Metal artifact suppression in megavoltage computed tomography
Bibliographic record
Abstract
There has been considerable interest in megavoltage CT (MVCT) imaging associated with the development of image guided radiation therapy. It is clear that MVCT can provide good image quality for patient setup verification with soft tissue contrast much better than noted in conventional megavoltage portal imaging. In addition, it has been observed that MVCT images exhibit considerably reduced artifacts surrounding metal implants (e.g., surgical clips, hip implants, dental fillings) compared to conventional diagnostic CT images (kVCT). When encountered, these artifacts greatly limit the usefulness of kVCT images, and a variety of solutions have been proposed to remove the artifacts, but these have met with only partial success. In this paper, we investigate the potential for CT imaging in regions surrounding metal implants using high-energy photons from a Cobalt-60 source and from a 4 MV linear accelerator. MVCT and kVCT images of contrast phantoms and a phantom containing a hip prosthesis are compared and analysed. We show that MVCT scans provide good fidelity for CT number quantification in the high-density regions of the images, and in the regions immediately adjacent to the metal implants. They also provide structural details within the high-density inserts and implants. Calculations will show that practical clinical MVCT imaging, able to detect 3% contrast objects, should be achievable with doses of about 2.5cGy. This suggests that MVCT not only has a role in radiotherapy treatment planning and guidance, but may also be indicated for surgical guidance and follow-up in regions where metal implants cannot be avoided.
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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".