Update on Cardiovascular Applications of Multienergy CT
Bibliographic record
Abstract
Advances in scanner technology enabling shorter scan times, improvements in spatial and temporal resolution, and more dose-efficient data reconstruction coupled with rapidly growing evidence from clinical trials have established computed tomography (CT) as an important imaging modality in the evaluation of cardiovascular disorders. Multienergy (or spectral or dual-energy) CT is a relatively recent advance in which attenuation data from different energies are used to characterize materials beyond what is possible at conventional CT. Current technologies for multienergy CT are either source based (ie, dual source, rapid kilovoltage switching, dual spin, and split beam) or detector based (ie, dual layer and photon counting), and material-based decomposition occurs in either image or projection space. In addition to conventional diagnostic images, multienergy CT provides image sets such as iodine maps, virtual nonenhanced, effective atomic number, and virtual monoenergy (VM) images as well as data at the elemental level (CT fingerprinting), which can complement and in some areas overcome the limitations posed by conventional CT methods. In myocardial perfusion imaging, iodine maps improve the sensitivity of perfusion defects, and VM images improve the specificity by decreasing artifacts. Iodine maps are also useful in improving the performance of CT in delayed-enhancement imaging. In pulmonary perfusion imaging, iodine maps enhance the sensitivity of detection of both acute and chronic pulmonary emboli. Low-energy (as measured in kiloelectron volts) VM images allow enhancement of vascular contrast, which can either be used to lower contrast dose or salvage a suboptimal contrast-enhanced study. High-energy VM images can be used to decrease or eliminate artifacts such as beam-hardening and metallic artifacts. Virtual nonenhanced images have similar attenuation as true nonenhanced images and help in reducing radiation dose by eliminating the need for the latter in multiphasic vascular studies. Other potential applications of multienergy CT include calcium scoring from virtual nonenhanced images created from coronary CT angiograms and myocardial iron quantification. Online supplemental material is available for this article. ©RSNA, 2017
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".