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Record W1995428248 · doi:10.1118/1.3613549

TH‐C‐220‐06: Optimization of a Retrospective Respiratory‐Gated Micro‐Computed Tomography Technique for Free‐Breathing Rats

2011· article· en· W1995428248 on OpenAlexaff
Nancy L. Ford, Kwok L. Yip, Darren Yohan, David W. Holdsworth, Maria Drangova

Bibliographic record

VenueMedical Physics · 2011
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsVoxelNuclear medicineImage qualityImage registrationBreathingProjection (relational algebra)Partial volumeTidal volumeIterative reconstructionCone beam computed tomographyMedicineMathematicsComputed tomographyRespiratory systemComputer scienceArtificial intelligenceRadiologyAlgorithmAnatomyImage (mathematics)

Abstract

fetched live from OpenAlex

Purpose: The objective of this study is to optimize a retrospective respiratory‐gated micro‐CT protocol for free‐breathing rats to maintain image quality while reducing scan time and x‐ray dose. Methods: Five male Sprague‐Dawley rats were anaesthetized with ketamine and xylazine. Micro‐CT images were acquired with a GE Locus Ultra scanner at 80 kVp, 50 mA, 10 gantry‐rotations, with a scan time of 50 s and 0.28 Gy entrance dose. The respiratory traces of the free‐breathing rats were recorded throughout the scans and used to retrospectively sort the projection views that were acquired during peak inspiration and end expiration. 3D images were reconstructed with an isotropic voxel spacing of 0.15 mm. To assess the impact of missing projection views, we reconstructed images using all 10 gantry‐rotations and progressively fewer rotations (down to 3). We reconstructed a second set of images where the missing views were filled with the projection that was closest to the desired phase. Image‐based analysis of the image noise and missing view artefacts were indicators of the image quality. The physiological data (lung volume, CT density, functional residual capacity and tidal volume) were also computed. Results: By adding nearly in‐phase projections, the missing view artefacts were completely eliminated and the image noise was reduced. Measured values of lung volume, lung density and airway volume were stable for 5 or more gantry‐ rotations. Filling the projections using nearly in‐phase views resulted in lower measured values for tidal volume (1.26 mL +/− 0.22 mL in‐phase, 0.89+/− 0.17 mL nearly in‐phase). Conclusions: A 25 s imaging protocol would provide adequate physiological measurements at both respiratory phases for in‐phase reconstructions with reduced image quality. An image containing 50% of in‐phase views (remaining views are nearly in‐phase) is required to measure lung volume, 90% for airway volume and >65% for tidal volume.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.298
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2011
Admission routes1
Has abstractyes

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