Accelerated MRI of the fetal heart using compressed sensing and metric optimized gating
Bibliographic record
Abstract
Purpose To develop and validate a method for accelerated time-resolved imaging of the fetal heart using a combination of compressed sensing (CS) and metric optimized gating (MOG). Theory and Methods Joint optimization of CS and MOG reconstructions was used to suppress competing artifact from random undersampling and ungated cardiac motion. Retrospectively and prospectively undersampled adult and fetal data were used to validate the proposed reconstruction algorithm qualitatively based on visual assessment, and quantitatively based on reconstruction error, blur, and MOG timing error. Results Excellent agreement was observed between the fully sampled and retrospectively undersampled reconstructions, up to an undersampling factor of four. Visually, differences between ECG and MOG reconstructions of adult data were negligible. This was consistent with quantitative comparisons of reconstruction error (RMSEECG = 0.07–0.13; RMSEMOG = 0.08–0.13), and image blur (BECG = 1.03–1.20; BMOG = 1.03–1.20). The calculated MOG timing error (2–42 ms) was comparable to the acquired temporal resolution (∼60 ms). Quantitative evaluation of retrospectively undersampled (R = 2–8) fetal data (RMSEMOG = 0.06–0.12; BMOG = 1.04–1.27) was comparable to the adult volunteer results. Conclusion CS-MOG for dynamic imaging of the fetal heart was developed and validated. Using CS-MOG, images were obtained up to four times faster than conventional acquisitions. Magn Reson Med 77:2125–2135, 2017. © 2016 International Society for Magnetic Resonance in Medicine
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".