A totally deflated lung's CT image construction by means of extrapolated deformable registration
Bibliographic record
Abstract
A novel technique is proposed to construct CT image of a totally deflated lung using breath-hold lung's preoperative CT images acquired during respiration. Such a constructed CT image is very useful in tumor targeting during tumor ablative procedures such as lung brachytherapy used for lung cancer treatment. To minimize motion within the target lung, tumor ablative procedures are frequently performed while the lung is totally deflated. Deflating the lung during such procedures renders pre-operative images ineffective for tumor targeting, because those images correspond to the lung while it is partially inflated. Furthermore, the problem cannot be solved using intra-operative Ultrasound (US) images. This is because the quality of lung US images degrades substantially as a result of the residual air inside the deflated lung, thus it is not an effective intra-operative imaging modality by itself. One possible approach for image-guided lung brachytherapy is to register high quality preoperative CT images of the deflated lung with their corresponding low quality intra-operative US images. To obtain the CT images of deflated lung, a novel image construction technique is presented. The proposed technique was implemented using two deformable registration methods: multi-resolution B-spline and multi-resolution demons. The technique was applied to ex vivo porcine lungs where results obtained were found to be very encouraging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".