Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-18T22:21:18.385516Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 0 inbound Pith citation observations for arXiv:2508.14950.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-18T22:21:18.385516Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
76 of 76 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 16841860-15e7-4fa2-96b4-82ab2e595550 · outbound
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Kappeler, S
Reference 1
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Keys, Cubic convolution interpolation for digital image processing, IEEE transactions on acoustics, speech, and signal processing 29 (6) (2003) 1153–1160
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Ledig, L
Reference 10
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Karnewar, O
Reference 13
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Liang, H
Reference 14
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Goodfellow, J
Reference 16
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI The relativistic discriminator: a key element missing from standard GAN
Reference 17
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Vinuesa, S
Reference 18
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Christensen-Jeffries, O
Reference 21
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Markl, A
Reference 22
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Stankovic, B
Reference 23
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Aristova, A
Reference 24
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Marlevi, J
Reference 25
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Hyodo, Y
Reference 26
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Schnell, C
Reference 27
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Aristova, J
Reference 28
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Gottwald, J
Reference 29
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Observation e609002c-d78f-42d0-8237-546d5dd828b7 · outbound
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Ferdian, A
Reference 31
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Ferdian, D
Reference 32
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
Reference 33
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Saitta, M
Reference 34
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Rutkowski, A
Reference 35
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Szajer, K
Reference 36
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI G ¨uemes, S
Reference 37
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
Reference 38
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
Reference 39
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Zhang, H
Reference 40
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Reference 41
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
Reference 42
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Reference 43
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Mescheder, A
Reference 44
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Zhang, P
Reference 46
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Schollenberger, N
Reference 47
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Ericsson, A
Reference 48
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
Reference 49
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Schollenberger, D
Reference 50
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Schnell, S
Reference 51
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
Reference 52
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Gulrajani, F
Reference 53
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Reference 54
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Ahrens, B
Reference 55
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
Reference 58
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Reference 59
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Face Super-Resolution Through Wasserstein GANs
Reference 60
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Jiang, Z
Reference 63
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Reference 64
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
Reference 65
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Unresolved cited work
Reference 66
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Observation bc7c249e-a380-4fe5-b38f-04810cd4b281 · outbound
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Kissas, Y
Reference 67
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Shone, N
Reference 68
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Reference 69
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Observation 355ce738-0de9-463f-a0a0-2a05f608883b · outbound
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Gormezano, S
Reference 70
Source-reported events for the cited work
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Observation fc451b35-2953-4216-8514-d50bd0aa14c7 · outbound
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Catapano, G
Reference 71
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Observation d8ab70ae-aa10-44e9-8a69-0e215578b5d3 · outbound
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Reference 72
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Reference 73
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Observation c064c96c-e2e7-41c9-b17c-7df710496985 · outbound
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Reference 74
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Observation e1bd1582-57ee-4026-84d3-5911b457b34c · outbound
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Takehara, 4d flow when and how?, La radiologia medica 125 (9) (2020) 838–850
Reference 75
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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b70fd2e2-0185-4f78-9ce2-c9f47d1aba67 · outbound
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI Deep learning for temporal super-resolution 4D Flow MRI
Reference 76
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