Analysis of the iodine distribution map in patients with diagnosis of pulmonary embolism: Initial results

Objectives: Analysing the iodine map distribution in patients with pulmonary embolism diagnosis by Dual Energy Computed Tomography. Materials and methods: Twenty-four images of pulmonary angiotomography by dual energy computed tomography were used to determinate the presence of pulmonary thrombi and identify the perfusion defects (PDs) in the Iodine Maps. Moreover, the iodine density (mg/ml) were measured in normal lung parenchyma and lung parenchyma with PDs areas. The documentary analysis was used thought the data collection sheet and the Likert scale questionnaire. The statistic software SPSS v.25 was used. Results: Thirty-four thrombi were found (21 occlusive and 13 partials occlusive) at monochromatic images. Forty-one perfusion defects (PD) were found at Iodine Maps, these have multiple origins: pulmonary thrombi (69.23%), artifacts (17.95%) and other alterations (12.82%). Furthermore, two new thrombi (5.56%) were identified, both were occlusive and segmental level. Mean Iodine density showed statistically significant differences among normal lung parenchyma (1.65 ± 0.66 mg/ml; [0.77-2.79 mg/ ml]) and parenchyma with PD areas (0.51 ± 0.26 mg/ml; [0.12-1.02 mg/ml])(p=0.000). Mean iodine density also had statistically significant differences between parenchyma with occlusive PD and partial occlusive PD (p=0.000). Iodine Map diagnostic quality was excellent (54.17%), good (33.33%), moderate (12.50%). Conclusion: The Iodine distribution Map offers a benefit greater than 5% in the diagnosis of pulmonary embolism by Dual-Energy Computed Tomography.

Quantitative benchmarking of iodine imaging for two CT spectral imaging technologies: a phantom study

Background The aim of this study was to quantitatively benchmark iodine imaging across specific virtual monoenergetic energy levels, iodine maps and virtual non-contrast images with different phantom sizes and iodine concentrations, using a rapid switching dual-energy CT (DECT) and a dual source DECT, in order to investigate accuracy and potential differences between the technologies. Methods Solutions of iodine contrast (10, 20, 30, 50, and 100 mg/mL), sterile water and saline were scanned in a phantom on a rapid switching single-source and dual-source DECT scanners from two different vendors. The phantom was equipped with polyurethane rings simulating three body sizes. The datasets were reconstructed in virtual monoenergetic energy levels (70, 80, 90, 100, 110, 120, 130, and 140 keV), virtual non-contrast images and iodine maps. HU and iodine concentrations were measured by placing ROIs in the iodine solutions. Results The iodine concentrations were reproduced with a high degree of accuracy for the single-source DECT (1.8–9.0%), showing a slight dependence on phantom size. The dual source DECT technique showed deviant values (error -33.8 to 12.0%) for high concentrations. In relation to the virtual non-contrast measurements, the images from both vendors were affected by the iodine concentration and phantom size (-127.8 to 539.1 HU). Phantom size did not affect the calculated monoenergetic attenuation values, but the attenuation values varied between the scanners. Conclusions Quantitative measurements of post-processed images are dependent on the concentration of iodine, the phantom size and different technologies. However, our study indicates that the iodine maps are reliable for quantification of iodine.

Evaluation of Diagnostic Accuracy and Radiation Exposure of Dual-Energy Computed Tomography (DECT) in the Course of Chronic Thromboembolic Pulmonary Hypertension (CTEPH)

Purpose The purpose of this study was to assess the diagnostic accuracy of computed tomography pulmonary angiogram (CTPA) including dual energy and reconstruction of iodine maps for diagnosing CTEPH. This method for detecting embolisms and perfusion failures was compared with V/Q-SPECT. An additional purpose was to compare the applied radiation dose of both techniques. Materials and Methods 71 patients (49 women) with suspected CTEPH were included in this prospective study. The patients received a V/Q-SPECT and a dual-energy CTPA. Iodine maps were reconstructed from the data set. CTPA and the iodine maps were read by an experienced radiologist unaware of the clinical information as well as the results of the V/Q-SPECT. Results were compared to the V/Q-SPECT. DLP and the applied amount of radionuclides (MAA, Technegas) were obtained for comparison of radiation dose. Results For the diagnosis of CTEPH, the sensitivity of DECT was 1.000, specificity 0.966, PPV 0.867 and NPV 1.000, respectively. There was not a considerable difference in the x-ray exposure between the DECT examination and the V/Q-SPECT (1.892 mSv vs. 1.911 mSv; p = 0.6115). Both examination modalities were highly consistent regarding the classification of pathological segments (1177/1278 segments, 92,09 %, κ = 0,5938). Conclusion This study presents the DECT, in combination with reconstructed iodine maps, as a potential alternative to the current imaging technique of first choice, V/Q-SPECT. For creating future prospective diagnostic algorithms, the implementation of DECT screening with iodine maps should be considered. Key Points: Citation Format

