MétaCan
Menu
Back to cohort
Record W2039428410 · doi:10.1002/mrm.20590

Fast spin‐echo for multiple mouse magnetic resonance phenotyping

2005· article· en· W2039428410 on OpenAlexafffund
Brian J. Nieman, Nicholas A. Bock, Johnathan Bishop, John G. Sled, Xianhao Chen, R. Mark Henkelman

Bibliographic record

VenueMagnetic Resonance in Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanada Research Chairs
KeywordsNuclear magnetic resonanceMagnetic resonance imagingPulse sequenceFast spin echoSpin echoSequence (biology)IsotropyPhysicsOpticsBiologyMedicineGenetics

Abstract

fetched live from OpenAlex

High-resolution magnetic resonance imaging is emerging as a powerful tool for phenotyping mice in biologic studies of genetic expression, development, and disease progression. In several applications, notably random mutagenesis trials, large cohorts of mice must be examined for abnormalities that may occur in any part of the body. In the aim of establishing a protocol for imaging multiple mice simultaneously in a standardized high-throughput fashion, this study investigates variations of a three-dimensional fast spin-echo sequence that implements driven equilibrium, modified refocusing, and partial excitation pulses. Sequence variations are compared by simulated and experimental measurements in phantoms and mice. Results indicate that when using a short repetition time (TR<or=T1) a sequence employing a partial excitation tip angle provides both improved signal and good T2 contrast compared with standard fast spin-echo imaging. This sequence is used to simultaneously acquire four live mouse head images at 100 microm isotropic resolution with a scan time under 3 h at 7 T.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.325
Teacher spread0.301 · 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 designOther design
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".

Quick stats

Citations42
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueMagnetic Resonance in MedicineSame topicAdvanced MRI Techniques and ApplicationsFrench-language works237,207