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Record W2769762815 · doi:10.1148/rg.2017170100

Update on Cardiovascular Applications of Multienergy CT

2017· article· en· W2769762815 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRadiographics · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineScannerDual energyImage resolutionSensitivity (control systems)IodineProjection (relational algebra)Nuclear medicineTomographyRadiologyComputer visionArtificial intelligenceComputer sciencePathologyMaterials science

Abstract

fetched live from OpenAlex

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

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.219
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it