Bibliographic record
Abstract
The advent of magnetic resonance imaging (MRI) has resulted in a paradigm shift in cerebral perfusion imaging, with the ability to image both perfusion and perfusion regulation at the millimeter-scale. The most widely used perfusion MRI method, dynamic susceptibility contrast (DSC), requires the injection of gadolinium-based contrast (Gd), an exogenous paramagnetic metal chelated to an organic moiety. On the other hand, imaging perfusion regulation, specifically cerebrovascular reactivity (CVR), necessitates a vascular stimulus which in turn yields a change in paramagnetic deoxyhemoglobin (dOHb). In both cases, T2*-weighted signal changes are exploited for the purposes of characterizing perfusion physiology. The reliance on invasive contrast in DSC-MRI is a notable limitation to current state-of-the-art perfusion imaging and begs the question of whether a non-invasive alternative is available. In addition, the relationship between magnetic susceptibility (amongst other acquisition/analysis parameters) and T2*-based perfusion has not been holistically characterized in the literature. To address these concerns, I first developed a simulation framework to investigate the accuracy and precision of DSC-MRI when varying different acquisition/analysis parameters and discovered dependencies in the simulations which were validated by experimental data from healthy subjects. Furthermore, using simulations and experimental data, I compared non-invasive gas control system-induced hypoxia (i.e., dOHb) with Gd as contrast for DSC-MRI. Next, I developed a breath-hold DSC pipeline for the non-invasive estimation of baseline perfusion at 3T and 7T, validated results with arterial spin labeling, and employed a simulation framework to address unique limitations associated with the breath-holding methodology. Finally, I extended my DSC-MRI simulation framework to investigate the accuracy of CVR imaging, and similarly uncovered quantification dependencies which were consistent with literature values and experimental data. In sum, this work enhances our understanding of and ability to interpret MRI-based perfusion/perfusion regulation measures and provides non-invasive solutions for perfusion imaging.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".