Paper Citation Record · LEDGER

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT

As of 23 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2501.05085.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.05085 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:25:08.378911Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T02:30:39.984590Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a8e40a8-fae2-472a-99e6-f1d313f43662 · outbound

This paper cites Alara: is there a cause for alarm? reducing radiation risks from computed tomography scanning in children.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Alara: is there a cause for alarm? reducing radiation risks from computed tomography scanning in children

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:09.014130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 52beb8ee-79cc-4135-a214-d80fb7c0e9b3 · outbound

This paper cites Modelling the physics in the iterative reconstruction for transmission computed tomography.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Modelling the physics in the iterative reconstruction for transmission computed tomography

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.997406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e153daf8-075a-4193-b66b-22a549c17b3d · outbound

This paper cites Algorithm to extend reconstruction field-of-view.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Algorithm to extend reconstruction field-of-view

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.981434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d6872bfe-f167-444c-bf00-526883ce33f9 · outbound

This paper cites A novel reconstruction algorithm to extend the ct scan field-of-view.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT A novel reconstruction algorithm to extend the ct scan field-of-view

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.964422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 491b3789-3be3-4f6c-b18f-fa26eb5acbe7 · outbound

This paper cites Compressed sensing based interior tomography.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Compressed sensing based interior tomography

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.947509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 27dd4a0d-a52b-460b-895a-fa774efe20db · outbound

This paper cites Interior tomography using 1d generalized total variation.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Interior tomography using 1d generalized total variation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.932668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 86c608e3-81f2-4693-8b34-dd8121a5b7e4 · outbound

This paper cites Interior tomography using 1d generalized total variation.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Interior tomography using 1d generalized total variation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.917071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d92467ee-3250-476f-b9e6-b720928f32cf · outbound

This paper cites Why do commercial ct scanners still employ traditional, filtered back-projection for image reconstruction? Inverse problems, 25(12):123009, 2009.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Why do commercial ct scanners still employ traditional, filtered back-projection for image reconstruction? Inverse problems, 25(12):123009, 2009

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.901158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1185d84d-035a-4e6c-a2c8-7ac4229a3f40 · outbound

This paper cites Evaluation of sparse-view reconstruction from flat-panel-detector cone- beam ct.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Evaluation of sparse-view reconstruction from flat-panel-detector cone- beam ct

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.884873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f170a19b-3c1e-4668-86f7-ab4c8de2650c · outbound

This paper cites Few-view image reconstruction with dual dictionaries.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Few-view image reconstruction with dual dictionaries

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.865345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9bc38e23-5fd4-4246-aa38-60efb5bae4fc · outbound

This paper cites Sparse-view spectral ct reconstruction using spectral patch-based low-rank penalty.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Sparse-view spectral ct reconstruction using spectral patch-based low-rank penalty

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.849326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 642f4ffc-37ac-4dec-acb6-bb3a52a95b4e · outbound

This paper cites Effects of sparse sampling schemes on image quality in low-dose ct.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Effects of sparse sampling schemes on image quality in low-dose ct

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.833474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c837ca2c-4b11-4881-8c3b-bb9ea50c4153 · outbound

This paper cites Iterative reconstruction methods in x-ray ct.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Iterative reconstruction methods in x-ray ct

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.817441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 16ded355-a238-488b-acfa-f7f1f4244ba8 · outbound

This paper cites A splitting-based iterative algorithm for accelerated statistical x-ray ct reconstruction.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT A splitting-based iterative algorithm for accelerated statistical x-ray ct reconstruction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.801410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 36e69995-c934-4f0a-8d50-11622efe8f3c · outbound

This paper cites Deep convolutional neural network for inverse problems in imaging.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Deep convolutional neural network for inverse problems in imaging

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.783928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 17f2fe4d-7bcb-4bed-ba3a-b6d3cbf3a1ce · outbound

This paper cites Deep-neural- network-based sinogram synthesis for sparse-view ct image reconstruction.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Deep-neural- network-based sinogram synthesis for sparse-view ct image reconstruction

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.767052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 85b367c0-e8c9-4d24-8a8f-d9b7bbce5834 · outbound

This paper cites Framing u-net via deep convolutional framelets: Application to sparse-view ct.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Framing u-net via deep convolutional framelets: Application to sparse-view ct

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.750764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 951dd100-21d6-498e-8ceb-5992244f69ad · outbound

This paper cites Generative adversarial networks for noise reduction in low-dose ct.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Generative adversarial networks for noise reduction in low-dose ct

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.734787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7d21627b-ae49-401d-8b80-8a8c74b28a1e · outbound

This paper cites Low-dose ct with a residual encoder-decoder convolutional neural network.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Low-dose ct with a residual encoder-decoder convolutional neural network

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.719355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f31af635-bac0-42e3-8dbf-c75a742bfcc1 · outbound

