Abstract 132: Dual‐Energy CT in Neurointervention: A Literature Review and Institutional Experience
Bibliographic record
Abstract
Introduction/Purpose Dual‐energy computed tomography (DECT) uses two distinct X‐ray energy levels to differentiate materials based on their attenuation profiles. This allows enhanced generation of virtual non‐contrast (VNC) and iodine overlay images, as well as artifact reduction, in comparison to conventional CT. These unique features have significant implications for neurointervention, where rapid and accurate imaging is required to provide patient‐centered care.This study reviews the current literature evaluating the clinical utility of DECT in neurointervention, highlighting its applications across preoperative, intraoperative, and postoperative settings. These findings are supplemented with institutional case experiences to demonstrate situations where DECT enhanced clinician confidence and impacted patient management. Materials/Methods A PubMed search identified studies evaluating DECT for neurointerventional use, focusing on its clinical utility in preoperative, intraoperative, and postoperative use. Additionally, institutional cases performed were reviewed to highlight real‐world applications. Results The literature highlights the clinical value of DECT in neurointervention. In the context of preoperative imaging, studies demonstrate that DECT offers enhanced plaque characterization in intracranial vessels. In addition, DECT can provide a means of evaluating ischemic penumbra, allowing for the identification of regions that may be salvageable and aiding in nuanced clinical decision making. Intraoperatively, artifact reduction techniques allow for more accurate visualization near metallic devices such as coils, stents, and embolic materials. Furthermore, postoperative advantages of DECT include distinguishing between contrast material and hemorrhage and monitoring for complications.Institutional case experiences align with findings from the literature, demonstrating increased diagnostic confidence. In the context of stroke imaging, DECT offers rapid distinction between hemorrhage and contrast staining, allowing for more informed therapeutic decision‐making. These combined insights highlight the potential of DECT to improve neurointerventional workflows. Conclusion Overall, both literature and institutional experience demonstrate that DECT enhances neurointerventional imaging in preoperative, intraoperative, and postoperative uses. These capabilities have broad clinical implications, supporting integration of DECT into neurointerventional workflows. image
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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".