March 1, 2025 at 2:50 PM
March 9, 2025 at 1:26 PM
April 17, 2025 at 5:51 PM
na was meint ihr wer scheisst heut ins sinogram
March 10, 2026 at 2:14 PM
na was meint ihr wer scheisst heut ins sinogram
es ist so schwierig sich leuten zu erklaeren wo das sinogram nicht mit ihnen redfet
July 31, 2026 at 5:31 PM
es ist so schwierig sich leuten zu erklaeren wo das sinogram nicht mit ihnen redfet
hab aus oben rechts unten rechts machts egmacht lolig oder
December 19, 2024 at 3:52 PM
hab aus oben rechts unten rechts machts egmacht lolig oder
Trivia:
- Mei-Phang is a lord of the Zhu clan, equal to Han-zhu.
Their last name is thus "Mei-Shan-zhu", but Shan+zhu makes one single sinogram pronounced "Phang/Pheng".
They thus have their own kamon (symbol) that is slightly different from Han-zhu.
- Mei-Phang is a lord of the Zhu clan, equal to Han-zhu.
Their last name is thus "Mei-Shan-zhu", but Shan+zhu makes one single sinogram pronounced "Phang/Pheng".
They thus have their own kamon (symbol) that is slightly different from Han-zhu.
February 22, 2025 at 6:10 PM
Trivia:
- Mei-Phang is a lord of the Zhu clan, equal to Han-zhu.
Their last name is thus "Mei-Shan-zhu", but Shan+zhu makes one single sinogram pronounced "Phang/Pheng".
They thus have their own kamon (symbol) that is slightly different from Han-zhu.
- Mei-Phang is a lord of the Zhu clan, equal to Han-zhu.
Their last name is thus "Mei-Shan-zhu", but Shan+zhu makes one single sinogram pronounced "Phang/Pheng".
They thus have their own kamon (symbol) that is slightly different from Han-zhu.
I don't know what is this sinogram-like-stuff like on the top left, too complicate to be a sinogram ;-)
the rest is Uyghur/Mongolian.
the rest is Uyghur/Mongolian.
June 7, 2025 at 6:04 PM
I don't know what is this sinogram-like-stuff like on the top left, too complicate to be a sinogram ;-)
the rest is Uyghur/Mongolian.
the rest is Uyghur/Mongolian.
I feel like we should repurpose 釖 as the single sinogram for US Dollar.
July 29, 2025 at 10:09 AM
I feel like we should repurpose 釖 as the single sinogram for US Dollar.
Ahá, takže když si dám na tričko sinogram třeba jater, bude to něco podobného?
February 3, 2024 at 1:39 PM
Ahá, takže když si dám na tričko sinogram třeba jater, bude to něco podobného?
why does the start point look so close to solution
because i can do tomographic reconstruction in my head the sinogram is speaking to ms
because i can do tomographic reconstruction in my head the sinogram is speaking to ms
March 11, 2026 at 6:27 PM
why does the start point look so close to solution
because i can do tomographic reconstruction in my head the sinogram is speaking to ms
because i can do tomographic reconstruction in my head the sinogram is speaking to ms
Ugh ... 🧐
(Unicode has 𭨐. Apparently something in an entirely different script ["西土字"] that turned into a quasi-sinogram?)
< 佛祖統紀 @ dl.ndl.go.jp/pid/2559763/...
(Unicode has 𭨐. Apparently something in an entirely different script ["西土字"] that turned into a quasi-sinogram?)
< 佛祖統紀 @ dl.ndl.go.jp/pid/2559763/...
September 19, 2025 at 8:27 PM
Ugh ... 🧐
(Unicode has 𭨐. Apparently something in an entirely different script ["西土字"] that turned into a quasi-sinogram?)
< 佛祖統紀 @ dl.ndl.go.jp/pid/2559763/...
(Unicode has 𭨐. Apparently something in an entirely different script ["西土字"] that turned into a quasi-sinogram?)
