GW24-e2928 Computed tomography metal artifact reduction for evaluation of lead calcification in patients with implantable cardiac defibrillator
Bibliographic record
Abstract
Objectives Patients with implantable cardiac defibrillator (ICD) may require lead extraction if there is presence of lead fibrosis and calcification but such procedure requires specialist equipment and skills and is associated with high mortality. We investigated the effectiveness of several image acquisition, reconstruction and processing methods for metal artefact reduction in CT to facilitate its use for pre-procedural identification of lead calcification. Methods A dual coil ICD lead (Medtronic Sprint Quattro Secure 6947 M) with radiopaque beads attached was inserted into the right ventricle of an excised pig heart. The heart was filled with water and scanned in approximately the same orientation as in patients with a single energy CT (SECT) protocol using 120 kV, 120 mAs and 0.625 mm collimation on a Discovery 750HD scanner (GE Healthcare). The scan was repeated with a dual energy CT (DECT) protocol using 140/80 kV alternating every 0.2 ms and 210 mAs. Three sets of 0.625-mm-thick cardiac images were generated using the DECT scan data: (1) monochromatic 70 keV, (2) 70 keV plus ASIR (Adaptive Statistical Iterative Reconstruction, GE), (3) 70 keV plus MARS (Metal artefact Reduction Software, GE). Image set (1) to (3) were used to reduce artefacts from beam hardening, projection noise and projection truncation induced by the lead respectively. artefacts in each image set were compared against those in the 0.625 mm and 10 mm averaged SECT images. Results DECT 70 keV and 70 keV + ASIR images manifested intense shading and streaking artefacts that were minimally different from those of the 0.625 mm SECT image and the lead was not visible in all these images. 70 keV + MARS image exhibited less artefacts but the lead region was invisible. The 10 mm averaged SECT image showed the least artefacts while the lead with the attached beads was clearly seen. Conclusions DECT + MARS showed better artifact removal than DECT without MARS or with ASIR suggesting projection truncation was the dominant cause of the lead artifacts. However, MARS is unable to restore the lead image adequately. The averaging method cancelled out the artifacts while restoring the lead image with minimal compromise of the axial resolution. Lead extraction is complicated and associated with significant mortality and morbidity. The proposed method facilitates the use of CT for assessing lead calcification and the need of lead extraction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".