Perfusion Quantification in the Human Brain Using DSC MRI – Simulations and Validations at 3T
Bibliographic record
Abstract
Abstract Gadolinium (Gd) and deoxyhemoglobin (dOHb) are paramagnetic contrast agents capable of inducing changes in T 2 *-weighted MRI signal, utilized in dynamic susceptibility contrast (DSC) MRI. With multiple contrast agents and analysis choices, there are a variety of questions as to its capability to accurately quantify perfusion values. To address these questions, we developed a novel signal model for DSC MRI that incorporates signal contributions from intravascular and extravascular water proton spins at 3T for arterial, venous, and cerebral tissue voxels. This framework allowed us to model the MRI signal in response to changes in Gd and dOHb concentrations, and the effects that various experimental and tissue parameters have on perfusion quantification. We compared the predictions of the numerical simulations with those obtained from experimental data at 3T on six healthy human subjects using Gd and dOHb boluses as contrast agents. Using standard DSC analysis, we identified perfusion quantification dependencies in the experimental results that were in close agreement with the simulations. We found that a reduced baseline oxygen saturation (base-S a O 2 ), greater susceptibility of applied contrast agent (Gd vs dOHb), and larger magnitude of the hypoxic drop (ΔS a O 2 ) reduces overestimation of the cerebral blood volume ( rCBV ) and flow ( rCBF ). Furthermore, shortening the bolus duration increases the accuracy and reduces the calculated values of mean transit time ( MTT ). This study demonstrates that changes in Gd and dOHb can be described by the same unifying theoretical framework, as validated by the experimental results. Based on our work, we suggest practices in DSC MRI that increase accuracy and reduce inter- and intra-subject variability. In uncovering a wide array of quantification dependencies, we argue that caution must be exercised when comparing perfusion values obtained from a standard DSC MRI analysis when employing different experimental paradigms.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".