This paper cites A deep convolutional neural network using directional wavelets for low-dose x-ray ct reconstruction.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT A deep convolutional neural network using directional wavelets for low-dose x-ray ct reconstruction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.702711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aac7f687-e0bf-4773-b7e5-94cb78daaeac · outbound

This paper cites Deep Learning Interior Tomography for Region-of-Interest Reconstruction.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Deep Learning Interior Tomography for Region-of-Interest Reconstruction

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:25:08.425116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1124e262-a727-4761-90fb-cbabda10fdf2 · outbound

This paper cites One network to solve all rois: Deep learning ct for any roi using differentiated backprojection.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT One network to solve all rois: Deep learning ct for any roi using differentiated backprojection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.686741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4203692b-683b-4610-9e2c-bb63283f87ee · outbound

This paper cites Deep convolutional framelets: A general deep learning framework for inverse problems.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Deep convolutional framelets: A general deep learning framework for inverse problems

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.671252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ad02aad9-0ae1-4269-98ee-1fcd5515e065 · outbound

This paper cites Computed tomography: principles, design, artifacts, and recent advances, volume.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Computed tomography: principles, design, artifacts, and recent advances, volume

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.655406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d047252d-0532-4e56-9bca-e51510277419 · outbound

This paper cites Noise reduction with low dose ct data based on a modified rof model.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Noise reduction with low dose ct data based on a modified rof model

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.640146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4c5d4d15-194a-4512-9aa2-7408aa2bb2b5 · outbound

This paper cites Understanding geometry of encoder-decoder cnns.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Understanding geometry of encoder-decoder cnns

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.623476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4d70dbb3-2269-49ad-8cac-c7e7a94d4f93 · outbound

This paper cites Annihilating filter-based low-rank hankel matrix approach for image inpainting.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Annihilating filter-based low-rank hankel matrix approach for image inpainting

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.604778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 307c502f-ae09-4780-ac29-0482d5b7c448 · outbound

This paper cites Sparse and low-rank decomposition of a hankel structured matrix for impulse noise removal.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Sparse and low-rank decomposition of a hankel structured matrix for impulse noise removal

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.588225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5a081526-352c-4805-84d4-ef48870de307 · outbound

This paper cites A general framework for compressed sensing and parallel mri using annihilating filter based low-rank hankel matrix.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT A general framework for compressed sensing and parallel mri using annihilating filter based low-rank hankel matrix

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.570865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 009d1ee7-b772-4caa-bf27-ea5f46948113 · outbound

This paper cites Acceleration of mr parameter mapping using annihilating filter-based low rank hankel matrix (aloha).

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Acceleration of mr parameter mapping using annihilating filter-based low rank hankel matrix (aloha)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.554456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0aac7230-a114-4077-ade5-aaba0bce70ee · outbound

This paper cites Reference-free single-pass epi n yquist ghost correction using annihilating filter-based low rank h ankel matrix (aloha).

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Reference-free single-pass epi n yquist ghost correction using annihilating filter-based low rank h ankel matrix (aloha)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.537905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fb0f4a83-ac26-4af6-82bb-8381e79a1865 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT U-net: Convolutional networks for biomedical image segmentation

Reference 32

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Source-reported events for the cited work

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Observation 031eff02-184d-4dd5-8c8a-7ee15a4cbf70 · outbound

This paper cites Tu-fg-207a-04: Overview of the low dose ct grand challenge.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Tu-fg-207a-04: Overview of the low dose ct grand challenge

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.508869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f364d4e4-c522-458c-aaef-c76ae0d5063b · outbound

This paper cites Development and validation of a practical lower-dose-simulation tool for optimizing computed tomography scan protocols.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Development and validation of a practical lower-dose-simulation tool for optimizing computed tomography scan protocols

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.491708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 82473b5e-00bd-4437-928c-f1cf7b0a24c0 · outbound

This paper cites A dual-domain cnn-based network for ct reconstruction.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT A dual-domain cnn-based network for ct reconstruction

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.475607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3e318983-7f6c-4682-ad8f-8cc852455e94 · outbound

This paper cites Kiki-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Kiki-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.458855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3252fd5f-1566-4037-b89e-c57869e28af6 · outbound

This paper cites Low-dose ct reconstruction using spatially encoded nonlocal penalty.

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT Low-dose ct reconstruction using spatially encoded nonlocal penalty

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:08.442220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation abac39a3-4dd0-48df-b37a-4eabb6891f1a · inbound

FORCE-Interior: Measurement-Consistent Adaptation of a Poisson-Flow Generative Prior for Interior CT cites this paper.

FORCE-Interior: Measurement-Consistent Adaptation of a Poisson-Flow Generative Prior for Interior CT End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T02:30:39.984590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.