Combined Angiographic, Structural and Perfusion Radial Imaging using Arterial Spin Labeling
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 T1-weighted structural brain images in a single six-minute acquisition. Methods The proposed Combined Angiographic, Structural and Perfusion Radial Imaging using Arterial Spin Labeling (CASPRIA) pulse sequence uses pseudocontinuous arterial spin labeling (PCASL) to label inflowing blood, an inversion pulse to provide background suppression and T1-weighted contrast, and a continuous 3D golden ratio spoiled gradient echo readout. Label-control subtraction isolates the blood signal and can be flexibly reconstructed at high/low spatiotemporal resolution for angiography/perfusion imaging. The mean signal retains the static tissue, allowing T1-weighted structural images to be reconstructed at different effective inversion times. CASPRIA was compared with conventional time-of-flight (TOF) angiography, 3D-gradient and spin echo (3D-GRASE) PCASL perfusion imaging and magnetization-prepared rapid gradient echo (MP-RAGE) structural imaging (10 minutes total) in healthy volunteers. Results CASPRIA gave improved distal vessel visibility and fewer artefacts than TOF angiography, whilst also providing dynamic information, with blood transit time and dispersion maps. CASPRIA perfusion images were comparable to 3D-GRASE data, but without through-slice blurring or artefacts in inferior brain regions. Comparable quantitative cerebral blood flow maps were produced, with CASPRIA being significantly more repeatable. Structural CASPRIA images were comparable to MP-RAGE, but also yielded a range of T1-weighted contrasts and allowed quantitative T1 maps to be estimated. Conclusion CASPRIA 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".