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Record W2102011884 · doi:10.1002/nbm.1359

Trading off SNR and resolution in MR images

2009· article· en· W2102011884 on OpenAlexaff
Shoan C. Kale, Xianhao Chen, R. Mark Henkelman

Bibliographic record

VenueNMR in Biomedicine · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsNeuroanatomyConsistency (knowledge bases)Magnetic resonance imagingImage qualitySignal-to-noise ratio (imaging)Image resolutionIsotropyResolution (logic)Computer scienceArtificial intelligenceComputer visionRange (aeronautics)Nuclear magnetic resonanceImage (mathematics)PhysicsOpticsPsychologyMedicineMaterials scienceNeuroscience

Abstract

fetched live from OpenAlex

With a fixed time to acquire a magnetic resonance (MR) image, time can be spent to acquire better spatial resolution with decrease in signal-to-noise ratio (SNR) or decreased resolution with increase in SNR. This resolution/SNR tradeoff at fixed time has been investigated by a visual rater study using images of ex vivo mouse brains. Simulated images with a tradeoff between SNR and resolution were produced from high-quality, 3D isotropic mouse brain images to emulate shorter constant acquisition times. The tradeoff images spanned a range of SNRs (63-6) and isotropic resolutions (32-81 microm). Fourteen readers identified the image which best displayed neuroanatomy. Additional experiments tested for (i) intra-observer consistency, (ii) the effect of emulated scan time, and (iii) specifically biased questions pertaining to the perception of neuroanatomy. Optimal anatomical viewing depended primarily on the SNR of the images. Specifically, for fixed imaging time, preference lay in the SNR range of approximately 30-35 with strong consistency and there was minimal effect from overall imaging time.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.342
Teacher spread0.323 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations34
Published2009
Admission routes1
Has abstractyes

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