Development of a Fast GlucoCEST Pulse Sequence for Investigating Gestational Metabolism and Physiology
Bibliographic record
Abstract
Early detection and diagnosis of fetal complications during gestation are often limited by either restrictive use of invasive diagnostic methods such as amniocentesis, or non-specific diagnostic methods such as ultrasound. Non-invasive magnetic resonance imaging (MRI) of tissue function through specific endogenous metabolite imaging could fill this diagnostic need. MR imaging of endogenous glucose would allow for early detection of tissue metabolic dysfunction, which can impede or complicate fetal development. However, standard MRI suffers from long acquisition times and low sensitivity, and is susceptible to artifacts from fetal and maternal motion, confounding its applicability to fetal imaging. This thesis proposes a novel MRI pulse sequence that both expedites acquisition and ameliorates sensitivity for glucose imaging. The implemented glucoCEST pulse sequence is validated through imaging of glucose calibration solutions and confound-producing phantoms. The glucoCEST pulse sequence produced is more sensitive and has faster acquisition than a reference CEST sequence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".