET imaging using artificial neural networks. Phys Med Biol. 2020;65(14). Wang X, Yang B, Moody J, Tang J, Wang X. Improved myocardial perfusion PET imaging using artificial neural networks. Phys Med Biol. 2020;65(14).
82.
go back to reference Schaefferkoetter J, Yan J, Ortega C, Sertic A, Lechtman E, Eshet Y, Metser U, Veit-Haibach P. Convolutional neural networks for improving image quality with noisy PET data. EJNMMI Research. 2020;10(1). Schaefferkoetter J, Yan J, Ortega C, Sertic A, Lechtman E, Eshet Y, Metser U, Veit-Haibach P. Convolutional neural networks for improving image quality with noisy PET data. EJNMMI Research. 2020;10(1).
83.
go back to reference Liu C-C, Qi J. Higher SNR PET image prediction using a deep learning model and MRI image. Phys Med Biol. 2019;64(11). Liu C-C, Qi J. Higher SNR PET image prediction using a deep learning model and MRI image. Phys Med Biol. 2019;64(11).
84.
go back to reference Costa-Luis COD, Reader AJ. Micro-networks for robust MR-guided low count PET imaging. IEEE Transactions on Radiat Plasma Med Sci. 2021;5(2):202–12.CrossRef Costa-Luis COD, Reader AJ. Micro-networks for robust MR-guided low count PET imaging. IEEE Transactions on Radiat Plasma Med Sci. 2021;5(2):202–12.CrossRef
85.
go back to reference Zhou L, Schaefferkoetter J, Tham I, Huang G, Yan J. Supervised learning with cyclegan for low-dose FDG PET image denoising. Med Image Anal. 2020;65. Zhou L, Schaefferkoetter J, Tham I, Huang G, Yan J. Supervised learning with cyclegan for low-dose FDG PET image denoising. Med Image Anal. 2020;65.
86.
go back to reference Jeong Y, Park H, Jeong J, Yoon H, Jeon K, Cho K, Kang D-Y. Restoration of amyloid pet images obtained with short-time data using a generative adversarial networks framework. Sci Reports. 2021;11(1). Jeong Y, Park H, Jeong J, Yoon H, Jeon K, Cho K, Kang D-Y. Restoration of amyloid pet images obtained with short-time data using a generative adversarial networks framework. Sci Reports. 2021;11(1).
87.
go back to reference Xue H, Teng Y, Tie C, Wan Q, Wu J, Li M, Liang G, Liang D, Liu X, Zheng H, Yang Y, Hu Z, Zhang N. A 3D attention residual encoder–decoder least-square GAN for low-count PET denoising. Nucl Inst Methods Phys Res A Acceler Spectrom Detect Associat Equip. 2020;983. Xue H, Teng Y, Tie C, Wan Q, Wu J, Li M, Liang G, Liang D, Liu X, Zheng H, Yang Y, Hu Z, Zhang N. A 3D attention residual encoder–decoder least-square GAN for low-count PET denoising. Nucl Inst Methods Phys Res A Acceler Spectrom Detect Associat Equip. 2020;983.
88.
go back to reference Lei Y, Dong X, Wang T, Higgins K, Liu T, Curran W, Mao H, Nye J, Yang X. Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks. Phys Med Biol. 2019;64(21). Lei Y, Dong X, Wang T, Higgins K, Liu T, Curran W, Mao H, Nye J, Yang X. Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks. Phys Med Biol. 2019;64(21).
89.
go back to reference Zhao K, Zhou L, Gao S, Wang X, Wang Y, Zhao X, Wang H, Liu K, Zhu Y, Ye H. Study of low-dose PET image recovery using supervised learning with CycleGAN. PLoS ONE. 2020;15(9). Zhao K, Zhou L, Gao S, Wang X, Wang Y, Zhao X, Wang H, Liu K, Zhu Y, Ye H. Study of low-dose PET image recovery using supervised learning with CycleGAN. PLoS ONE. 2020;15(9).
