MétaCan
Menu
Back to cohort
Record W2016077807 · doi:10.1088/0031-9155/50/20/001

A novel microbubble construct for intracardiac or intravascular MR manometry: a theoretical study

2005· article· en· W2016077807 on OpenAlexaff
Rohan Dharmakumar, Donald B. Plewes, Graham A. Wright

Bibliographic record

VenuePhysics in Medicine and Biology · 2005
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMicrobubblesMaterials scienceRADIUSNuclear magnetic resonanceShell (structure)Magnetic nanoparticlesVolume (thermodynamics)Biomedical engineeringMechanicsNanoparticlePhysicsNanotechnologyAcousticsUltrasoundThermodynamicsMedicineComposite material

Abstract

fetched live from OpenAlex

It has been demonstrated that gas-filled microbubble contrast agents, based on their volume changes, can serve as pressure probes in an MR field. It was recently reported that such an MR-based pressure measurement with microbubbles at 1.5 T must make use of microbubbles that have a volumetric magnetic susceptibility difference with the blood of at least 34 ppm in SI units. In this work, we show through analytical approximations and numerical simulations that such a microbubble formulation can be achieved by coating typical lipid-shelled microbubbles with particles of high dipole moment. Through finite-element simulations we demonstrate that the effective volumetric magnetic susceptibility of a coated microbubble is dependent on the radius, the shell volume fraction and the magnetic susceptibility of the particulates on the shell. Our calculations suggest that a suitable microbubble formulation which will be MR-sensitive to small pressure changes at 1.5 T must be 2-3 microm in radius and be uniformly coated with single-domain magnetic nanoparticles, such as magnetite, at shell volume fractions below 5%.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.513
Threshold uncertainty score0.320

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 studies0.0000.001
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 head0.142
GPT teacher head0.440
Teacher spread0.298 · 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 designTheoretical or conceptual
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

Citations22
Published2005
Admission routes1
Has abstractyes

Explore more

Same venuePhysics in Medicine and BiologySame topicAdvanced MRI Techniques and ApplicationsFrench-language works237,207