Development of 2D Spin-Echo Rosette MR Spectroscopic Imaging of the Human Brain at 3 Tesla
Bibliographic record
Abstract
Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive method for measuring brain tissue metabolite concentrations in vivo. A challenge in 1H-MRSI is that extracranial adipose tissue gives rise to lipid signals which appear in the MR spectra of voxels within the brain, hindering reliable quantification of metabolites-of-interest. The goal of this thesis was to develop a pipeline for acquisition, reconstruction, processing and quantification of whole-slice 1H-MRSI of the human brain, with a focus on mitigating lipid contamination. My first aim involved the development of a 2D spin-echo rosette MRSI pulse sequence, and optimization of echo time and spatial resolution. My second aim was to expand on reconstruction and pre-processing pipeline for rosette MRSI data in the FID-A toolkit. Finally, using these approaches, I demonstrated the acquisition of rapid high-resolution 2D 1H-MRSI maps with minimal lipid contamination. Overall, the methods presented in this work will advance MRSI for neurochemistry studies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".