Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer
Bibliographic record
Abstract
Problem: The gold standard for prostate cancer diagnosis is B-mode transrectal ultrasound-guided systematic core needle biopsy. However, cancer is indistinguishable under ultrasound and thus additional costly imaging methods are necessary to perform targeted biopsies. Speed of sound is a potential biomarker for prostate cancer and has the potential to be measured using ultrasound tomography. Given the physical constraints of the prostate’s anatomy, this work explores a simulation study using deep learning for limited-angle ultrasound tomography to reconstruct speed of sound. Methods: A deep learning-based image reconstruction framework is used to address the limited-angle ultrasound tomography problem. The training data is generated using the k-wave acoustic simulation package. The general network structure is composed of a series of dense fully-connected layers followed by an encoder and a decoder network. The basic idea behind this neural network is to encode a time of flight map into a lower dimension representation that can then be decoded into a speed of sound image. Results and Conclusions: We show that limited-angle UST is feasible in simulation using an auto-encoder-like DL framework. There was a mean absolute error of 7.5 ± 8.1 m/s with a maximum absolute error of 139.3 m/s. Future validation on experimental data will further assess their ability in improving limited-angle ultrasound tomography.
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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".