< 佛祖統紀 @ dl.ndl.go.jp/pid/2559763/...
Yuejie Liu, Alessandro Lupoli, David Uribe Gallo, Andreas Fischer, Felix Krahmer: Informed Sinogram Interpolation for Sparse View Reconstruction https://arxiv.org/abs/2609.18415 https://arxiv.org/pdf/2609.18415 https://arxiv.org/html/2609.18415
September 17, 2026 at 6:45 AM
Yuejie Liu, Alessandro Lupoli, David Uribe Gallo, Andreas Fischer, Felix Krahmer: Informed Sinogram Interpolation for Sparse View Reconstruction https://arxiv.org/abs/2609.18415 https://arxiv.org/pdf/2609.18415 https://arxiv.org/html/2609.18415
or inconsistent sinogram information. Moreover, the OSMM's unsupervised learning framework provides strong robustness and generalizability, adapting seamlessly to varying sparsity levels of CT sinograms. This ensures consistent and reliable [5/7 of https://arxiv.org/abs/2505.09985v1]
May 16, 2025 at 6:01 AM
or inconsistent sinogram information. Moreover, the OSMM's unsupervised learning framework provides strong robustness and generalizability, adapting seamlessly to varying sparsity levels of CT sinograms. This ensures consistent and reliable [5/7 of https://arxiv.org/abs/2505.09985v1]
focuses on sinogram inpainting. We leverage MR-SDEs, a variant of diffusion models that characterize the diffusion process with mean-reverting stochastic differential equations, to fill in missing angular data at the projection level. Furthermore, by [2/5 of https://arxiv.org/abs/2505.19385v1]
May 27, 2025 at 6:27 AM
focuses on sinogram inpainting. We leverage MR-SDEs, a variant of diffusion models that characterize the diffusion process with mean-reverting stochastic differential equations, to fill in missing angular data at the projection level. Furthermore, by [2/5 of https://arxiv.org/abs/2505.19385v1]
Yar, Peiris, Jamadar, Fornito, Chen: SinoDiff: Physics-Consistent Self-Supervised Diffusion for Unified Low-Dose to Standard-Dose PET Sinogram Recovery https://arxiv.org/abs/2608.11514 https://arxiv.org/pdf/2608.11514 https://arxiv.org/html/2608.11514
August 13, 2026 at 6:45 AM
Yar, Peiris, Jamadar, Fornito, Chen: SinoDiff: Physics-Consistent Self-Supervised Diffusion for Unified Low-Dose to Standard-Dose PET Sinogram Recovery https://arxiv.org/abs/2608.11514 https://arxiv.org/pdf/2608.11514 https://arxiv.org/html/2608.11514
NOTABLE ARTICLE, June 2026 #MedicalPhysics: Sinogram‐based flow estimation in computed tomography using a physics‐informed neural network: Impact of gantry rotation speed, X‐ray fluence & pulsed acquisition on accuracy
https://ow.ly/tJi850ZwCuu
Please join us in congratulating Jinyuxuan Guo et al
https://ow.ly/tJi850ZwCuu
Please join us in congratulating Jinyuxuan Guo et al
August 5, 2026 at 6:01 PM
NOTABLE ARTICLE, June 2026 #MedicalPhysics: Sinogram‐based flow estimation in computed tomography using a physics‐informed neural network: Impact of gantry rotation speed, X‐ray fluence & pulsed acquisition on accuracy
https://ow.ly/tJi850ZwCuu
Please join us in congratulating Jinyuxuan Guo et al
https://ow.ly/tJi850ZwCuu
Please join us in congratulating Jinyuxuan Guo et al
man when i saw the sinogram i knew i was gonna be good
July 10, 2026 at 1:16 PM
man when i saw the sinogram i knew i was gonna be good
Shi, Pelt and Batenburg: Multi-stage deep learning artifact reduction for parallel-beam computed tomography ##ArtifactReduction##DeepLearning##ComputedTomography@UniLeiden...##IUCrhttps://journals.iucr.org/paper?S1600577525000359
Multi-stage deep learning artifact reduction for parallel-beam computed tomography
An illustration of the synchrotron-based computed tomography pipeline is given, highlighting the three key processing stages — projection, sinogram and reconstruction — each incorporating multiple processing steps to enhance image quality.
