Dual-Energy CT for the Detection of Portal Vein Thrombosis: Improved Diagnostic Performance Using Virtual Monoenergetic Reconstructions.
Simon S MartinJetlir KolaneciRouben CzwiklaChristian BoozLeon David GruenewaldMoritz H AlbrechtZachary M ThompsonLukas LengaIbrahim YelThomas J VoglJulian L WichmannVitali KochPublished in: Diagnostics (Basel, Switzerland) (2022)
Purpose: To investigate the diagnostic performance of noise-optimized virtual monoenergetic images (VMI+) in dual-energy CT (DECT) of portal vein thrombosis (PVT) compared to standard reconstructions. Method: This retrospective, single-center study included 107 patients (68 men; mean age, 60.1 ± 10.7 years) with malignant or cirrhotic liver disease and suspected PVT who had undergone contrast-enhanced portal-phase DECT of the abdomen. Linearly blended (M_0.6) and virtual monoenergetic images were calculated using both standard VMI and noise-optimized VMI+ algorithms in 20 keV increments from 40 to 100 keV. Quantitative measurements were performed in the portal vein for objective contrast-to-noise ratio (CNR) calculation. The image series showing the greatest CNR were further assessed for subjective image quality and diagnostic accuracy of PVT detection by two blinded radiologists. Results: PVT was present in 38 subjects. VMI+ reconstructions at 40 keV revealed the best objective image quality (CNR, 9.6 ± 4.3) compared to all other image reconstructions ( p < 0.01). In the standard VMI series, CNR peaked at 60 keV (CNR, 4.7 ± 2.1). Qualitative image parameters showed the highest image quality rating scores for the 60 keV VMI+ series (median, 4) ( p ≤ 0.03). The greatest diagnostic accuracy for the diagnosis of PVT was found for the 40 keV VMI+ series (sensitivity, 96%; specificity, 96%) compared to M_0.6 images (sensitivity, 87%; specificity, 92%), 60 keV VMI (sensitivity, 87%; specificity, 97%), and 60 keV VMI+ reconstructions (sensitivity, 92%; specificity, 97%) ( p ≤ 0.01). Conclusions: Low-keV VMI+ reconstructions resulted in significantly improved diagnostic performance for the detection of PVT compared to other DECT reconstruction algorithms.
Keyphrases
- dual energy
- image quality
- computed tomography
- contrast enhanced
- deep learning
- diffusion weighted
- machine learning
- convolutional neural network
- artificial intelligence
- pulmonary embolism
- air pollution
- end stage renal disease
- chronic kidney disease
- optical coherence tomography
- ejection fraction
- systematic review
- high resolution
- newly diagnosed
- clinical trial
- label free
- diffusion weighted imaging
- mass spectrometry
- cross sectional
- randomized controlled trial
- real time pcr
- patient reported outcomes
- sleep quality
- single cell
- double blind
- middle aged