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Record W1991744090 · doi:10.1002/mrm.10173

MRI of the lungs using hyperpolarized noble gases

2002· review· en· W1991744090 on OpenAlexaff
Harald E. Möller, X. Josette Chen, B. Saam, Klaus D. Hagspiel, G. Allan Johnson, Talissa A. Altes, Eduard E. de Lange, Hans‐Ulrich Kauczor

Bibliographic record

VenueMagnetic Resonance in Medicine · 2002
Typereview
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersNational Heart, Lung, and Blood InstituteNycomed AmershamNational Institutes of HealthMax-Planck-GesellschaftDeutsche ForschungsgemeinschaftNational Center for Research ResourcesDeutscher Akademischer AustauschdienstEuropean CommissionWhitaker FoundationAlexander von Humboldt-Stiftung
KeywordsNoble gasNuclear magnetic resonanceHyperpolarization (physics)Polarization (electrochemistry)Induced polarizationMaterials scienceChemistryPhysicsNuclear magnetic resonance spectroscopyAtomic physics

Abstract

fetched live from OpenAlex

The nuclear spin polarization of the noble gas isotopes (3)He and (129)Xe can be increased using optical pumping methods by four to five orders of magnitude. This extraordinary gain in polarization translates directly into a gain in signal strength for MRI. The new technology of hyperpolarized (HP) gas MRI holds enormous potential for enhancing sensitivity and contrast in pulmonary imaging. This review outlines the physics underlying the optical pumping process, imaging strategies coping with the nonequilibrium polarization, and effects of the alveolar microstructure on relaxation and diffusion of the noble gases. It presents recent progress in HP gas MRI and applications ranging from MR microscopy of airspaces to imaging pulmonary function in patients and suggests potential directions for future developments.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.067
GPT teacher head0.353
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations396
Published2002
Admission routes1
Has abstractyes

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