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Record W2158496347 · doi:10.1109/tmag.2010.2074208

Design and Optimization of a Novel Bored Biplanar Permanent-Magnet Assembly for Hybrid Magnetic Resonance Imaging Systems

2010· article· en· W2158496347 on OpenAlexaff
Tony Tadic, B. G. Fallone

Bibliographic record

VenueIEEE Transactions on Magnetics · 2010
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMagnetHomogeneity (statistics)Finite element methodMagnetic fieldMagnetic resonance imagingNuclear magnetic resonancePole pieceMaterials scienceComputer scienceMechanical engineeringAcousticsPhysicsEngineering

Abstract

fetched live from OpenAlex

We present a novel design for a biplanar permanent-magnet assembly suitable for use in hybrid magnetic resonance imaging (MRI) systems. The key feature of our design is a large cylindrical hole that is longitudinally bored through the entire magnet assembly. The presence of the bore permits the potential inclusion of additional peripheral devices within or near the magnet structure that may benefit from being placed along, or oriented parallel to, the main magnetic field. In particular, the magnet assembly can be considered for use in an integrated system consisting of a 6 MV medical linear accelerator (linac) coupled to an MRI system for state-of-the-art real-time image-guided adaptive radiotherapy. We use magnetic field calculations based on the finite-element method (FEM) to quantify the detrimental effects of the bore on the field homogeneity in the imaging volume for pole-piece designs found commonly in industry. We then demonstrate that shape optimization of the pole pieces yields novel pole designs that lead to suitable levels of field homogeneity. We examined the resultant magnetic field within the bore for the optimized design and found that it has maximum field homogeneity.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.534
Threshold uncertainty score0.670

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 science0.0000.000
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 head0.019
GPT teacher head0.276
Teacher spread0.257 · 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 designSimulation or modeling
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

Citations21
Published2010
Admission routes1
Has abstractyes

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