MO‐G‐18C‐01: BEST IN PHYSICS (IMAGING) – Novel Correction of Signal Modulation and Motion Artifacts in Temporal Bone BSSFP MRI
Bibliographic record
Abstract
Purpose: Phase cycled balanced steady state free precession (bSSFP) magnetic resonance (MR) images demonstrate high contrast in the temporal bone region, but are limited by motion artifacts stemming from globe motion, CSF and carotid artery pulsation, and by signal loss/modulation typical of bSSFP imaging of complex tissue environments. We sought to generate images delineating the fine morphology of temporal bone structures while minimizing signal loss and motion artifacts. Methods: Four axial 3D bSSFP MR images with Δθ = 0°, 90°, 180°, and 270° phase cycling respectively were acquired with a Philips Ingenia 3T MRI scanner in 2.3 minutes of total scan time. Other parameters included a 30° flip angle applied with a transmit/receive radiofrequency head coil, TE/TR = 4.2/2.1ms, receiver bandwidth = 890 Hz/pixel, and 180/180/120 matrix size and 1/1/1 mm voxel size along frequency/phase/slice directions. Complex image data were input into the Geometric Solution (GS) algorithm for calculation of artifact‐free bSSFP signal on a pixel‐by‐pixel basis. SNR was improved by linearizing the solution in a second‐pass regional optimization. Results: The GS eliminated the bSSFP signal intensity modulations while minimizing motion artifacts present in the original four images. The efficacy of the GS is validated through comparison with a complex average of the four original bSSFP images; unlike the GS, the complex average only moderates artifacts. Conclusion: The GS represents a novel method of correcting bSSFP MRI signal modulation and motion artifacts while maintaining contrast near the temporal bone. The technique is fast, easy to implement, and requires minimal post‐processing. This technique may inspire early clinical adoption, as it overcomes two major artifactual obstacles which have historically limited the widespread use of bSSFP MRI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".