Paper Citation Record · LEDGER

Improving Quality Control Of MRI Images Using Synthetic Motion Data

As of 22 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2502.00160.

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

pith.paper-citation-record.v1
2502.00160 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:01:43.299734Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:01:43.175125Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T20:01:43.463557Z

Reference resolution

26 of 26 outbound references displayed

verified exact1
verified fuzzy23
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ddde95d1-3354-4051-b9cc-29fbcf132272 · outbound

This paper cites This modality is how- ever subject to multiple sources of artifacts [1], the most frequent being motion related artifacts.

Improving Quality Control Of MRI Images Using Synthetic Motion Data This modality is how- ever subject to multiple sources of artifacts [1], the most frequent being motion related artifacts

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.824831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5fc95cb0-2df4-44c1-a2ec-1fa5341a1c12 · outbound

This paper cites Improving Quality Control Of MRI Images Using Synthetic Motion Data.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Improving Quality Control Of MRI Images Using Synthetic Motion Data

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T20:01:43.469012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c062a855-6c10-4c26-ac0b-6c84c95235f7 · outbound

This paper cites The models performed similarly on the test set, indicating good generalization to unseen data (Figure 3).

Improving Quality Control Of MRI Images Using Synthetic Motion Data The models performed similarly on the test set, indicating good generalization to unseen data (Figure 3)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.809258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 440f5aea-ef4b-4838-a952-f93afbfddcfb · outbound

This paper cites Furthermore, models trained to quantify motion appear to learn meaningful embeddings that can be leveraged to per- form QC classification of real MRI data.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Furthermore, models trained to quantify motion appear to learn meaningful embeddings that can be leveraged to per- form QC classification of real MRI data

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.794321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 451b1578-638a-45df-95b9-808b13859544 · outbound

This paper cites Available QC datasets are highly unbalanced and use different and subjective scales, making it hard to train one model that fits all scenarios.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Available QC datasets are highly unbalanced and use different and subjective scales, making it hard to train one model that fits all scenarios

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.779415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e309e475-ffb2-423f-93a4-6ebf8baf3fe7 · outbound

This paper cites Approval was granted by the Research Ethics Committee of ´Ecole de technologie sup´erieure.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Approval was granted by the Research Ethics Committee of ´Ecole de technologie sup´erieure

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.764144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c5cbe91f-70d4-4735-9b78-9496fdffd6d0 · outbound

This paper cites Imaging artifacts at 3.0T,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Imaging artifacts at 3.0T,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.749147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ebd68649-e541-4d21-9216-2f0eb187b57c · outbound

This paper cites Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.734341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 02e4661f-77cf-4a7b-a4c3-667160e5ac90 · outbound

This paper cites V olumetric Navigators (vNavs) for Prospective Motion Correction and Selective Reacquisi- tion in Neuroanatomical MRI,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data V olumetric Navigators (vNavs) for Prospective Motion Correction and Selective Reacquisi- tion in Neuroanatomical MRI,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.719482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 49888c3d-14ba-471e-9144-b271440bd004 · outbound

This paper cites Quantifying MR head motion in the Rhineland Study – A robust method for population co- horts,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Quantifying MR head motion in the Rhineland Study – A robust method for population co- horts,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.704399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b0807bde-39f2-4896-a18c-53f1895fd28e · outbound

This paper cites Automated reference-free detec- tion of motion artifacts in magnetic resonance images,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Automated reference-free detec- tion of motion artifacts in magnetic resonance images,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.688925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 00a5de55-5fe3-440a-b231-f52772daab11 · outbound

This paper cites Motion Artifact Detection for T1- Weighted Brain MR Images Using Convolutional Neu- ral Networks,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Motion Artifact Detection for T1- Weighted Brain MR Images Using Convolutional Neu- ral Networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.672561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7ef9ecca-121e-4019-aef8-440396edb0c5 · outbound

This paper cites Automatic MR image quality evalua- tion using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Automatic MR image quality evalua- tion using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.657227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cf034215-25c3-4066-b588-043771101cb9 · outbound

This paper cites Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.642189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 55fd52f9-5011-4d3e-907d-4c27060c1641 · outbound

This paper cites Estimating Head Motion from MR- Images,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Estimating Head Motion from MR- Images,

Reference 15

Resolution: verified exact; raw_fallback, observed 2026-08-09T20:01:43.444988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f7779e78-4100-4b8a-b880-275e98474ebb · outbound

This paper cites Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.627553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e869311a-778f-4cfb-87bc-8998c41ecffd · outbound

This paper cites Classifying MRI motion severity using a stacked ensemble approach,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Classifying MRI motion severity using a stacked ensemble approach,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.612456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 171ab0f6-97fe-44bd-8ebe-d666820a15e9 · outbound

This paper cites Measuring Transformation Error by RMS Deviation,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Measuring Transformation Error by RMS Deviation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.597415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bc0ff4e0-d52d-4785-a58f-4fc85a6a295f · outbound

This paper cites An Introduction to the Human Con- nectome Project for Early Psychosis,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data An Introduction to the Human Con- nectome Project for Early Psychosis,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.582485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f377ba27-5b4f-48b8-a1b3-5ca64514249b · outbound

This paper cites Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study De- sign of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study De- sign of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.567499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 592d852c-3397-4abe-bc4d-157f4dc639f4 · outbound

This paper cites Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Stud- ies,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Stud- ies,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.552112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 17518210-b54b-4b32-8ff6-93d54340acf5 · outbound

This paper cites PyTorch: an imperative style, high- performance deep learning library,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data PyTorch: an imperative style, high- performance deep learning library,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.535522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e4b6706c-d42e-4bae-ac89-b09ef8b98ac8 · outbound

This paper cites PyTorchLightning/pytorch-lightning: 0.7.6 release,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data PyTorchLightning/pytorch-lightning: 0.7.6 release,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.519694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 09d1c736-0d76-45f1-9213-62b0447e675b · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

Improving Quality Control Of MRI Images Using Synthetic Motion Data MONAI: An open-source framework for deep learning in healthcare

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T20:01:43.289262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc39d43b-2d06-4ea8-80ff-4e6792563287 · outbound

This paper cites TorchIO: A Python library for ef- ficient loading, preprocessing, augmentation and patch- based sampling of medical images in deep learning,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data TorchIO: A Python library for ef- ficient loading, preprocessing, augmentation and patch- based sampling of medical images in deep learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.503895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c9a862c2-1a11-47eb-b81e-e26da797a093 · outbound

This paper cites MRI k-Space Motion Artefact Aug- mentation: Model Robustness and Task-Specific Uncer- tainty,.

Improving Quality Control Of MRI Images Using Synthetic Motion Data MRI k-Space Motion Artefact Aug- mentation: Model Robustness and Task-Specific Uncer- tainty,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:01:43.486765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 5fc95cb0-2df4-44c1-a2ec-1fa5341a1c12 · inbound