Investigation of motion artifacts associated with fat saturation technique in 3D flash imaging
Bibliographic record
Abstract
PURPOSE: Fast low-angle shot (FLASH) imaging is widely used in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) because it permits fast and accurate T1 measurement in vivo. Suppression of the fat signal is necessary for most FLASH applications; otherwise, fat will appear hyperintense. The fat saturation technique is one popular method to reduce fat images on clinical scanners. However, fat saturation combined with the 3D FLASH sequence in breast DCE-MRI scans results in heavy ghosting artifacts caused by heartbeat. We used simulation and experimental scans to determine the cause of these artifact-enhancement phenomena. METHODS: We simulated imaging of motion in the x, y, and z directions, with and without fat saturation, to investigate the origin of artifacts. Fourier transform (FT) of the whole field of view was used in the simulation, and we assumed that the uniform phantom was static during one TR. The amplitude of each echo was considered a factor in the FT data. Images were reconstructed using FT data from different phantom positions multiplied by the amplitude factor. Phantom experiments and volunteer studies were implemented to verify the conclusion. RESULTS: Both phantom and volunteer results showed artifacts similar to those in simulation images. We found that FLASH sequence without fat saturation is insensitive to motion. Fat saturation radiofrequency pulses placed before each group of echoes disrupted the steady state of the signal amplitude and produced a low-pass filter effect that enhanced the motion artifacts. CONCLUSIONS: We conclude that the low-pass filter effect associated with the fat saturation technique is responsible for dramatically increased motion 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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".