Metal Artifact Reduction: Added Value of Rapid-Kilovoltage-Switching Dual-Energy CT in Relation to Single-Energy CT in a Piglet Animal Model
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to evaluate virtual monochromatic spectral imaging and metal artifact reduction software for reducing metal artifact and to compare it with conventional single-energy CT (SECT) in an animal model. MATERIALS AND METHODS: Postmortem juvenile (n = 5) and adult (n = 1) swine specimens were scanned with SECT followed by a dual-energy CT (DECT) pediatric protocol after the insertion of two rods into their paraspinal thoracolumbar regions. Virtual monochromatic spectral images were extrapolated from DECT images at five monoenergetic levels (64, 69, 75, 88, and 105 keV) with and without the use of metal artifact reduction software. Images were evaluated by a 5-point scoring system for the extent of metallic artifacts and image interpretability in soft-tissue and bone windows. The density in the most pronounced artifact was measured. CT dose index was recorded. RESULTS: In studies without metal artifact reduction software, higher energy reconstructions resulted in fewer artifacts and better image interpretability in both soft-tissue and bone windows (p < 0.0001). Artifact density decreased from -792 HU at 64 keV to -128 HU at 105 keV without the use of metal artifact reduction software. No difference was noted in attributes' scores or in artifact density in studies using metal artifact reduction software (p > 0.05). DECT studies showed lower scores compared with SECT with regard to all attributes. A new faint perimetallic hypodense halo was seen in all studies with metal artifact reduction software. The CT dose index of DECT was 1.18-3.56 times higher than that of SECT techniques. CONCLUSION: DECT at all energy levels with metal artifact reduction software and higher energy extrapolations without metal artifact reduction software reduced metallic artifact and enhanced image interpretability compared with SECT. Radiation dose with DECT could be significantly higher than SECT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".