90.
go back to reference Kaplan S, Zhu Y-M. Full-dose PET image estimation from low-dose PET image using deep learning: a pilot study. J Digit Imaging. 2019;32(5):773–8.PubMedCrossRef Kaplan S, Zhu Y-M. Full-dose PET image estimation from low-dose PET image using deep learning: a pilot study. J Digit Imaging. 2019;32(5):773–8.PubMedCrossRef
91.
go back to reference Ouyang J, Chen K, Gong E, Pauly J, Zaharchuk G. Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med Phys. 2019;46(8):3555–64.PubMedCrossRef Ouyang J, Chen K, Gong E, Pauly J, Zaharchuk G. Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med Phys. 2019;46(8):3555–64.PubMedCrossRef
92.
go back to reference Wang Y, Yu B, Wang L, Zu C, Lalush D, Lin W, Wu X, Zhou J, Shen D, Zhou L. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage. 2018;174:550–62.PubMedCrossRef Wang Y, Yu B, Wang L, Zu C, Lalush D, Lin W, Wu X, Zhou J, Shen D, Zhou L. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage. 2018;174:550–62.PubMedCrossRef
93.
go back to reference Wang Y, Zhou L, Yu B, Wang L, Zu C, Lalush D, Lin W, Wu X, Zhou J, Shen D. 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Trans Med Imaging. 2019;38(6):1328–39.PubMedCrossRef Wang Y, Zhou L, Yu B, Wang L, Zu C, Lalush D, Lin W, Wu X, Zhou J, Shen D. 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Trans Med Imaging. 2019;38(6):1328–39.PubMedCrossRef
94.
go back to reference Gong Y, Shan H, Teng Y, Tu N, Li M, Liang G, Wang G, Wang S. Parameter-transferred Wasserstein generative adversarial network (PT-WGAN) for low-dose PET image denoising. IEEE Trans Radiat Plasma Med Sci. 2021;5(2):213–23.PubMedCrossRef Gong Y, Shan H, Teng Y, Tu N, Li M, Liang G, Wang G, Wang S. Parameter-transferred Wasserstein generative adversarial network (PT-WGAN) for low-dose PET image denoising. IEEE Trans Radiat Plasma Med Sci. 2021;5(2):213–23.PubMedCrossRef
95.
go back to reference Lu W, Onofrey J, Lu Y, Shi L, Ma T, Liu Y, Liu C. An investigation of quantitative accuracy for deep learning based denoising in oncological pet. Phys Med Biol. 2019;64(16). Lu W, Onofrey J, Lu Y, Shi L, Ma T, Liu Y, Liu C. An investigation of quantitative accuracy for deep learning based denoising in oncological pet. Phys Med Biol. 2019;64(16).
96.
go back to reference Chen K, Toueg T, Koran M, Davidzon G, Zeineh M, Holley D, Gandhi H, Halbert K, Boumis A, Kennedy G, Mormino E, Khalighi M, Zaharchuk G. True ultra-low-dose amyloid pet/mri enhanced with deep learning for clinical interpretation. Eur J Nucl Med Mol Imaging. 2021. Chen K, Toueg T, Koran M, Davidzon G, Zeineh M, Holley D, Gandhi H, Halbert K, Boumis A, Kennedy G, Mormino E, Khalighi M, Zaharchuk G. True ultra-low-dose amyloid pet/mri enhanced with deep learning for clinical interpretation. Eur J Nucl Med Mol Imaging. 2021.
97.
go back to reference Chen K, Schürer M, Ouyang J, Koran M, Davidzon G, Mormino E, Tiepolt S, Hoffmann K-T, Sabri O, Zaharchuk G, Barthel H. Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning. Eur J Nucl Med Mol Imaging. 2020;47(13):2998–3007.PubMedPubMedCentralCrossRef Chen K, Schürer M, Ouyang J, Koran M, Davidzon G, Mormino E, Tiepolt S, Hoffmann K-T, Sabri O, Zaharchuk G, Barthel H. Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning. Eur J Nucl Med Mol Imaging. 2020;47(13):2998–3007.PubMedPubMedCentralCrossRef
98.
go back to reference Liu H, Wu J, Lu W, Onofrey J, Liu Y-H, Liu C. Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET. Phys Med Biol. 2020;65(18). Liu H, Wu J, Lu W, Onofrey J, Liu Y-H, Liu C. Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET. Phys Med Biol. 2020;65(18).