Value of spectral detector computed tomography for the early assessment of technique efficacy after microwave ablation of hepatocellular carcinoma

Objectives To investigate whether virtual monoenergetic images (VMI) and iodine maps derived from spectral detector computed tomography (SDCT) improve early assessment of technique efficacy in patients who underwent microwave ablation (MWA) for hepatocellular carcinoma (HCC) in liver cirrhosis. Methods This retrospective study comprised 39 patients with 49 HCC lesions treated with MWA. Biphasic SDCT was performed 7.7±4.0 days after ablation. Conventional images (CI), VMI and IM were reconstructed. Signal- and contrast-to-noise ratio (SNR, CNR) in the ablation zone (AZ), hyperemic rim (HR) and liver parenchyma were calculated using regions-of-interest analysis and compared between CI and VMI between 40–100 keV. Iodine concentration and perfusion ratio of HR and residual tumor (RT) were measured. Two readers evaluated subjective contrast of AZ and HR, technique efficacy (complete vs. incomplete ablation) and diagnostic confidence at determining technique efficacy. Results Attenuation of liver parenchyma, HR and RT, SNR of liver parenchyma and HR, CNR of AZ and HR were significantly higher in low-keV VMI compared to CI (all p<0.05). Iodine concentration and perfusion ratio differed significantly between HR and RT (all p<0.05; e.g. iodine concentration, 1.6±0.5 vs. 2.7±1.3 mg/ml). VMI50keV improved subjective AZ-to-liver contrast, HR-to-liver contrast, visualization of AZ margin and vessels adjacent to AZ compared to CI (all p<0.05). Diagnostic accuracy for detection of incomplete ablation was slightly higher in VMI50keV compared to CI (0.92 vs. 0.89), while diagnostic confidence was significantly higher in VMI50keV (p<0.05). Conclusions Spectral detector computed tomography derived low-keV virtual monoenergetic images and iodine maps provide superior early assessment of technique efficacy of MWA in HCC compared to CI.

Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status

Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.

Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules

Background The purpose of this study was to analyze if the use of texture analysis on spectral detector CT (SDCT)-derived iodine maps (IM) in addition to conventional images (CI) improves lung nodule differentiation, when being applied to a k-nearest neighbor (KNN) classifier. Methods 183 cancer patients who underwent contrast-enhanced, venous phase SDCT of the chest were included: 85 patients with 146 benign lung nodules (BLN) confirmed by either prior/follow-up CT or histopathology and 98 patients with 425 lung metastases (LM) verified by histopathology, 18F-FDG-PET-CT or unequivocal change during treatment. Semi-automatic 3D segmentation of BLN/LM was performed, and volumetric HU attenuation and iodine concentration were acquired. For conventional images and iodine maps, average, standard deviation, entropy, kurtosis, mean of the positive pixels (MPP), skewness, uniformity and uniformity of the positive pixels (UPP) within the volumes of interests were calculated. All acquired parameters were transferred to a KNN classifier. Results Differentiation between BLN and LM was most accurate, when using all CI-derived features combined with the most significant IM-derived feature, entropy (Accuracy:0.87; F1/Dice:0.92). However, differentiation accuracy based on the 4 most powerful CI-derived features performed only slightly inferior (Accuracy:0.84; F1/Dice:0.89, p=0.125). Mono-parametric lung nodule differentiation based on either feature alone (i.e. attenuation or iodine concentration) was poor (AUC=0.65, 0.58, respectively). Conclusions First-order texture feature analysis of contrast-enhanced staging SDCT scans of the chest yield accurate differentiation between benign and metastatic lung nodules. In our study cohort, the most powerful iodine map-derived feature slightly, yet insignificantly increased classification accuracy compared to classification based on conventional image features only.