Determination of the optimal range for virtual monoenergetic images in dual‐energy CT based on physical quality parameters

2021 ◽  
Author(s):  
Enric Fernandez‐Velilla Cepria ◽  
Miguel Ángel González‐Ballester ◽  
Jaume Quera Jordana ◽  
Oscar Pera ◽  
Xavier Sanz Latiesas ◽  
...  
Keyword(s):  
Quality Parameters ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Optimal Range ◽  
Physical Quality ◽  
Virtual Monoenergetic Images ◽  
Monoenergetic Images

Advanced virtual monoenergetic images: improving the contrast of dual-energy CT pulmonary angiography

2015 ◽  
Vol 70 (11) ◽  
pp. 1244-1251 ◽  
Author(s):  
A. Meier ◽  
M. Wurnig ◽  
L. Desbiolles ◽  
S. Leschka ◽  
T. Frauenfelder ◽  
...  
Keyword(s):  
Pulmonary Angiography ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Ct Pulmonary Angiography ◽  
Virtual Monoenergetic Images ◽  
Monoenergetic Images

Comparison of noise-optimized linearly blended images and noise-optimized virtual monoenergetic images evaluated by dual-source, dual-energy CT in cardiac vein assessment

2020 ◽  
pp. 028418512093324
Author(s):  
Gyeong Min Kim ◽  
Ki Seok Choo ◽  
Jin Hyeok Kim ◽  
Jae-Yeon Hwang ◽  
Chan Kyu Park ◽  
...  
Keyword(s):  
Dual Energy ◽  
Venous System ◽  
Treatment Modalities ◽  
Dual Energy Ct ◽  
Great Cardiac Vein ◽  
Coronary Venous System ◽  
Virtual Monoenergetic Images ◽  
Image Set ◽  
Noise Ratio ◽  
Monoenergetic Images

Background The coronary venous system is frequently used as an entry route to the heart and treatment modalities for many cardiac diseases and many procedures. Consequently, evaluation of the coronary venous system and understanding cardiac vein anatomy is crucial. Purpose To determine the optimal image set in a comparison of noise-optimized linearly blended images (F_0.6) and noise-optimized virtual monoenergetic images (VMI+) evaluated by dual-energy computed tomography (DECT) for cardiac vein assessment. Material and Methods Thirty-four patients (mean age 58.2 ± 14.2 years) who underwent DECT due to chest pain were enrolled. Images were post-processed with the F_0.6, and VMI+ algorithms at energy levels in the range of 40–100 keV in 10-keV increments. Enhancement (HU), noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively measured at two points in the great cardiac vein by consensus of two radiologists. Two blinded observers evaluated the subjective image quality of the great cardiac vein on a 4-point scale. Results HU, noise, and SNR peaked at 40 keV VMI+ ( P < 0.05) among 50–100 keV VMI+. CNR peaked at 100 keV VMI+; however, there were no significant differences compared to CNR images processed at 40–90 keV VMI+. HU and noise were significantly higher in 40 keV VMI+ than F_0.6 images; however, both SNR and CNR were significantly higher in F_0.6 images. An assessment of subjective vein delineation revealed that F_0.6 images had the highest scores Conclusion F_0.6 images were superior to VMI+ and provided the optimal image set for cardiac vein assessment.

scholarly journals Virtual Monoenergetic Images of Dual-Energy CT—Impact on Repeatability, Reproducibility, and Classification in Radiomics

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4710
Author(s):  
André Euler ◽  
Fabian Christopher Laqua ◽  
Davide Cester ◽  
Niklas Lohaus ◽  
Thomas Sartoretti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Penalized Regression ◽  
High Energy ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Virtual Monoenergetic Images ◽  
Repeatability And Reproducibility ◽  
The Impact ◽  
Monoenergetic Images

The purpose of this study was to (i) evaluate the test–retest repeatability and reproducibility of radiomic features in virtual monoenergetic images (VMI) from dual-energy CT (DECT) depending on VMI energy (40, 50, 75, 120, 190 keV), radiation dose (5 and 15 mGy), and DECT approach (dual-source and split-filter DECT) in a phantom (ex vivo), and (ii) to assess the impact of VMI energy and feature repeatability on machine-learning-based classification in vivo in 72 patients with 72 hypodense liver lesions. Feature repeatability and reproducibility were determined by concordance–correlation–coefficient (CCC) and dynamic range (DR) ≥0.9. Test–retest repeatability was high within the same VMI energies and scan conditions (percentage of repeatable features ranging from 74% for SFDE mode at 40 keV and 15 mGy to 86% for DSDE at 190 keV and 15 mGy), while reproducibility varied substantially across different VMI energies and DECTs (percentage of reproducible features ranging from 32.8% for SFDE at 5 mGy comparing 40 with 190 keV to 99.2% for DSDE at 15 mGy comparing 40 with 50 keV). No major differences were observed between the two radiation doses (<10%) in all pair-wise comparisons. In vivo, machine learning classification using penalized regression and random forests resulted in the best discrimination of hemangiomas and metastases at low-energy VMI (40 keV), and for cysts at high-energy VMI (120 keV). Feature selection based on feature repeatability did not improve classification performance. Our results demonstrate the high repeatability of radiomics features when keeping scan and reconstruction conditions constant. Reproducibility diminished when using different VMI energies or DECT approaches. The choice of optimal VMI energy improved lesion classification in vivo and should hence be adapted to the specific task.

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