journals.iucr.org
February 18, 2025 at 1:01 AM
Shi, Pelt and Batenburg: Multi-stage deep learning artifact reduction for parallel-beam computed tomography ##ArtifactReduction##DeepLearning##ComputedTomography@UniLeiden...##IUCrhttps://journals.iucr.org/paper?S1600577525000359
Rautio, Meaney, Latva-\o, Agrawal, Brix, Jayakody, Siltanen: Complex Wavelet-Based Sinogram Segmentation for Metal Artifact Reduction in Cone-Beam CT https://arxiv.org/abs/2602.14315 https://arxiv.org/pdf/2602.14315 https://arxiv.org/html/2602.14315
February 17, 2026 at 6:48 AM
Rautio, Meaney, Latva-\o, Agrawal, Brix, Jayakody, Siltanen: Complex Wavelet-Based Sinogram Segmentation for Metal Artifact Reduction in Cone-Beam CT https://arxiv.org/abs/2602.14315 https://arxiv.org/pdf/2602.14315 https://arxiv.org/html/2602.14315
Liyang Hu, Chong Chen: An efficient approach with theoretical guarantees to simultaneously reconstruct activity and attenuation sinogram for TOF-PET https://arxiv.org/abs/2510.13562 https://arxiv.org/pdf/2510.13562 https://arxiv.org/html/2510.13562
October 16, 2025 at 6:48 AM
Liyang Hu, Chong Chen: An efficient approach with theoretical guarantees to simultaneously reconstruct activity and attenuation sinogram for TOF-PET https://arxiv.org/abs/2510.13562 https://arxiv.org/pdf/2510.13562 https://arxiv.org/html/2510.13562
diffusion models. Such methods train a diffusion model (without sinogram data) on high-quality, but still noisy, PET images. In this work, we propose a simple method for generating subject-specific PET images from a dataset of multi-subject PET-MR [2/7 of https://arxiv.org/abs/2506.03804v1]
June 5, 2025 at 6:14 AM
diffusion models. Such methods train a diffusion model (without sinogram data) on high-quality, but still noisy, PET images. In this work, we propose a simple method for generating subject-specific PET images from a dataset of multi-subject PET-MR [2/7 of https://arxiv.org/abs/2506.03804v1]
removal in sinogram images was proposed. The proposed method classifies ring artifacts into single stripes and multiple stripes, which were identified and eliminated using median filtering and multiphase decomposition, respectively. A novel algorithm [3/5 of https://arxiv.org/abs/2505.19513v1]
May 27, 2025 at 6:14 AM
removal in sinogram images was proposed. The proposed method classifies ring artifacts into single stripes and multiple stripes, which were identified and eliminated using median filtering and multiphase decomposition, respectively. A novel algorithm [3/5 of https://arxiv.org/abs/2505.19513v1]
Zou, Qi, Zhang, Zhang, Wang, Zhang, Yao, Jiang: Ring artifacts correction method in x-ray computed tomography based on stripe classification and removal in sinogram images https://arxiv.org/abs/2505.19513 https://arxiv.org/pdf/2505.19513 https://arxiv.org/html/2505.19513
May 27, 2025 at 6:14 AM
Zou, Qi, Zhang, Zhang, Wang, Zhang, Yao, Jiang: Ring artifacts correction method in x-ray computed tomography based on stripe classification and removal in sinogram images https://arxiv.org/abs/2505.19513 https://arxiv.org/pdf/2505.19513 https://arxiv.org/html/2505.19513