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Record W2043096658 · doi:10.1002/jmri.20503

Pulsed arterial spin labeling parameter optimization for an elderly population

2006· article· en· W2043096658 on OpenAlexaff
Alison Campbell, Christian Beaulieu

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2006
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsArterial spin labelingPerfusionCerebral blood flowTransit timeCerebral perfusion pressurePerfusion scanningMedicinePopulationSIGNAL (programming language)Nuclear medicineNuclear magnetic resonanceCardiologyBiomedical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

PURPOSE: To optimize pulsed arterial spin labeling (PASL) parameters for the elderly to take into account possible perfusion changes that occur in the brain with age. MATERIALS AND METHODS: Healthy young (N = 14, age range = 21-27 years) and elderly (N = 12, age range = 61-67 years) subjects were scanned using Q2TIPS (QUIPSS II with thin-slice TI, periodic saturation) with varying inversion times (TI(2)) at 1.5T. The difference signal (DeltaM), transit time (deltat), and cerebral blood flow (CBF) were calculated in segmented gray matter (GM). RESULTS: The young displayed more perfusion-weighted signal difference than the elderly at all TI(2)'s. The peak DeltaM occurred at TI(2) approximately 1300 msec and 1500 msec in the young and elderly groups, respectively. Qualitatively, intravascular signal was minimal in the younger group by TI(2) = 1500 msec, whereas a longer TI(2) of 1800 msec was needed to minimize this signal in the elderly. The transit time was approximately 100 msec longer in the elderly, and CBF was in the range of literature values. CONCLUSION: For acquiring perfusion-weighted images with minimal intravascular signal and adequate tissue signal for PASL studies of cerebral perfusion in the elderly, a longer inversion time is advantageous.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.916
Threshold uncertainty score0.341

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 head0.015
GPT teacher head0.316
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.

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

Citations50
Published2006
Admission routes1
Has abstractyes

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