Free‐Breathing 3D Cardiac MRI Using Iterative Image‐Based Respiratory Motion Correction
Bibliographic record
Abstract
Respiratory motion compensation using diaphragmatic navigator gating with a 5 mm gating window is conventionally used for free-breathing cardiac MRI. Because of the narrow gating window, scan efficiency is low resulting in long scan times, especially for patients with irregular breathing patterns. In this work, a new retrospective motion compensation algorithm is presented to reduce the scan time for free-breathing cardiac MRI that increasing the gating window to 15 mm without compromising image quality. The proposed algorithm iteratively corrects for respiratory-induced cardiac motion by optimizing the sharpness of the heart. To evaluate this technique, two coronary MRI datasets with 1.3 mm(3) resolution were acquired from 11 healthy subjects (seven females, 25 ± 9 years); one using a navigator with a 5 mm gating window acquired in 12.0 ± 2.0 min and one with a 15 mm gating window acquired in 7.1 ± 1.0 min. The images acquired with a 15 mm gating window were corrected using the proposed algorithm and compared to the uncorrected images acquired with the 5 and 15 mm gating windows. The image quality score, sharpness, and length of the three major coronary arteries were equivalent between the corrected images and the images acquired with a 5 mm gating window (P-value > 0.05), while the scan time was reduced by a factor of 1.7.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".