254 related articles for article (PubMed ID: 35697017)
1. A personalized deep learning denoising strategy for low-count PET images.
Liu Q; Liu H; Mirian N; Ren S; Viswanath V; Karp J; Surti S; Liu C
Phys Med Biol; 2022 Jul; 67(14):. PubMed ID: 35697017
[No Abstract] [Full Text] [Related]
2. Performance of a deep learning-based CT image denoising method: Generalizability over dose, reconstruction kernel, and slice thickness.
Zeng R; Lin CY; Li Q; Jiang L; Skopec M; Fessler JA; Myers KJ
Med Phys; 2022 Feb; 49(2):836-853. PubMed ID: 34954845
[TBL] [Abstract][Full Text] [Related]
3. Dose reduction and image enhancement in micro-CT using deep learning.
Muller FM; Maebe J; Vanhove C; Vandenberghe S
Med Phys; 2023 Sep; 50(9):5643-5656. PubMed ID: 36994779
[TBL] [Abstract][Full Text] [Related]
4. Higher SNR PET image prediction using a deep learning model and MRI image.
Liu CC; Qi J
Phys Med Biol; 2019 May; 64(11):115004. PubMed ID: 30844784
[TBL] [Abstract][Full Text] [Related]
5. Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.
Dutta K; Laforest R; Luo J; Jha AK; Shoghi KI
Med Phys; 2024 Jun; 51(6):4324-4339. PubMed ID: 38710222
[TBL] [Abstract][Full Text] [Related]
6. Cross noise level PET denoising with continuous adversarial domain generalization.
Liu X; Vafay Eslahi S; Marin T; Tiss A; Chemli Y; Huang Y; Johnson KA; El Fakhri G; Ouyang J
Phys Med Biol; 2024 Apr; 69(8):. PubMed ID: 38484401
[No Abstract] [Full Text] [Related]
7. Virtual high-count PET image generation using a deep learning method.
Liu J; Ren S; Wang R; Mirian N; Tsai YJ; Kulon M; Pucar D; Chen MK; Liu C
Med Phys; 2022 Sep; 49(9):5830-5840. PubMed ID: 35880541
[TBL] [Abstract][Full Text] [Related]
8. Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain.
Manoj Doss KK; Chen JC
Med Phys; 2024 Jan; 51(1):209-223. PubMed ID: 37966121
[TBL] [Abstract][Full Text] [Related]
9. A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images.
Kim B; Han M; Shim H; Baek J
Med Phys; 2019 Sep; 46(9):3906-3923. PubMed ID: 31306488
[TBL] [Abstract][Full Text] [Related]
10. Noise2Void: unsupervised denoising of PET images.
Song TA; Yang F; Dutta J
Phys Med Biol; 2021 Nov; 66(21):. PubMed ID: 34663767
[No Abstract] [Full Text] [Related]
11. Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification.
Liu Q; Tsai YJ; Gallezot JD; Guo X; Chen MK; Pucar D; Young C; Panin V; Casey M; Miao T; Xie H; Chen X; Zhou B; Carson R; Liu C
Med Image Anal; 2024 Jul; 95():103180. PubMed ID: 38657423
[TBL] [Abstract][Full Text] [Related]
12. Pediatric evaluations for deep learning CT denoising.
Nelson BJ; Kc P; Badal A; Jiang L; Masters SC; Zeng R
Med Phys; 2024 Feb; 51(2):978-990. PubMed ID: 38127330
[TBL] [Abstract][Full Text] [Related]
13. An investigation of quantitative accuracy for deep learning based denoising in oncological PET.
Lu W; Onofrey JA; Lu Y; Shi L; Ma T; Liu Y; Liu C
Phys Med Biol; 2019 Aug; 64(16):165019. PubMed ID: 31307019
[TBL] [Abstract][Full Text] [Related]
14. FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising.
Zhou B; Xie H; Liu Q; Chen X; Guo X; Feng Z; Hou J; Zhou SK; Li B; Rominger A; Shi K; Duncan JS; Liu C
Med Image Anal; 2023 Dec; 90():102993. PubMed ID: 37827110
[TBL] [Abstract][Full Text] [Related]
15. Learning low-dose CT degradation from unpaired data with flow-based model.
Liu X; Liang X; Deng L; Tan S; Xie Y
Med Phys; 2022 Dec; 49(12):7516-7530. PubMed ID: 35880375
[TBL] [Abstract][Full Text] [Related]
16. Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET.
Liu H; Wu J; Lu W; Onofrey JA; Liu YH; Liu C
Phys Med Biol; 2020 Sep; 65(18):185006. PubMed ID: 32924973
[TBL] [Abstract][Full Text] [Related]
17. Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-β levels in Alzheimer's disease patients using a deep-learning-based denoising algorithm.
Peng Z; Ni M; Shan H; Lu Y; Li Y; Zhang Y; Pei X; Chen Z; Xie Q; Wang S; Xu XG
Comput Biol Med; 2021 Nov; 138():104919. PubMed ID: 34655898
[TBL] [Abstract][Full Text] [Related]
18. Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging.
Liu J; Yang Y; Wernick MN; Pretorius PH; King MA
Med Phys; 2021 Jan; 48(1):156-168. PubMed ID: 33145782
[TBL] [Abstract][Full Text] [Related]
19. Spatial adaptive and transformer fusion network (STFNet) for low-count PET blind denoising with MRI.
Zhang L; Xiao Z; Zhou C; Yuan J; He Q; Yang Y; Liu X; Liang D; Zheng H; Fan W; Zhang X; Hu Z
Med Phys; 2022 Jan; 49(1):343-356. PubMed ID: 34796526
[TBL] [Abstract][Full Text] [Related]
20. Need for objective task-based evaluation of deep learning-based denoising methods: A study in the context of myocardial perfusion SPECT.
Yu Z; Rahman MA; Laforest R; Schindler TH; Gropler RJ; Wahl RL; Siegel BA; Jha AK
Med Phys; 2023 Jul; 50(7):4122-4137. PubMed ID: 37010001
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
