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Record W2560398163 · doi:10.15353/vsnl.v1i1.47

Compensated Diffusion Magnetic Resonance Imaging

2015· article· en· W2560398163 on OpenAlexaffvenue
Edward Li, Mohammad Shafiee, Ameneh Boroomand, Farzad Khalvati, Masoom A. Haider, Alexander Wong

Bibliographic record

VenueVision Letters · 2015
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsMagnetic resonance imagingNuclear magnetic resonanceContrast (vision)DiffusionMagnetic resonance spectroscopic imagingChemistryNuclear medicineMedicinePhysicsOpticsRadiology

Abstract

fetched live from OpenAlex

This paper presents a novel compensated diffusion magnetic resonance imaging (cdMRI) system for improved tissue detail and contrast for screening and diagnosis. The proposed cdMRI system incorporates the intrinsic properties of the MRI apparatus to compensate for artifacts and degradation caused through the imaging process. Experimental results for prostate imaging show that significant improvements in tissue detail and contrast can be obtained compared to current MRI systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes2
Has abstractyes

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