TU‐H‐206‐05: Investigating the Resistance of GS‐BSSFP to Motion Artifacts
Bibliographic record
Abstract
Purpose: In addition to correcting magnetic field inhomogeneity‐induced banding artifacts in balanced steady state free precession (bSSFP) MRI, preliminary in vivo studies indicate that the geometric solution (GS) also mitigates motion artifacts. The purpose here is to investigate the source of motion artifact correction through simulations, to further ascertain GS‐bSSFP's clinical potential. Methods: Four bSSFP MR images with Δθ = 0°, 90°, 180°, and 270° respective phase cycling and TE/TR = 4.2/2.1ms were 1) acquired in vivo with a 3D axially‐oriented sequence on a Philips Ingenia 3T MRI scanner using a flip angle α = 30°, and 180/180/120 matrix size and 1.0/1.0/1.0mm voxel size along frequency/phase/slice directions, and 2) simulated using α = 80°, parameters varied across the field‐of‐view (T1 relaxation = 200–>3000ms, T2 relaxation = 40‐>3000ms, and field inhomogeneity θ = −π−>+π), and added zero‐mean Gaussian noise. The GS (the cross‐point of lines/spokes connecting alternating phase cycles in the complex plane) and complex sum (CS) were computed pixel‐by‐pixel. Simulated data noise was reoriented in each associated spoke's frame of reference in order to derive the noise radiality. Plots were then generated of GS and CS error and deviation as a function of the noise radiality in the original data. Results: In vivo data indicates that the GS mitigates motion artifacts relative to the CS in the foramen magnum region. Simulated data indicates that the GS has less error in size and deviation than the CS, and this discrepancy grows as noise radiality increases. Conclusion: The GS shows more accuracy than the CS in all tests executed, especially as phasecycled image noise radiality increases. This implies that noise‐like image artifacts such as those caused by motion and flow are suppressed by the GS, inspiring clinical applications of GS‐bSSFP given its additional elimination of banding artifacts.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".