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Record W2022932409 · doi:10.1038/jcbfm.2013.59

Improved Cerebral Blood Flow Measurement with Multiexposure Speckle Imaging

2013· letter· en· W2022932409 on OpenAlexaff
Ian R. Winship

Bibliographic record

VenueJournal of Cerebral Blood Flow & Metabolism · 2013
Typeletter
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSpeckle patternBlood flowSpeckle imagingCerebral blood flowImage resolutionContrast (vision)Computer scienceArtificial intelligenceMedicineCardiology

Abstract

fetched live from OpenAlex

Laser speckle contrast imaging (LSCI) is a powerful tool for in vivo imaging of blood flow dynamics. Based on the blurring of the random interference pattern (‘speckle') produced at the camera by laser light reflecting off a surface, LSCI has been rapidly adopted because of its high spatial and temporal resolution coupled with the relative simplicity and low cost of assembling a LSCI instrument. Wide-field maps of cerebral blood flow acquired via LSCI have made significant contributions to our understanding of cerebral hemodynamics in the healthy and ischemic brain in animal models.1, 2, 3, 4 More recently, LSCI has been applied in studies of human patients.5, 6 While the high-resolution maps of relative changes in blood flow acquired during LSCI have been well validated, flow estimates from LSCI are affected by a number of parameters unrelated to blood flow. As such, the exact quantitative relationship between speckle contrast and blood flow velocity has not been precisely defined.7, 8 The sensitivity of LSCI to nonflow parameters is particularly problematic in long-term studies where nonflow parameters are more likely to vary between imaging sessions. Recently, traditional single-exposure LSCI was extended with a multiexposure speckle imaging (MESI) protocol that improved quantitative accuracy in microfluidic flow simulations and in vivo imaging in rodents.9, 10 Using improved mathematical models and instrumentation to acquire speckle contrast images at multiple, defined exposures, MESI accurately estimates flow changes associated during acute ischemic stroke, contrary to the underestimates of flow changes associated with traditional single-exposure LSCI.10 However, the utility of MESI in long-term, repeated-imaging studies has not been validated. In the current issue, Kazmi et al11 perform MESI in mice implanted with chronic cranial imaging windows. Multiexposure speckle imaging data are compared with quantitative measurements of absolute blood flow acquired via high-frame rate red blood cell photography (RBC tracking). In healthy mice and mice with targeted ischemic strokes imaged over multiple days, MESI consistently quantified blood flow more accurately than single-exposure LSCI. Notably, by testing multiple exposure times, Kazmi et al demonstrated that no single exposure time was optimal in all animals and imaging sessions. By incorporating multiple exposure times and improved mathematical modeling, MESI better accounted for variations in imaging conditions and more reliably quantified the blood flow during chronic imaging. Improved quantitative accuracy is demonstrated by reduced deviation between flow measurements derived from MESI and RBC tracking (∼10%) relative to single-exposure LSCI and RBC tracking (∼24%). While the reliability of quantitative accuracy between animal comparisons remains to be confirmed, MESI significantly improves the quantitative accuracy of LSCI blood flow measurements in both acute and chronic imaging paradigms. This improved quantitative accuracy extends LSCI beyond qualitative descriptions of changes in flow, an important contribution to preclinical and clinical studies of stroke hemodynamics and flow restoration therapies.

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)0.001
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.232
Teacher spread0.213 · 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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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