The theory of and practical consideration for ultrasound guided interventions: from phantom data to clinical studies
Bibliographic record
Abstract
Cancer is one of the leading causes of death in Canada. Research in early detection is essential for improving survival rates. The current standards for diagnosis include physical examinations, chemical tests, and biopsy. However, these tests are inaccurate and invasive. There is a need for a more accurate and less invasive diagnostic tool. To address this, Temporal Enhanced Ultrasound (TeUS) was developed. \n \nTeUS is a method of non-invasive imaging based on the temporal response of the tissue to ultrasound irradiation. Previous studies have shown its effectiveness for in vivo and ex vivo classification of prostate cancer. Additional studies investigated the physical phenomenon of TeUS and demonstrated that the tissue response to physiological micro-vibrations recorded as a time series were the basis for tissue classification. This hypothesis was later validated through a series of simulations and tissue-mimicking phantom experiments. \n \nDespite the clinical success of TeUS, one underlying issue is having a controlled imaging environment with standard and repeatable micro-vibrations. Additionally, specialized ultrasound equipment is required for acquisition. This thesis aims at addressing these challenges. \n \nFirst, I introduced a new method of TeUS acquisition by incorporating changes to the imaging focal point in a time-dependent manner. I built 9 tissue-mimicking phantoms that differed in scatterer size and elasticity, collected TeUS, and used machine learning models to classify the phantoms. These results demonstrated the effectiveness of modifying the imaging focal point during acquisition for classification of phantoms. Second, I introduced two new methods of post-processing to further enhance ultrasound time series analysis. The first method is used to accommodate the changes to the imaging focal point, while the second method is a post-acquisition technique to create time series from a single ultrasound frame. These methods were evaluated using phantom experiments. Lastly, I demonstrated the feasibility of creating a time series from a single ultrasound frame using data collected from prostate cancer biopsy. A deep learning model was trained and results were compared to classification using traditional TeUS. The results obtained in this thesis may be useful for further improving the clinical translation of Temporal Enhanced Ultrasound for cancer diagnosis.
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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 | 0.003 |
| 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 | 0.001 |
| 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".