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Record W4230148768 · doi:10.32920/ryerson.14644593.v1

Monte Carlo and Tissue-Phantom studies of Photoacoustic Imaging of Carotid Intraplaque Haemorrhage

2021· preprint· en· W4230148768 on OpenAlexaff
Ahmed Yahia Khiari

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPhotoacoustic imaging in biomedicineImaging phantomCarotid arteriesMonte Carlo methodMedicineMagnetic resonance imagingRadiologyBiomedical engineeringCardiologyOpticsPhysicsMathematics

Abstract

fetched live from OpenAlex

Strokes are the second leading cause of death worldwide. Vulnerable carotid plaques are a primary cause of stroke, with carotid Intraplaque Haemmorrghages being a key feature of vulnerability. MR imaging, which is sensitive to the presence of endogenous Methemoglobin (MetHb), is used to detect IPH. MRI is however expensive, and not readily available. We propose the use of Photoacoustic Imaging (PAI) for the detection of IPH, with MetHb being the primary PA imaging target. I examine the feasibility of this approach by performing Monte Carlo studies of light propagation, energy deposition and PA generation in tissuemimicking models, as well as experimental PA measurements of MetHb in tissue-mimicking phantoms. I show that is possible to achieve an SNR of ∼50dB at the average carotid artery depth of ∼21mm, with the possibility of imaging up to ≥ 32mm in Type I skin using commonly available hardware.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.261
Teacher spread0.246 · 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 designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

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