Bibliographic record
Abstract
A method for reconstruction of time-resolved MRI called highly-constrained backprojection (HYPR) has been developed. To evaluate the HYPR reconstruction in relation to data sparsity and temporal dynamics, computer simulations were performed, investigating signal modulations under different situations that reflect dynamic contrast-enhanced MR angiography (MRA). In vivo studies were also performed with gadolinium diethylenetriamine pentaacetic acid (Gd-DTPA) for abdominal MRA in a canine model to demonstrate the application of HYPR for three-dimensional (3D) time-resolved MRA. When contrast dynamics vary over space, large vessels (e.g., veins) tend to introduce signal interference to small vessels (e.g., arteries) in HYPR, particularly when the vessels are in close proximity. The enhancement of background tissue signals may also alter the arterial and venous temporal profiles in HYPR. However, the artifacts are manifest as intensity modulation rather than structural interference, and therefore have little impact on structural diagnosis. Increasing the number of projections per time point increases temporal blur while reducing corruption of temporal behavior from adjacent tissues. Uniformly interleaved acquisition order, such as the bit-reversed order, is important to reduce artifacts. With high signal-to-noise ratio (SNR) and limited artifacts, HYPR reconstruction has potential to greatly improve time-resolved MRA in clinical practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".