Analysis of accelerated 4D flow MRI in the murine aorta by radial acquisition and compressed sensing reconstruction
Bibliographic record
Abstract
Preclinical 4D flow MRI remains challenging and is restricted for parallel imaging acceleration due to the limited number of available receive channels. A radial acquisition with combined parallel imaging and temporal compressed sensing reconstruction was implemented to achieve accelerated preclinical 4D flow MRI. In order to increase the accuracy of the measured velocities, a quantitative evaluation of different temporal regularization weights for the compressed sensing reconstruction based on velocity instead of magnitude data is performed. A 3D radial retrospectively triggered phase contrast sequence with a combined parallel imaging and compressed sensing reconstruction with temporal regularization was developed. It was validated in a phantom and in vivo (C57BL/6 J mice), against an established fully sampled Cartesian sequence. Different undersampling factors (USFs [12, 15, 20, 30, 60]) were evaluated, and the effect of undersampling was analyzed in detail for magnitude and velocity data. Temporal regularization weights λ were evaluated for different USFs. Acceleration factors of up to 20 compared with full Nyquist sampling were achieved. The peak flow differences compared with the Cartesian measurement were the following: USF 12, 3.38%; USF 15, 4.68%; USF 20, 0.95%. The combination of 3D radial center‐out trajectories and compressed sensing reconstruction is robust against motion and flow artifacts and can significantly reduce measurement time to 30 min at a resolution of 180 μm3. Concisely, radial acquisition with combined compressed sensing and parallel imaging proved to be an excellent method for analyzing complex flow patterns in mice.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".