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Exploration of novel contrast agents for functional imaging using microwave tomography

2013· article· en· W2022354501 on OpenAlexaff
Cameron Kaye, Joe LoVetri, Amer Zakaria, Anastasia Baran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrowave imagingContext (archaeology)Materials scienceMicrobubblesContrast (vision)Biomedical engineeringMicrowaveBreast imagingBreast cancerMedical physicsNuclear magnetic resonanceMammographyMedicineRadiologyComputer scienceArtificial intelligencePhysicsUltrasoundCancerTelecommunications

Abstract

fetched live from OpenAlex

Microwave tomography (MWT) or microwave imaging spectroscopy (MIS) has been studied as a promising low-cost portable alternative or complementary biomedical diagnostic technique for conventional soft-tissue imaging modalities for several years. The ability to quantitatively reconstruct unique, diagnostically useful properties of the tissue of interest (i.e. the permittivity and effective conductivity) at safe, non-ionizing frequency ranges without the need of a contrast agent has been among MWT's frequently quoted advantages, especially in the context of its most developed application in breast imaging. However, recent large-scale studies characterizing the ultra-wideband dielectric properties of freshly excised breast tissues have shown that the contrast between malignant breast carcinoma and normal fibroconnective/glandular tissue is inherently low (M. Lazebnik et al., Phys. Med. Biol. 52, 6093-6115, 2007), making it clear that this emerging technology could benefit from the use of exogenous contrast agents that selectively accumulate in cancerous tissues. This concession has spurred interest in the study of conventional and novel contrast agents in the field of microwave imaging, with recent publications employing microbubbles (S.C. Hagness et al., Phys. Med. Biol., 54, 641-650, 2009), single-walled carbon nanotubes (S.C. Hagness et al., IEEE Trans. BME, 57, 8, 1831-1834, 2010), and magnetic nanocomposites (O.M. Bucci et al., IEEE Trans. BME, 58, 9, 2528-2536, 2011) demonstrating encouraging results. A broad study of contrast agents, including compounds traditionally employed in nuclear medicine procedures (albeit using their radioactively-inert isotopic counterparts) and other conventional anatomical imaging modalities, is conducted to assess their feasibility for microwave imaging. The search is expanded to explore not only tumour markers, but any organic or non-organic chemical compounds whose distribution within live tissues could yield useful functional, physiological information. Examples include potassium analogues (rubidium, cesium), tracers for inflammation and rapid cell division (gallium, flurodeoxyglucose), tissue oxygenation markers (nitroxide and trityl radicals), blood pool agents (gadolinium-based complexes) and functionalized magnetic nanoparticles (iron oxide nanocrystals). Such chemical species with favourable complex permittivity measured in solution over a frequency range of interest are evaluated as potential MWT contrast agents for in vivo imaging applications based on similar criteria that govern their selection for other modalities. These criteria include chemical stability and water solubility, pharmacological half-life and method of clearance, ease of administration, cost and availability, and selectivity of the agent's biodistribution. The compound should demonstrate sufficient cellular uptake to significantly affect dielectric contrast in tissues of interest and most importantly, exhibit no biological toxicity at the concentrations required to obtain said contrast.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.442

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 head0.040
GPT teacher head0.235
Teacher spread0.195 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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