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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".