Parallel Imaging Artifacts in Body Magnetic Resonance Imaging
Bibliographic record
Abstract
OBJECTIVE: To familiarize the reader with the fundamental concepts of partial parallel imaging (PPI); to review the technical aspects of PPI including calibration scan, coil geometry, and field of view (FOV); and to illustrate artifacts related to parallel imaging and describe solutions to minimize their negative impact. RESULTS: PPI has led to a significant advance in body magnetic resonance imaging by reducing the time required to generate an image without loss of spatial resolution. Although PPI can improve image quality, it is not free of artifacts, which can result in significant image degradation. Knowledge of these artifacts and how to minimize their effect is important to optimize the use of parallel imaging for specific body magnetic resonance imaging applications. CONCLUSIONS: The reader will be introduced to the fundamental principles of PPI. Common imaging characteristics of PPI artifacts will be displayed with an emphasis on those seen with image-based methods, the principles behind their generation presented, and measures to minimize their negative impact will be proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".