Optimized navigator-based correction of breathing-induced B <sub>0</sub> field fluctuations in multi-echo gradient-echo imaging of the spinal cord
Bibliographic record
Abstract
Abstract Purpose Multi-echo gradient-echo (ME-GRE) imaging in the spinal cord is susceptible to breathing-induced B 0 field fluctuations due to the proximity of the lungs, leading to ghosting artifacts. A navigator readout can be used to monitor the fluctuations; however, standard navigator processing often fails in the spinal cord. Here, we introduce navigator processing tailored specifically for spinal cord imaging. Methods ME-GRE data covering all spinal cord regions were acquired in six healthy volunteers during free breathing at 3T. Centerline navigator readouts and respiratory belt recordings were collected during the acquisitions. The navigator processing included a Fast Fourier Transform and subsequent interval selection targeting the spinal cord, as well as SNR-weighted averaging over samples and coils on the complex data. Furthermore, a phase unwrapping algorithm making use of the belt recordings was developed. Imaging data were corrected by phase demodulation before image reconstruction. Results B 0 field fluctuations and ghosting artifacts were largest in the lower cervical and upper lumbosacral cord (∼5 Hz std), close to the edges of the lungs. Image reconstruction based on optimized navigator correction improved visual image quality and quantitative metrics (SNR, CNR, ghosting) in all regions of the spinal cord. The improvement was largest in regions with large field fluctuations (group-averaged increase in SNR/CNR of up to 29%/37% in single-echo images). Conclusions Optimized navigator-based correction can reduce ghosting artifacts and increase SNR/CNR in anatomical ME-GRE of the spinal cord. The enhanced image quality and ease of implementation across sites makes the technique attractive for clinical and scientific applications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 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".