99.
go back to reference Gong K, Guan J, Liu C-C, Qi J. Pet image denoising using a deep neural network through fine tuning. IEEE Trans Radiat Plasma Med Sci. 2018;3(2):153–61.PubMedPubMedCentralCrossRef Gong K, Guan J, Liu C-C, Qi J. Pet image denoising using a deep neural network through fine tuning. IEEE Trans Radiat Plasma Med Sci. 2018;3(2):153–61.PubMedPubMedCentralCrossRef
100.
go back to reference Chaudhari AS, Mittra E, Davidzon GA, Gulaka P, Gandhi H, Brown A, Zhang T, Srinivas S, Gong E, Zaharchuk G, et al. Low-count whole-body PET with deep learning in a multicenter and externally validated study. npj Digit Med. 2021;4(1):1–11. Chaudhari AS, Mittra E, Davidzon GA, Gulaka P, Gandhi H, Brown A, Zhang T, Srinivas S, Gong E, Zaharchuk G, et al. Low-count whole-body PET with deep learning in a multicenter and externally validated study. npj Digit Med. 2021;4(1):1–11.
101.
go back to reference Mehranian A, Wollenweber SD, Walker MD, Bradley KM, Fielding PA, Su K-H, Johnsen R, Kotasidis F, Jansen FP, McGowan DR. Image enhancement of whole-body oncology [18 F]-FDG pet scans using deep neural networks to reduce noise. Eur J Nucl Med Mol Imaging. 2021. Mehranian A, Wollenweber SD, Walker MD, Bradley KM, Fielding PA, Su K-H, Johnsen R, Kotasidis F, Jansen FP, McGowan DR. Image enhancement of whole-body oncology [18 F]-FDG pet scans using deep neural networks to reduce noise. Eur J Nucl Med Mol Imaging. 2021.
102.
go back to reference Sudarshan VP, Upadhyay U, Egan GF, Chen Z, Awate SP. Towards lower-dose pet using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data. Med Image Anal. 2021;73:102187.PubMedCrossRef Sudarshan VP, Upadhyay U, Egan GF, Chen Z, Awate SP. Towards lower-dose pet using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data. Med Image Anal. 2021;73:102187.PubMedCrossRef
103.
go back to reference Ulyanov D, Vedaldi A, Lempitsky VS. Deep image prior. CoRR, vol. abs/1711.10925, 2017. Ulyanov D, Vedaldi A, Lempitsky VS. Deep image prior. CoRR, vol. abs/1711.10925, 2017.
104.
go back to reference Cui J, Gong K, Guo N, Wu C, Meng X, Kim K, Zheng K, Wu Z, Fu L, Xu B, Zhu Z, Tian J, Liu H, Li Q. PET image denoising using unsupervised deep learning. Eur J Nucl Med Mol Imaging. 2019;46(13):2780–9.PubMedPubMedCentralCrossRef Cui J, Gong K, Guo N, Wu C, Meng X, Kim K, Zheng K, Wu Z, Fu L, Xu B, Zhu Z, Tian J, Liu H, Li Q. PET image denoising using unsupervised deep learning. Eur J Nucl Med Mol Imaging. 2019;46(13):2780–9.PubMedPubMedCentralCrossRef
105.
go back to reference Heckel R, Hand P. Deep decoder: concise image representations from untrained non-convolutional networks. arXiv preprint arXiv:1810.03982, 2018. Heckel R, Hand P. Deep decoder: concise image representations from untrained non-convolutional networks. arXiv preprint arXiv:1810.03982, 2018.
106.
go back to reference Cui J, Gong K, Guo N, Wu C, Kim K, Liu H, Li Q. Populational and individual information based PET image denoising using conditional unsupervised learning. Phys Med Biol. 2021;66(15):155001.CrossRef Cui J, Gong K, Guo N, Wu C, Kim K, Liu H, Li Q. Populational and individual information based PET image denoising using conditional unsupervised learning. Phys Med Biol. 2021;66(15):155001.CrossRef
107.
go back to reference Xue S, Guo R, Bohn KP, Matzke J, Viscione M, Alberts I, Meng H, Sun C, Zhang M, Zhang M, et al. A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET. Eur J Nucl Med Mol Imaging. 2021;1–14. Xue S, Guo R, Bohn KP, Matzke J, Viscione M, Alberts I, Meng H, Sun C, Zhang M, Zhang M, et al. A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET. Eur J Nucl Med Mol Imaging. 2021;1–14.
