Combined angiography and perfusion using radial imaging and arterial spin labeling with structural contrast
Bibliographic record
Abstract
Abstract Purpose To develop a non‐contrast MRI method for the simultaneous acquisition of time‐resolved 3D angiographic, perfusion, and multi‐contrast T 1 ‐weighted structural brain images in a single 6 min acquisition. Methods The proposed combined angiography and perfusion using radial imaging and arterial spin labeling with structural contrast (CAPRIA+S) pulse sequence uses pseudocontinuous arterial spin labeling to label inflowing blood, an inversion pulse to provide background suppression and T 1 ‐weighted contrast, and a continuous 3D golden ratio spoiled gradient echo readout. Label‐control subtraction isolates the blood signal which can be flexibly reconstructed at high/low spatiotemporal resolution for angiography/perfusion imaging. The mean signal retains the static tissue, allowing T 1 ‐weighted structural images to be reconstructed at different effective TIs. CAPRIA+S was compared with conventional time‐of‐flight angiography, 3D‐gradient and spin echo pseudocontinuous arterial spin labeling perfusion imaging, and MPRAGE structural imaging (10 min total) in healthy volunteers. Results CAPRIA+S gave improved distal vessel visibility and fewer artifacts than time‐of‐flight angiography, while also providing dynamic information, with blood transit time and dispersion maps. CAPRIA+S perfusion images were comparable to 3D‐gradient and spin echo data but without through‐slice blurring or artifacts in inferior brain regions. Comparable quantitative cerebral blood flow maps were produced, with CAPRIA+S being significantly more repeatable. Structural CAPRIA+S images were comparable to MPRAGE but also yielded a range of T 1 ‐weighted contrasts and allowed quantitative T 1 maps to be estimated. Conclusion CAPRIA+S is an efficient single acquisition to provide intrinsically co‐registered quantitative information about brain blood flow and structure that has considerable advantages over conventional methods.
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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) | 0.001 | 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 | 0.001 | 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".