Dual-modality image guided high intensity focused ultrasound device design for prostate cancer: A numerical study
Bibliographic record
Abstract
In this study we established the feasibility of designing a multi-element high intensity focused \nultrasound (HIFU) device for Magnetic Resonance and an ultrasound imaging-guided transrectal \ntreatment of prostate cancer. An initial geometry was specified based on a clinical transrectal \nHIFU device with a central open space to lodge an independent ultrasound imaging probe \nfor guidance. A parametric study was performed to determine the optimal focal length (L ), \noperating frequency (j), element size (a) and central opening radius (r) of a device that would be \ncapable of treating cancerous tissue within the prostate, spare the surrounding organs and \nminimize the number of elements. Images from the Visible Human Project were used to \ndetermine the organ sizes and treatment locations for simulation. Six virtual ellipsoidal tumors \nwere located throughout a simulated prostate and their lateral and axial limit locations were \nselected as test locations. Using Tesla 1060(NVIDIA) graphics processors, the Bio-Heat Transfer \nEquation was implemented to simulate the heating produced during treatment at the test \nlocations. L, f a and r were varied from 45 to 75mm, 2.25 to 3.00MHz, 1.5 to 8 times A, \nwhere A= f speed of sound , and 9 to 12.5mm respectively. Results indicated that a combination of \nL,f, a and r of 68mm, 2.75MHz, 2.05A. and 9mm respectively could safely ablate tumors within \nthe prostate and spare the surrounding organs. The number of therapeutic elements required for \nthis device was 761.
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 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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".