108.
go back to reference Chen Z, Jamadar SD, Li S, Sforazzini F, Baran J, Ferris N, Shah NJ, Egan GF. From simultaneous to synergistic MR-PET brain imaging: a review of hybrid MR-PET imaging methodologies. Hum Brain Mapp. 2018;39(12):5126–44.PubMedPubMedCentralCrossRef Chen Z, Jamadar SD, Li S, Sforazzini F, Baran J, Ferris N, Shah NJ, Egan GF. From simultaneous to synergistic MR-PET brain imaging: a review of hybrid MR-PET imaging methodologies. Hum Brain Mapp. 2018;39(12):5126–44.PubMedPubMedCentralCrossRef
109.
go back to reference Onishi Y, Hashimoto F, Ote K, Ohba H, Ota R, Yoshikawa E, Ouchi Y. Anatomical-guided attention enhances unsupervised PET image denoising performance. Med Image Anal. 2021;74:102226.PubMedCrossRef Onishi Y, Hashimoto F, Ote K, Ohba H, Ota R, Yoshikawa E, Ouchi Y. Anatomical-guided attention enhances unsupervised PET image denoising performance. Med Image Anal. 2021;74:102226.PubMedCrossRef
110.
go back to reference Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Salazar J, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. The Alzheimer’s disease neuroimaging initiative 3: continued innovation for clinical trial improvement. Alzheimer’s Dementia. 2017;13(5):561–71.PubMedCrossRef Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Salazar J, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. The Alzheimer’s disease neuroimaging initiative 3: continued innovation for clinical trial improvement. Alzheimer’s Dementia. 2017;13(5):561–71.PubMedCrossRef
111.
go back to reference Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. arXiv preprint arXiv:1406.2661, 2014. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. arXiv preprint arXiv:1406.2661, 2014.
112.
go back to reference Hashimoto F, Ohba H, Ote K, Teramoto A, Tsukada H. Dynamic PET image denoising using deep convolutional neural networks without prior training datasets. IEEE Access. 2019;7:96594–603.CrossRef Hashimoto F, Ohba H, Ote K, Teramoto A, Tsukada H. Dynamic PET image denoising using deep convolutional neural networks without prior training datasets. IEEE Access. 2019;7:96594–603.CrossRef
113.
go back to reference Hashimoto F, Ohba H, Ote K, Kakimoto A, Tsukada H, Ouchi Y. 4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network. Phys Med Biol. 2021;66(1). Hashimoto F, Ohba H, Ote K, Kakimoto A, Tsukada H, Ouchi Y. 4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network. Phys Med Biol. 2021;66(1).
114.
go back to reference Sun H, Peng L, Zhang H, He Y, Cao S, Lu L. Dynamic pet image denoising using deep image prior combined with regularization by denoising. IEEE Access. 2021;9:52378–92.CrossRef Sun H, Peng L, Zhang H, He Y, Cao S, Lu L. Dynamic pet image denoising using deep image prior combined with regularization by denoising. IEEE Access. 2021;9:52378–92.CrossRef
115.
go back to reference He Y, Cao S, Zhang H, Sun H, Wang F, Zhu H, Lv W, Lu L. Dynamic pet image denoising with deep learning-based joint filtering. IEEE Access. 2021;9:41998–2012.CrossRef He Y, Cao S, Zhang H, Sun H, Wang F, Zhu H, Lv W, Lu L. Dynamic pet image denoising with deep learning-based joint filtering. IEEE Access. 2021;9:41998–2012.CrossRef
116.
go back to reference Cui J, Liu X, Wang Y, Liu H. Deep reconstruction model for dynamic pet images. PLoS ONE. 2017;12(9). Cui J, Liu X, Wang Y, Liu H. Deep reconstruction model for dynamic pet images. PLoS ONE. 2017;12(9).
118.
go back to reference Hoppe E, Körzdörfer G, Würfl T, Wetzl J, Lugauer F, Pfeuffer J, Maier AK. “Deep learning for magnetic resonance fingerprinting: A new approach for predicting quantitative parameter values from time series. GMDS. 2017;1:202–6. Hoppe E, Körzdörfer G, Würfl T, Wetzl J, Lugauer F, Pfeuffer J, Maier AK. “Deep learning for magnetic resonance fingerprinting: A new approach for predicting quantitative parameter values from time series. GMDS. 2017;1:202–6.
