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Record W2210961856 · doi:10.1118/1.4937780

Design and evaluation of an MRI-compatible linear motion stage

2015· article· en· W2210961856 on OpenAlexafffund
M. Ali Tavallaei, Patricia M. Johnson, Junmin Liu, Maria Drangova

Bibliographic record

VenueMedical Physics · 2015
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsScannerComputer scienceStage (stratigraphy)Computer visionMotion estimationMagnetic resonance imagingHomogeneity (statistics)Motion (physics)Linear motionSignal-to-noise ratio (imaging)Artificial intelligenceOpticsPhysicsRadiology

Abstract

fetched live from OpenAlex

PURPOSE: To develop and evaluate a tool for accurate, reproducible, and programmable motion control of imaging phantoms for use in motion sensitive magnetic resonance imaging (MRI) appli cations. METHODS: In this paper, the authors introduce a compact linear motion stage that is made of nonmagnetic material and is actuated with an ultrasonic motor. The stage can be positioned at arbitrary positions and orientations inside the scanner bore to move, push, or pull arbitrary phantoms. Using optical trackers, measuring microscopes, and navigators, the accuracy of the stage in motion control was evaluated. Also, the effect of the stage on image signal-to-noise ratio (SNR), artifacts, and B0 field homogeneity was evaluated. RESULTS: The error of the stage in reaching fixed positions was 0.025 ± 0.021 mm. In execution of dynamic motion profiles, the worst-case normalized root mean squared error was below 7% (for frequencies below 0.33 Hz). Experiments demonstrated that the stage did not introduce artifacts nor did it degrade the image SNR. The effect of the stage on the B0 field was less than 2 ppm. CONCLUSIONS: The results of the experiments indicate that the proposed system is MRI-compatible and can create reliable and reproducible motion that may be used for validation and assessment of motion related MRI applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.421
Teacher spread0.279 · 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 designOther design
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

Citations20
Published2015
Admission routes2
Has abstractyes

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