Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.
Jinjin CaoNayla MrouehSimon LennartzNathaniel D MercaldoNisanard PisuchpenSasiprang KongboonvijitShravya Srinivas RaoKampon YuenyongsinchaiTheodore T PierceMadeleine SerticRyan ChungAvinash R KambadakonePublished in: European radiology (2024)
Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population.
Keyphrases
- deep learning
- image quality
- dual energy
- convolutional neural network
- artificial intelligence
- computed tomography
- machine learning
- contrast enhanced
- air pollution
- magnetic resonance
- systematic review
- randomized controlled trial
- magnetic resonance imaging
- physical activity
- clinical trial
- optical coherence tomography
- body weight
- pet ct
- double blind