Suppression of high-density artefacts in x-ray CT images using temporal digital subtraction with application to cryotherapy
Bibliographic record
Abstract
Image guidance in cryotherapy is usually performed using ultrasound. Although not currently in routine clinical use, x-ray CT imaging is an alternative means of guidance that can display the full 3D structure of the iceball, including frozen and unfrozen regions. However, the quality of x-ray CT images is compromised by the presence of high-density streak artefacts. To suppress these artefacts we applied temporal digital subtraction (TDS). This TDS method has the added advantage of improving the grey scale contrast between frozen and unfrozen tissue in the CT images. Two sets of CT images were taken of a phantom material, cryoprobes and a urethral warmer (UW) before and during the cryoprobe freeze cycle. The high density artefacts persisted in both image sets. TDS was performed on these two image sets using the corresponding mask image of unfrozen material and the same geometrical configuration of the cryoprobes and the UW. The resultant difference image had a significantly reduced artefact content. Thus TDS can be used to significantly suppress or eliminate high-density CT streak artefacts without reducing the metallic content of the cryoprobes. In vivo study needs to be conducted to establish the utility of this TDS procedure for CT assisted prostate or liver cryotherapy. Applying TDS in x-ray CT guided cryotherapy will facilitate estimation of the number and location of all frozen and unfrozen regions, potentially making cryotherapy safer and less operator dependent.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".