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Fast Retrospectively Gated Quantitative Four-Dimensional (4D) Cardiac Micro Computed Tomography Imaging of Free-Breathing Mice

2007· article· en· W2004237183 on OpenAlexaff
Maria Drangova, Nancy L. Ford, Sarah A. Detombe, Andrew Wheatley, David W. Holdsworth

Bibliographic record

VenueInvestigative Radiology · 2007
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsNuclear medicineScannerVoxelBreathingCardiac PETMedicineCardiac cycleEjection fractionBiomedical engineeringRadiologyPositron emission tomographyPhysicsAnatomyCardiologyHeart failureOptics

Abstract

fetched live from OpenAlex

OBJECTIVE: We sought to demonstrate retrospectively gated dynamic 3D cardiac micro computed tomography (CT) of free-breathing mice. MATERIALS AND METHODS: Five C57Bl6 mice were scanned using a cone-beam scanner with a slip-ring-mounted flat-panel detector. After the injection of an intravascular iodinated contrast agent, projection images were acquired over the course of 50 seconds, while the scanner rotated through 10 complete rotations. The mouse respiratory and electrocardiogram signals were recorder simultaneously with image acquisition. After acquisition, the projection images were retrospectively sorted into projections belonging to different cardiac time points, occurring only during expiration. RESULTS: Dynamic 3D cardiac images, with isotropic 150-microm voxel spacing, were reconstructed at 12-millisecond intervals throughout the cardiac cycle in all mice. The average ejection fraction and cardiac output were 58.2+/-4.6% and 11.4+/-1.3 mL/min, respectively. The measured entrance dose for the entire scan was 28 cGy. Repeat scans of the same animals showed that intrasubject variability was smaller than intersubject variability. CONCLUSIONS: We have developed a high-resolution micro computed tomography method for evaluating the cardiac function and morphology of free-breathing mice in acquisition times shorter than 1 minute.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.303
Teacher spread0.272 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations93
Published2007
Admission routes1
Has abstractyes

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