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Record W2062241450 · doi:10.4236/jbise.2014.76036

Does Resampled Image Data Offer Quantitative Image Quality Benefit for Pediatric CT?

2014· article· en· W2062241450 on OpenAlexafffund
Nancy L. Ford, Angjelina Protik, Paul Babyn, Karen E. Thomas

Bibliographic record

VenueJournal of Biomedical Science and Engineering · 2014
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsUniversity of British ColumbiaHospital for Sick ChildrenSickKids FoundationToronto Metropolitan UniversityUniversity of TorontoUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaHospital for Sick Children
KeywordsImage resolutionImage qualityScannerImaging phantomMaterials scienceNoise (video)Image noiseContrast (vision)Coronal planeLimitingSignal-to-noise ratio (imaging)Contrast-to-noise ratioBiomedical engineeringNuclear medicineArtificial intelligenceComputer scienceOpticsImage (mathematics)MedicinePhysicsRadiology

Abstract

fetched live from OpenAlex

Acquiring CT images with thin slices can improve resolution and detectability, but cause an increase in the image noise. To compensate for the additional image noise, the kVp or mA can be increased, which carries a dose penalty to the patient. We investigate the image quality achieved in MPR images reformatted from different slice thicknesses 0.625 mm and 5 mm, to determine if a thicker slice could be resampled to smaller thickness with minimal loss of image information. Catphan?600 phantom was imaged using selected kVp/mA settings (80 kVp/250 mA, 100 kVp/ 150 mA and 120 kVp/200 mA) to generate slices with thicknesses of 0.625 mm and 5 mm using a GE Discovery HD750 64-slice CT scanner to investigate the impact of the acquisition slice thickness on the overall image quality in MPRs. Measurements of image noise, uniformity, contrast-to-noise ratio (CNR), low contrast detectability and limiting spatial resolution were performed on axial and coronal multiplanar reformatted images (MPRs). Increased noise, reduced contrast-to-noise ratio, and improved limiting spatial resolution and low contrast detection were observed in 2 mm coronal MPRs generated with 0.625 mm thin slices when compared to the MPRs from 5 mm thick slices. If the 2 mm coronal MPRs acquired with 5 mm slices are resampled to 0.6 mm slice thickness, the reductions in limiting resolution and low contrast detection are compensated, although with reduced uniformity and increased image noise. Thick slice image acquisitions yield better CNR and less noise in the images, whereas thin slices exhibited improved spatial resolution and low contrast detectability. Retrospectively resampling into thinner slices before obtaining the coronal MPRs provided a balance between image smoothness and identifying fine image detail. Which approach provides the optimal image quality may also depend on the imaging task, size and composition of the features of interest, and radiologist preference.

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)0.008
metaresearch head score (Gemma)0.037
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
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 head0.035
GPT teacher head0.352
Teacher spread0.316 · 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
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".

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Citations1
Published2014
Admission routes2
Has abstractyes

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