Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Euler A

PROVIDER: S-EPMC8467875 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesTo evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms.MethodsA phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated.Results92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI70keV (r2 0.3870-0.6178, all p < 0.001).ConclusionsThe majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.

Project description:BackgroundPhoton-counting detector computed tomography (PCD-CT) may influence imaging characteristics for various clinical conditions due to higher signal and contrast-to-noise ratio in virtual monoenergetic images (VMI). Radiomics analysis relies on quantification of image characteristics. We evaluated the impact of different VMI reconstructions on radiomic features in in vitro and in vivo PCD-CT datasets.MethodsAn organic phantom consisting of twelve samples (four oranges, four onions, and four apples) was scanned five times. Twenty-three patients who had undergone coronary computed tomography angiography on a first generation PCD-CT system with the same image acquisitions were analyzed. VMIs were reconstructed at 6 keV levels (40, 55, 70, 90, 120, and 190 keV). The phantoms and the patients' left ventricular myocardium (LVM) were segmented for all reconstructions. Ninety-three original radiomic features were extracted. Repeatability and reproducibility were evaluated through intraclass correlations coefficient (ICC) and post hoc paired samples ANOVA t test.ResultsThere was excellent repeatability for radiomic features in phantom scans (all ICC = 1.00). Among all VMIs, 36/93 radiomic features (38.7%) in apples, 28/93 (30.1%) in oranges, and 33/93 (35.5%) in onions were not significantly different. For LVM, the percentage of stable features was high between VMIs ≥ 90 keV (90 versus 120 keV, 77.4%; 90 versus 190 keV, 83.9%; 120 versus 190 keV, 89.3%), while comparison to lower VMI levels led to fewer reproducible features (40 versus 55 keV, 8.6%).ConclusionsVMI levels influence the stability of radiomic features in an organic phantom and patients' LVM; stability decreases considerably below 90 keV.Relevance statementSpectral reconstructions significantly influence radiomic features in vitro and in vivo, necessitating standardization and careful attention to these reconstruction parameters before clinical implementation.Key points• Radiomic features have an excellent repeatability within the same PCD-CT acquisition and reconstruction. • Differences in VMI lead to decreased reproducibility for radiomic features. • VMI ≥ 90 keV increased the reproducibility of the radiomic features.

Project description:BackgroundDual-energy computed tomography (CT) can provide a range of image information beyond conventional CT through virtual monoenergetic images (VMIs). The purpose of this study was to investigate the impact of material decomposition in detector-based spectral CT on radiomics features and effectiveness of using deep learning-based image synthesis to improve the reproducibility of radiomics features.MethodsIn this paper, spectral CT image data from 45 esophageal cancer patients were collected for investigation retrospectively. First, we computed the correlation coefficient of radiomics features between conventional kilovoltage peak (kVp) CT images and VMI. Then, a wavelet loss-enhanced CycleGAN (WLL-CycleGAN) with paired loss terms was developed to synthesize virtual monoenergetic CT images from the corresponding conventional single-energy CT (SECT) images for improving radiomics reproducibility. Finally, the radiomic features in 6 different categories, including gray-level co-occurrence matrix (GLCM), gray-level difference matrix (GLDM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and wavelet, were extracted from the gross tumor volumes from conventional single energy CT, synthetic virtual monoenergetic CT images, and virtual monoenergetic CT images. Comparison between errors in the VMI and synthetic VMI (sVMI) suggested that the performance of our proposed deep learning method improved the radiomic feature accuracy.ResultsMaterial decomposition of dual-layer dual-energy CT (DECT) can substantially influence the reproducibility of the radiomic features, and the degree of impact is feature dependent. The average reduction of radiomics errors for 15 patients in testing sets was 96.9% for first-order, 12.1% for GLCM, 12.9% for GLDM, 15.7% for GLRLM, 50.3% for GLSZM, 53.4% for NGTDM, and 6% for wavelet features.ConclusionsThe work revealed that material decomposition has a significant effect on the radiomic feature values. The deep learning-based method reduced the influence of material decomposition in VMIs and might improve the robustness and reproducibility of radiomic features in esophageal cancer. Quantitative results demonstrated that our proposed wavelet loss-enhanced paired CycleGAN outperforms the original CycleGAN.

Project description:ObjectivesTo evaluate the image quality and utility of virtual monoenergetic images (VMI) of dual-layer spectrum computed tomography (DLSCT) in assessing preoperative T-stage for early rectal adenocarcinoma (ERA).MethodsThis retrospective study included 67 ERA patients (mean age 62 ± 11.1 years) who underwent DLSCT and MR examination. VMI 40-200 keV and poly energetic image (PEI) were reconstructed. The image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and tumor contrast of different energy levels were calculated and compared, respectively. Two radiologists independently assess the image quality of the VMIs and PEI using 5-point scales. The diagnostic accuracies of DLSCT and HR-MRI for ERA T-staging were evaluated and compared.ResultsThe maximum noise was observed at VMI 40 keV, and noise at VMI 40-200 keV in the arterial and venous phases showed no significant difference (all p > 0.05). The highest SNR and CNR were obtained at VMI 40 keV, significantly greater than other energy levels and PEI (all p < 0.05). Tumor contrast was more evident than PEI at 40-100 keV in the arterial phase and at 40 keV in the venous phase (all p < 0.05). When compared with PEI, VMI 40 keV yielded the highest scores for overall image quality, tumor visibility, and tumor margin delineation, especially in the venous phase (p < 0.05). The overall diagnostic accuracy of DLSCT and HR-MRI for T-stage was 65.67 and 71.64% and showed no significant difference (p > 0.05).ConclusionsVMI 40 keV improves image quality and accuracy in identifying lesions, providing better diagnostic information for ERA staging.Critical relevance statementLow-keV VMI from DLSCT can improve tumor staging accuracy for early rectal carcinoma, helping guide surgical intervention decisions, and has shed new light on the potential breakthroughs of assessing preoperative T-stage in RC.Keypoints• Compared with PEI, low-keV VIM derived from DLSCT, particularly at the 40 keV, significantly enhanced the objective and subjective image quality of ERA. • Using VMI 40 keV helped increase lesion detectability, leading to improved diagnostic accuracy for ERA. • Low-keV VMI from DLSCT has shed new light on the potential breakthroughs of assessing preoperative T-stage in RC.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets