A rapid magnetic resonance acoustic radiation force imaging sequence for ultrasonic refocusing
Bibliographic record
Abstract
Magnetic resonance guided acoustic radiation force imaging (MR-ARFI) is being used to correct for aberrations induced by tissue heterogeneities when using high intensity focusing ultrasound (HIFU). A compromise between published MR-ARFI adaptive solutions is proposed to achieve efficient refocusing of the ultrasound beam in under 10 min. In addition, an ARFI sequence based on an EPI gradient echo sequence was used to simultaneously monitor displacement and temperature with a large SNR and low distortion. This study was conducted inside an Achieva 3T clinical MRI using a Philips Sonalleve MR-HIFU system to emit a 1 ms pulsed sonication with duty cycle of 2.3% at 300 Wac inside a polymer phantom. Virtual elements defined by a Hadamard array with sonication patterns composed of 6 phase steps were used to characterize 64 groups of 4 elements to find the optimal phase of the 256 elements of the transducer. The 384 sonication patterns were acquired in 580 s to identify the set of phases that maximize the displacement at the focal point. Three aberrators (neonatal skull, 8 year old skull and a checkered pattern) were added to each sonication pattern to evaluate the performance of this refocusing algorithm (n = 4). These aberrators reduced the relative intensities to 95.3%, 69.6% and 25.5% for the neonatal skull, 8 year old skull, and checkered pattern virtual aberrators respectively. Using a 10 min refocusing algorithm, relative intensities of 101.6%, 91.3% and 93.3% were obtained. Better relative intensities of 103.9%, 94.3% and 101% were achieved using a 25 min refocusing algorithm. An average temperature increase of 4.2 °C per refocusing test was induced for the 10 min refocusing algorithm, resulting in a negligible thermal dose of 2 EM. A rapid refocusing of the beam can be achieved while keeping thermal effects to a minimum.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".