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Record W2117756700 · doi:10.1142/s1793545808000236

<i>IN VIVO</i> MAPPING BRAIN MICROCIRCULATION BY LASER SPECKLE CONTRAST IMAGING: A MAGNETIC RESONANCE PERSPECTIVE OF THEORETICAL FRAMEWORK

2008· article· en· W2117756700 on OpenAlexafffund
Zheng Wang

Bibliographic record

VenueJournal of Innovative Optical Health Sciences · 2008
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsRobarts Clinical TrialsWestern University
FundersCanadian Institutes of Health Research
KeywordsSpeckle patternMagnetic resonance imagingNeuroimagingFunctional magnetic resonance imagingPerspective (graphical)MicrocirculationComputer scienceContrast (vision)NeuroscienceArtificial intelligencePsychologyMedicineRadiology

Abstract

fetched live from OpenAlex

The fundamental limitations of most vascular-based functional neuroimaging techniques are placed by the fact how fine the brain regulates the blood supply system. In vivo mapping of the cerebral microcirculation with high resolution and sensitivity hence becomes unprecedentedly compelling. This paper reviews the theoretical background of the laser speckle contrast imaging (LSCI) technique and attempts to present a complete framework stemming from a simple biophysical model. Through the sensitivity analysis, more insights into the tool optimization are attained for in vivo applications. Open questions of the technical aspects are discussed within this unified framework. Finally, it concludes with a brief perspective of future research in a way analogous to the magnetic resonance imaging (MRI) technique. Such exploration could catalyze their development and initiate a technological fusion for precise assessment of blood flow across various spatial scales.

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)0.002
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 score0.882
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.025
GPT teacher head0.345
Teacher spread0.320 · 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

Citations1
Published2008
Admission routes2
Has abstractyes

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