Volterra series modelling and compensation of non-linear distortions caused by susceptibility difference artefacts related to the presence of ferromagnetic implants in magnetic resonance imaging
Bibliographic record
Abstract
Magnetic resonance imaging is popular in medical settings due to its unique technical characteristics. However, its full potential has been limited by imaging artefacts caused by various phenomena. Previously, a methodology was proposed to characterize and reduce artefacts caused specifically by magnetic susceptibility differences. In the present work, the Volterra series approach is suggested as an alternative method for describing non-linear distortions induced by susceptibility artefacts. A second-order Volterra series is utilized for characterizing the image non-linearities using a block-by-block processing approach. Subsequently, a corresponding second-order inverse Volterra series is applied to compensate for the quantified distortions. In addition, a technique for automatic demarcation of recoverable and non-recoverable regions in magnetic resonance images is proposed to ameliorate the developed methodology. Experimental results indicate that this approach offers improved accuracy and flexibility in reducing non-linear distortions caused by magnetic susceptibility artefacts.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".