High intensity focused ultrasound with large aperture transducers: A MRI based focal point correction for tissue heterogeneity
Bibliographic record
Abstract
PURPOSE: The risk of undesired tissue damage to thoracic cage, heart, and lung during MR guided HIFU ablations of breast cancer can be greatly reduced if a phased array transducer design with a lateral beam direction is used in combination with a large aperture. The disadvantage is an increased sensitivity to focus aberrations due to tissue heterogeneity. Here, the authors propose to restore the focal coherence by using a matched aperture phase correction, which is based on a noninvasively obtained tissue model. METHODS: The method combines high resolution MRI with ultrasound wave measurements of different tissue types to determine a phase correction, which compensates focal point aberrations caused by tissue heterogeneity. 3D segmentation of tissue is used to quantify the relative proportion of each tissue type along a line running from the center of each element of the phased array to the target focal point. RESULTS: For tissue types with a celerity difference of 3%, the proposed method allows to quantify the phase aberration with an accuracy of 6° ± 20° and a correlation factor R(2) = 0.95. Using the refocusing method for a complex heterogeneous phantom resulted in 95% of the maximal pressure, whereas only 70% of the maximal pressure is obtained in absence of any phase correction. CONCLUSIONS: Since the proposed refocusing algorithm is compatible with a standard interventional preplanning and requires only a minimal amount of processing, it presents a promising approach to compensate for aberration in heterogeneous tissues such as the human breast.
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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 | 0.000 | 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".