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Record W2083298333 · doi:10.1118/1.4871621

Photoacoustic detection and optical spectroscopy of high-intensity focused ultrasound-induced thermal lesions in biologic tissue

2014· article· en· W2083298333 on OpenAlexaff
Mosa Alhamami, Michael C. Kolios, Jahangir Tavakkoli

Bibliographic record

VenueMedical Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceHigh-intensity focused ultrasoundBiomedical engineeringAttenuation coefficientIntegrating spherePhotoacoustic spectroscopyPenetration depthSpectroscopyUltrasoundOpticsNear-infrared spectroscopyPhotoacoustic imaging in biomedicineNuclear medicineMedicineRadiology

Abstract

fetched live from OpenAlex

PURPOSE: The aims of this study are: (a) to investigate the capability of photoacoustic (PA) method in detecting high-intensity focused ultrasound (HIFU) treatments in muscle tissues in vitro; and (b) to determine the optical properties of HIFU-treated and native tissues in order to assist in the interpretation of the observed contrast in PA detection of HIFU treatments. METHODS: A single-element, spherically concaved HIFU transducer with a centre frequency of 1 MHz was utilized to create thermal lesions in chicken breast tissues in vitro. To investigate the detectability of HIFU treatments photoacoustically, PA detection was performed at 720 and 845 nm on seven HIFU-treated tissue samples. Within each tissue sample, PA signals were acquired from 22 locations equally divided between two regions of interest within two volumes in tissue - a HIFU-treated volume and an untreated volume. Optical spectroscopy was then carried out on 10 HIFU-treated chicken breast specimens in the wavelength range of 500-900 nm, in 1-nm increments, using a spectrophotometer with an integrating sphere attachment. The authors' optical spectroscopy raw data (total transmittance and diffuse reflectance) were used to obtain the optical absorption and reduced scattering coefficients of HIFU-induced thermal lesions and native tissues by employing the inverse adding-doubling method. The aforementioned interaction coefficients were subsequently used to calculate the effective attenuation coefficient and light penetration depth of HIFU-treated and native tissues in the wavelength range of 500-900 nm. RESULTS: HIFU-treated tissues produced greater PA signals than native tissues at 720 and 845 nm. At 720 nm, the averaged ratio of the peak-to-peak PA signal amplitude of HIFU-treated tissue to that of native tissue was 3.68 ± 0.25 (mean ± standard error of the mean). At 845 nm, the averaged ratio of the peak-to-peak PA signal amplitude of HIFU-treated tissue to that of native tissue was 3.75 ± 0.26 (mean ± standard error of the mean). The authors' spectroscopic investigation has shown that HIFU-treated tissues have a greater optical absorption and reduced scattering coefficients than native tissues in the wavelength range of 500-900 nm. In fact, at 720 and 845 nm, the ratio of the optical absorption coefficient of HIFU-treated tissues to that of native tissues was 1.13 and 1.17, respectively; on the other hand, the ratio of the reduced scattering coefficient of HIFU-treated tissues to that of native tissues was 13.22 and 14.67 at 720 and 845 nm, respectively. Consequently, HIFU-treated tissues have a higher effective attenuation coefficient and a lower light penetration depth than native tissues in the wavelength range 500-900 nm. CONCLUSIONS: Using a PA approach, HIFU-treated tissues interrogated at 720 and 845 nm optical wavelengths can be differentiated from untreated tissues. Based on the authors' spectroscopic investigation, the authors conclude that the observed PA contrast between HIFU-induced thermal lesions and untreated tissue is due, in part, to the increase in the optical absorption coefficient, the reduced scattering coefficient and, therefore, the deposited laser energy fluence in HIFU-treated tissues.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.529

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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 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

Citations24
Published2014
Admission routes1
Has abstractyes

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