Two Beam RAFA Lens’s Focused Enhancement Of HIFU Medical Treatment
Bibliographic record
Abstract
High Intensity Focused Ultrasound (HIFU) effectively treats tumors non-surgically such as cancer, kidney stones, neurological disease [1] and likely other malignancies such as endometriosis by ablation, cavitation and/or beam heating at megahertz (MHz) frequencies between 1 MHz and 15 MHz where the higher frequencies have higher energy used for shallow tissue treatment. The lower frequencies have greater penetration and transfer more power. Unfortunately, not only diseased tissue is treated but healthy tissue suffers collateral damage. Below ∼1 MH no tissue damage occurs. A HIFU problem currently is the treatments cannot be monitored resulting in under or over treatment causing damage to the surrounding healthy tissue. The diffuse acoustic confocal imager (DACI) has a Reflective Advance Focused Aperture (RAFA) lens that overcomes these HIFU deficiencies as well as some deficiencies of ultrasound imaging, i.e., its inability to diagnose diseased tissue when found requiring a biopsy. In brief, DACI RAFA diagnostically images three dimensionally diseased tissue using the confocal method of many slices, diagnoses tissue using speed-of-sound (SOS) imaging and can treat the diseased tissue, if found, using a small, high powered probe, the emphasis of this paper. The change in tissue due to treatment can be monitored using DACI’s measurement of SOS [2], necessary to be able discontinue the treatment when finished imaging and treatment modalities. DACI RAFA’s treatment capabilities using COMSOL simulations show enhanced treatment by mixing a 100 kHz (kilohertz) beam with a 300 kHz beam at an overlap distance of 10 cm, the focused probe position for treatment, also used as a virtual source for SOS imaging (Fig. 1). Use of the 100 kHz frequency transfers 10 times more sound power in decibels than 1 MHz. Mixing these two frequencies using the RAFA lens at the focused probe position resonates at 1 MH providing six times (∼6x) more amplitude or thirty-six times (∼36x) more power, (Fig. 2). Together, these two enhancements provide ∼360 times more power within the overlapped focused probe volume simulated to be 65 cm3 (∼4r3, r = 2.5 cm) without any damage to healthy tissue before, afterwards and to the sides. A standard treatment volume of ∼630 cm3 (V = 2π(hxL), h = 10 cm, L = 20 cm) would occur from the use of a single 1 MHz beam. Thus, the use of DACI’s RAFA lens reduces the volume by ∼10 times (650/63) and increases the power by ∼ 360 times, both significant improvements for HIFU treatment of diseased tissue [3]. DACI’s RAFA lens (green cone) reflects and mixes the 100 KHz beam (yellow) and 300 KHz beam (reddish) traveling independent paths to the diseased tissue (green blob) treated at the beam overlap position (red circle) without any healthy tissue being damaged before, after or to the sides. Separation of the beams, h = 10 cm occurs at the reflecting annular mirror and the penetration depth of the acoustic beams [2]. Total path length of the acoustic beams is L ∼20 cm. Scattering back from the focused probe (dash arrows) to the detector enables SOS measurements, tissue diagnosis and HIFU treatment monitoring. a) Simulation of the mixing of the 100 kHz and 300 kHz beams, b) their resonance at 1 MHz increases its acoustic pressure (A) by ∼6 times, i.e., enhanced power (A2) by 36 times, within an overlap volume of 65 cm3 from the resonance radius ∼2.5 cm. See text.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".