Noninvasive Quantification of Cerebral Blood Flow Using Hybrid <scp>PET</scp>/<scp>MR</scp> Imaging to Extract the [<scp><sup>15</sup>O</scp>]<scp>H<sub>2</sub>O</scp> Image‐Derived Input Function Free of Partial Volume Errors
Bibliographic record
Abstract
Background Quantification of cerebral blood flow (CBF) with [15O]H2O‐positron emission tomography (PET) requires arterial sampling to measure the input function. This invasive procedure can be avoided by extracting an image‐derived input function (IDIF); however, IDIFs are sensitive to partial volume errors due to the limited spatial resolution of PET. Purpose To present an alternative hybrid PET/MR imaging of CBF (PMRFlowIDIF) that uses phase‐contrast (PC) MRI measurements of whole‐brain (WB) CBF to calibrate an IDIF extracted from a WB [15O]H2O time‐activity curve. Study Type Technical development and validation. Animal Model Twelve juvenile Duroc pigs (83% female). Population Thirteen healthy individuals (38% female). Field Strength/Sequences 3 T; gradient‐echo PC‐MRI. Assessment PMRFlowIDIF was validated against PET‐only in a porcine model that included arterial sampling. CBF maps were generated by applying PMRFlowIDIF and two previous PMRFlow methods (PC‐PET and double integration method [DIM]) to [15O]H2O‐PET data acquired from healthy individuals. Statistical Tests PMRFlow and PET CBF measurements were compared with regression and correlation analyses. Paired t‐tests were performed to evaluate differences. Potential biases were assessed using one‐sample t‐tests. Reliability was assessed by intraclass correlation coefficients. Statistical significance: = 0.05. Results In the animal study, strong agreement was observed between PMRFlowIDIF (average voxel‐wise CBF, 58.0 ± 16.9 mL/100 g/min) and PET (63.0 ± 18.9 mL/100 g/min). In the human study, PMRFlowDIM (y = 1.11x − 5.16, R2 = 0.99 ± 0.01) and PMRFlowPC−PET (y = 0.87x + 3.82, R2 = 0.97 ± 0.02) performed similarly to PMRFlowIDIF, and CBF was within the expected range (eg, 49.7 ± 7.2 mL/100 g/min for gray matter). Data Conclusion Accuracy of PMRFlowIDIF was confirmed in the animal study with the primary source of error attributed to differences in WB CBF measured by PC MRI and PET. In the human study, differences in CBF from PMRFlowIDIF, PMRFlowDIM, and PMRFlowPC−PET were due to the latter two not accounting for blood‐borne activity. Level of Evidence 2 Technical Efficacy Stage 1
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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 | 0.000 |
| 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".