Non-invasive perfusion MR imaging of the human brain via breath-holding
Bibliographic record
Abstract
Dynamic susceptibility contrast (DSC) MRI plays a pivotal role in the accurate diagnosis and prognosis of several neurovascular diseases, but is limited by its reliance on gadolinium, an intravascularly injected chelated metal. Here, we determined the feasibility of measuring perfusion using a DSC analysis of breath-hold-induced gradient-echo-MRI signal changes. We acquired data at both 3 T and 7 T from ten healthy participants who engaged in eight consecutive breath-holds. By pairing a novel arterial input function strategy with a standard DSC MRI analysis, we measured the cerebral blood volume, flow, and transit delay, and found values to agree with those documented in the literature using gadolinium. We also observed voxel-wise agreement between breath-hold and arterial spin labeling measures of cerebral blood flow. Breath-holding resulted in significantly higher contrast-to-noise (6.2 at 3 T vs. 8.5 at 7 T) and gray matter-to-white matter contrast at higher field strength. Finally, using a simulation framework to assess the effect of dynamic vasodilation on perfusion estimation, we found global perfusion underestimation of 20-40%. For the first time, we have assessed the feasibility of and limitations associated with using breath-holds for perfusion estimation with DSC. We hope that the methods and results presented in this study will help pave the way toward contrast-free perfusion imaging, in both basic and clinical research.
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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".