Banding artifact removal for bSSFP imaging with an elliptical signal model
Bibliographic record
Abstract
PURPOSE: Balanced steady-state free precession (bSSFP) imaging has broad clinical applications by virtue of its high time efficiency and desirable contrast. Unfortunately, banding artifact is often seen as a result of signal modulation due to B0 inhomogeneity. This study aims to develop an effective method for banding artifact suppression. METHODS: bSSFP is analyzed with an elliptical signal model. A simple analytical "Geometric-Solution" (GS) is presented to demodulate the signal from B0 inhomogeneity dependence with phase-cycled bSSFP data from both a computer simulation and experiments using phantom and human subjects. RESULTS: The proposed algorithm is able to remove banding artifacts completely. It also compares favorably with the complex sum (CS), which is considered one of the more efficient methods for banding artifact correction. CONCLUSION: Using an elliptical signal model, an analytical solution to the bSSFP banding problem has been found and demonstrated with simulation as well as phantom and in vivo experiments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".