119.
go back to reference P. Virtue, S. Yu, and M. Lustig, “Better than real: Complex-valued neural nets for MRI fingerprinting,” vol. 2017, pp. 3953–3957, 2018. P. Virtue, S. Yu, and M. Lustig, “Better than real: Complex-valued neural nets for MRI fingerprinting,” vol. 2017, pp. 3953–3957, 2018.
120.
go back to reference Klyuzhin I, Cheng J-C, Bevington C, Sossi V. Use of a tracer-specific deep artificial neural net to denoise dynamic pet images. IEEE Trans Med Imaging. 2020;39(2):366–76.PubMedCrossRef Klyuzhin I, Cheng J-C, Bevington C, Sossi V. Use of a tracer-specific deep artificial neural net to denoise dynamic pet images. IEEE Trans Med Imaging. 2020;39(2):366–76.PubMedCrossRef
121.
go back to reference Cheng (kevin) J-C, Klyuzhin I, Bevington C, Cheng J-C, Sossi V. Detection of transient neurotransmitter response using personalized neural networks. Phys Med Biol. 2020;65(23). Cheng (kevin) J-C, Klyuzhin I, Bevington C, Cheng J-C, Sossi V. Detection of transient neurotransmitter response using personalized neural networks. Phys Med Biol. 2020;65(23).
122.
go back to reference Wang B, Ruan D, Liu H. Noninvasive estimation of macro-parameters by deep learning. IEEE Trans Radiat Plasma Med Sci. 2020;4(6):684–95.CrossRef Wang B, Ruan D, Liu H. Noninvasive estimation of macro-parameters by deep learning. IEEE Trans Radiat Plasma Med Sci. 2020;4(6):684–95.CrossRef
123.
go back to reference Angelis G, Fuller O, Gillam J, Meikle S. Denoising non-steady state dynamic PET data using a feed-forward neural network. Phys Med Biol. 2021;66(3). Angelis G, Fuller O, Gillam J, Meikle S. Denoising non-steady state dynamic PET data using a feed-forward neural network. Phys Med Biol. 2021;66(3).
124.
go back to reference Morris ED, Yoder KK, Wang C, Normandin MD, Zheng Q-H, Mock B, Raymond FM Jr, Froehlich JC. ntPET: a new application of PET imaging for characterizing the kinetics of endogenous neurotransmitter release. Mol Imaging. 2005;4(4):7290–2005.CrossRef Morris ED, Yoder KK, Wang C, Normandin MD, Zheng Q-H, Mock B, Raymond FM Jr, Froehlich JC. ntPET: a new application of PET imaging for characterizing the kinetics of endogenous neurotransmitter release. Mol Imaging. 2005;4(4):7290–2005.CrossRef
125.
go back to reference Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;38(2):295–307.PubMedCrossRef Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;38(2):295–307.PubMedCrossRef
126.
go back to reference Lim B, Son S, Kim H, Nah S, Lee KM. Enhanced deep residual networks for single image super-resolution. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1132–1140, 2017. Lim B, Son S, Kim H, Nah S, Lee KM. Enhanced deep residual networks for single image super-resolution. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1132–1140, 2017.
127.
go back to reference Hong X, Zan Y, Weng F, Tao W, Peng Q, Huang Q. Enhancing the image quality via transferred deep residual learning of coarse PET sinograms. IEEE Trans Med Imaging. 2018;37(10):2322–32.PubMedCrossRef Hong X, Zan Y, Weng F, Tao W, Peng Q, Huang Q. Enhancing the image quality via transferred deep residual learning of coarse PET sinograms. IEEE Trans Med Imaging. 2018;37(10):2322–32.PubMedCrossRef
128.
129.
130.
go back to reference Shiri I, Leung K, Geramifar P, Ghafarian P, Oveisi M, Ay MR, Rahmim A. PSFNET: ultrafast generation of PSF-modelled-like PET images using deep convolutional neural network. J Nucl Med. 2019;60(supplement 1):1369–1369. Shiri I, Leung K, Geramifar P, Ghafarian P, Oveisi M, Ay MR, Rahmim A. PSFNET: ultrafast generation of PSF-modelled-like PET images using deep convolutional neural network. J Nucl Med. 2019;60(supplement 1):1369–1369.
