Motion compensated cine CMR of the fetal heart using radial undersampling and compressed sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To develop and evaluate a reconstruction framework for high resolution time-resolved CMR of the fetal heart in the presence of motion. METHODS: Data were acquired using a golden angle radial trajectory in seven fetal subjects and reconstructed as real-time images to detect fetal movement. Data acquired during through-plane motion were discarded whereas in-plane motion was corrected. A fetal cardiac gating signal was extracted to sort the corrected data by cardiac phase, allowing reconstruction of cine images. The quality of motion corrected images and the effect of data undersampling were quantified using separate expressions for spatial blur and image error. RESULTS: Motion corrected reordered cine reconstructions (127 slices) showed improved image quality relative to both uncorrected cines and corresponding real-time images across a range of root-mean-squared (RMS) displacements (0.3-3.7 mm) and fetal heart rates (119-176 bpm). The relative spatial blur between cines with and without motion correction increased with in-plane RMS displacement leading to an effective decrease in the effective spatial resolution for images without motion correction. Image error between undersampled and reference images was less than 10% for reconstructions using 750 or more spokes, yielding a minimum acceptable scan time of approximately 4 s/slice during quiescent through plane motion. CONCLUSIONS: By rejecting data corrupted by through-plane motion, and correcting data corrupted by in-plane translation, the proposed reconstruction framework accounts for common sources of motion artifact (gross fetal movement, maternal respiration, fetal cardiac contraction) to produce high quality images of the fetal heart.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it