Variable ultrasound trigger delay for improved magnetic resonance acoustic radiation force imaging
Bibliographic record
Abstract
Magnetic resonance acoustic radiation force imaging (MR-ARFI) allows the quantification of microscopic displacements induced by ultrasound pulses, which are proportional to the local acoustic intensity. This study describes a new method to acquire MR-ARFI maps, which reduces the measurement noise in the quantification of displacement as well as improving its robustness in the presence of motion. Two MR-ARFI sequences were compared in this study. The first sequence 'variable MSG' involves switching the polarity of the motion sensitive gradient (MSG) between odd and even image frames. The second sequence named 'static MSG' involves a variable ultrasound trigger delay to sonicate during the first or second MSG for odd and even image frames, respectively. As previously published, the data acquired with a variable MSG required the use of reference data acquired prior to any sonication to process displacement maps. In contrary, data acquired with a static MSG were converted to displacement maps without using reference data acquired prior to the sonication. Displacement maps acquired with both sequences were compared by performing sonications for three different conditions: in a polyacrylamide phantom, in the leg muscle of a freely breathing pig and in the leg muscle of pig under apnea. The comparison of images acquired at even image frames and odd image frames indicates that the sequence with a static MSG provides a significantly better steady state (p < 0.001 based on a Student's t-test) than the images acquired with a variable MSG. In addition no reference data prior to sonication were required to process displacement maps for data acquired with a static MSG. The absence of reference data prior to sonication provided a 41% reduction of the spatial distribution of noise (p < 0.001 based on a Student's t-test) and reduced the sensitivity to motion for displacements acquired with a static MSG. No significant differences were expected and observed for thermal maps acquired with a variable MSG and a static MSG. The use of a static MSG with a variable ultrasound trigger delay improves the ARFI displacement map quality without additional acquisition time and remains compatible with the simultaneous acquisition of MR thermal maps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 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".