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Design and Construction of a Heteronuclear 1H and 31P Double Tuned Coil for Breast Imaging and Spectroscopy

2016· article· en· W2743334691 on OpenAlexaff
Sergei Obruchkov, Norman B. Konyer, Michael D. Noseworthy

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2016
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcMaster UniversityHamilton Health SciencesSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMagnetic resonance imagingNuclear magnetic resonanceIn vivo magnetic resonance spectroscopyRadio frequencyModality (human–computer interaction)Breast imagingRadiofrequency coilComputer scienceBiomedical engineeringBreast cancerPhysicsMaterials scienceRadiologyMedicineCancerArtificial intelligence

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) is a noninvasive imaging modality that uses radio frequency (RF) energy to excite nuclei in the presence of a strong magnetic field and linear spatially encoding magnetic field gradients. Clinically, MRI takes advantage of the spin properties of hydrogen (1H) nuclei due to the high concentration and relative abundance in tissue water and fats. However, other nuclei having the quantum mechanical property of spin can also be probed. One of the most common is phosphorous (31P), which has 100% natural abundance and reasonable in vivo concentrations that are measurable at clinical MRI field strengths. Phosphorous measurements can provide an understanding of important metabolic pathways within tissues, which ultimately can help in better understanding disease and treatment. However, clinical MRIs do not routinely come with the ability to assess non-1H nuclei. Hence, hardware and pulse sequences need development, while considering the need to easily interface with standard clinical MRI hardware and protocols. This review describes the motivation for and development of MRI RF hardware designs for a human breast imaging system that can acquire 31P data from a clinically approved breast MR imaging and biopsy table.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.427
Threshold uncertainty score0.257

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 head0.020
GPT teacher head0.332
Teacher spread0.311 · 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 designBench or experimental
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

Citations0
Published2016
Admission routes1
Has abstractyes

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