CT-Enhanced Ultrasound Image of a Totally Deflated Lung for Image-Guided Minimally Invasive Tumor Ablative Procedures
Bibliographic record
Abstract
A technique is proposed to enhance the quality of intraoperative ultrasound (US) images of a deflated lung undergoing minimally invasive tumor ablative procedure. Since US images are very sensitive to residual air remaining in deflated lung, lung US images have very poor quality, and hence, are not appropriate for image-guided procedures. Therefore, a reliable and high-quality intraoperative image of the lung is a paramount necessity for tumor localization and fusion with real-time navigation data during such procedures. The proposed technique employs information of a deflated lung's computed tomography (CT) image constructed preoperatively in order to enhance those of the intraoperative US images. The enhancement is performed via two concurrent registration processes. The output is an enhanced US image of the deflated lung oriented and positioned accurately within its preoperative CT counterpart. Ex vivo experiments were conducted to evaluate the performance of the proposed technique. The obtained results indicate that very considerable improvement was achieved in the quality of the input intraoperative US images of the deflated lung.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".