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Record W2174957696 · doi:10.1504/ijamc.2009.026850

MRI image enhancement by PROPELLER data fusion

2009· article· en· W2174957696 on OpenAlexaboutno aff
Krzysztof Malczewski

Bibliographic record

VenueInternational Journal of Advanced Media and Communication · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsComputer sciencePropellerComputer visionImage (mathematics)Image fusionArtificial intelligenceImage enhancementSensor fusionGeology

Abstract

fetched live from OpenAlex

Magnetic Resonance Imaging (MRI) image reconstruction, based on the frequency-domain Super-Resolution (SR) algorithm, is presented in this paper. It is shown that the approach improves MRI spatial resolution in cases where Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) sequences are used. The PROPELLER MRI method collects data in rectangular 'blades' rotated around the origin of the k-space. Inter-blade patient motion is the premise for the use of SR technique. Images obtained from sets of irregularly located frequency-domain samples are combined into the high-resolution MRI image. The SR reconstruction replaces the usually applied direct averaging of low-resolution images. This is an expanded version of a paper presented at the 3rd IEEE International Workshop on Medical Measurements and Applications, 9 10 May 2008, Ottawa, ON, Canada.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.277

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 science0.0010.000
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 head0.022
GPT teacher head0.360
Teacher spread0.338 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2009
Admission routes1
Has abstractyes

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