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Record W2033594520 · doi:10.1002/mrm.22038

In vivo MR elastography of the prostate gland using a transurethral actuator

2009· article· en· W2033594520 on OpenAlexaff
Rajiv Chopra, Arvin Arani, Yuexi Huang, Mireía Musquera, Jeff Wachsmuth, Michael Bronskill, Donald B. Plewes

Bibliographic record

VenueMagnetic Resonance in Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsProstate glandProstateMagnetic resonance elastographyIn vivoMaterials scienceElastographyBiomedical engineeringActuatorAcousticsMedicineComputer sciencePhysicsUltrasoundInternal medicineBiology

Abstract

fetched live from OpenAlex

Conventional approaches for MR elastography (MRE) using surface drivers have difficulty achieving sufficient shear wave propagation in the prostate gland due to attenuation. In this study we evaluate the feasibility of generating shear wave propagation in the prostate gland using a transurethral device. A novel transurethral actuator design is proposed, and the performance of this device was evaluated in gelatin phantoms and in a canine prostate gland. All MRI was performed on a 1.5T MR imager using a conventional gradient-echo MRE sequence. A piezoceramic actuator was used to vibrate the transurethral device along its length. Shear wave propagation was measured transverse and parallel to the rod at frequencies between 100 and 250 Hz in phantoms and in the prostate gland. The shear wave propagation was cylindrical, and uniform along the entire length of the rod in the gel experiments. The feasibility of transurethral MRE was demonstrated in vivo in a canine model, and shear wave propagation was observed in the prostate gland as well as along the rod. These experiments demonstrate the technical feasibility of transurethral MRE in vivo. Further development of this technique is warranted.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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 designObservational
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

Citations55
Published2009
Admission routes1
Has abstractyes

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