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Record W2005387445 · doi:10.1002/mrm.22633

Transurethral prostate magnetic resonance elastography: Prospective imaging requirements

2010· article· en· W2005387445 on OpenAlexaff
Arvin Arani, Donald B. Plewes, Rajiv Chopra

Bibliographic record

VenueMagnetic Resonance in Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMagnetic resonance elastographyElastographyProstateMagnetic resonance imagingProstate cancerStiffnessMaterials scienceBiomedical engineeringNuclear magnetic resonanceUltrasoundMedicineRadiologyCancerPhysics

Abstract

fetched live from OpenAlex

Tissue stiffness is known to undergo alterations when affected by prostate cancer and may serve as an indicator of the disease. Stiffness measurements can be made with magnetic resonance elastography performed using a transurethral actuator to generate shear waves in the prostate gland. The goal of this study was to help determine the imaging requirements of transurethral magnetic resonance elastography and to evaluate whether the spatial and stiffness resolution of this technique overlapped with the requirements for prostate cancer detection. Through the use of prostate-mimicking gelatin phantoms, frequencies of at least 400 Hz were necessary to obtain accurate stiffness measurements of 10 mm diameter inclusions, but the detection of inclusions with diameters as small as 4.75 mm was possible at 200 Hz. The shear wave attenuation coefficient was measured in vivo in the canine prostate gland, and was used to predict the detectable penetration depth of shear waves in prostate tissue. These results suggested that frequencies below 200 Hz could propagate to the prostate boundary with a signal to noise ratio (SNR) of 60 and an actuator capable of producing 60 μm displacements. These requirements are achievable with current imaging and actuator technologies, and motivate further investigation of magnetic resonance elastography for the targeting of prostate cancer.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.009
GPT teacher head0.267
Teacher spread0.258 · 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.

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

Citations33
Published2010
Admission routes1
Has abstractyes

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