SU‐E‐I‐132: Whole‐Brain DCE Quantitative Perfusion Imaging
Bibliographic record
Abstract
Purpose: To develop a whole‐brain acquisition protocol based on dynamic contrast enhanced (DCE) MR imaging that is able to provide absolute brain tissue perfusion estimates and to assess, evaluate and minimize the uncertainty in the estimates of contrast agent concentration. Methods: Preliminary in vivo data was collected using a 3D spoiled‐gradient recalled echo (SPGR) sequence with TR/TE/flip angle (FA) = 2.5 ms/1.15 ms/15°. The spatial resolution was 1.7×1.7×4.0 mm3. A variable rate k‐space sampling scheme and view sharing resulted in a temporal resolution of 2.4 s. The perfusion estimates were calculated using indicator‐dilution theory. Based on preliminary data, simulations estimating the uncertainty and sensitivity to misestimated parameters in the concentration measures were performed. Tissue parameters and contrast agent relaxivities were obtained from literature. The SNR before contrast injection was extracted from the preliminary data. Results: Absolute perfusion maps were successfully generated for the whole brain in all three subjects. As predicted, the low signal enhancement in cerebral tissue, especially in white matter, resulted in noisy perfusion maps. The range of the cerebral blood flow (CBF) and cerebral blood volume (CBV) maps corresponded to the range of the values reported in literature. The mean transit times (MTT) however were overestimated. The simulations showed that a FA of 9° minimizes the concentration coefficient of variation in white matter. However, the sensitivity to misestimated parameters was reduced with increasing FA: a FA > 20° was beneficial. For realistic SNR levels, averaging 5 acquisitions of the signal before contrast agent arrival were sufficient. More baseline measurements did not significantly decrease the signal variance. Conclusion: It is possible to acquire qualitatively acceptable whole‐brain bolus tracking data using a DCE sequence. In addition, the FA influences the uncertainty and sensitivity in concentration estimates when using a SPGR sequence. Grant support from the Canadian Institutes for Health Research, Canada Research Chairs Program and Hopewell Professorship in Brain Imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".