131.
go back to reference da Costa-Luis CO, Reader AJ. Deep learning for suppression of resolution-recovery artefacts in mlem pet image reconstruction. In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 1–3, IEEE, 2017. da Costa-Luis CO, Reader AJ. Deep learning for suppression of resolution-recovery artefacts in mlem pet image reconstruction. In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 1–3, IEEE, 2017.
132.
go back to reference Schramm G, Rigie D, Vahle T, Rezaei A, Van Laere K, Shepherd T, Nuyts J, Boada F. Approximating anatomically-guided pet reconstruction in image space using a convolutional neural network. NeuroImage. 2021;224. Schramm G, Rigie D, Vahle T, Rezaei A, Van Laere K, Shepherd T, Nuyts J, Boada F. Approximating anatomically-guided pet reconstruction in image space using a convolutional neural network. NeuroImage. 2021;224.
133.
go back to reference Antun V, Renna F, Poon C, Adcock B, Hansen AC. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc Natl Acad Sci. 2020;117(48):30088–95.PubMedPubMedCentralCrossRef Antun V, Renna F, Poon C, Adcock B, Hansen AC. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc Natl Acad Sci. 2020;117(48):30088–95.PubMedPubMedCentralCrossRef
134.
go back to reference Gal Y, Ghahramani Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning, pp. 1050–1059, PMLR; 2016. Gal Y, Ghahramani Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning, pp. 1050–1059, PMLR; 2016.
135.
go back to reference Begoli E, Bhattacharya T, Kusnezov D. The need for uncertainty quantification in machine-assisted medical decision making. Nat Mach Intell. 2019;1(1):20–3.CrossRef Begoli E, Bhattacharya T, Kusnezov D. The need for uncertainty quantification in machine-assisted medical decision making. Nat Mach Intell. 2019;1(1):20–3.CrossRef
136.
go back to reference Schwarz CG, Kremers WK, Therneau TM, Sharp RR, Gunter JL, Vemuri P, Arani A, Spychalla AJ, Kantarci K, Knopman DS, Petersen RC, Jack CR. Identification of anonymous MRI research participants with face-recognition software. N Engl J Med. 2019;381(17):1684–6.PubMedPubMedCentralCrossRef Schwarz CG, Kremers WK, Therneau TM, Sharp RR, Gunter JL, Vemuri P, Arani A, Spychalla AJ, Kantarci K, Knopman DS, Petersen RC, Jack CR. Identification of anonymous MRI research participants with face-recognition software. N Engl J Med. 2019;381(17):1684–6.PubMedPubMedCentralCrossRef
137.
go back to reference Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):1–7.CrossRef Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):1–7.CrossRef
138.
go back to reference Paredes-Pacheco J, López-González FJ, Silva-Rodríguez J, Efthimiou N, Ninerola-Baizán A, Ruibal A, Róé- Vellvé N, Aguiar P. SimPET—an open online platform for the Monte Carlo simulation of realistic brain PET data. validation for 18f-fdg scans. Med Phys. 2021;48(5):2482–93.PubMedCrossRef Paredes-Pacheco J, López-González FJ, Silva-Rodríguez J, Efthimiou N, Ninerola-Baizán A, Ruibal A, Róé- Vellvé N, Aguiar P. SimPET—an open online platform for the Monte Carlo simulation of realistic brain PET data. validation for 18f-fdg scans. Med Phys. 2021;48(5):2482–93.PubMedCrossRef
139.
go back to reference Scheins JJ, Lenz M, Pietrzyk U, Shah NJ, Lerche C. High-throughput, accurate Monte Carlo simulation on CPU hardware for PET applications. Phys Med Biol. 2021;66(18):185001.CrossRef Scheins JJ, Lenz M, Pietrzyk U, Shah NJ, Lerche C. High-throughput, accurate Monte Carlo simulation on CPU hardware for PET applications. Phys Med Biol. 2021;66(18):185001.CrossRef
Metadata
Title
Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement
Authors
Cameron Dennis Pain
Gary F. Egan
Zhaolin Chen
Publication date
21-03-2022
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 9/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-022-05746-4

Other articles of this Issue 9/2022

European Journal of Nuclear Medicine and Molecular Imaging 9/2022 Go to the issue