Virtual Monochromatic Images Research Articles

The optimal energy level of virtual monochromatic imaging in Dual-energy CT arthrography of the wrist.

To suggest an optimal energy level of virtual monochromatic images (VMIs) in dual-energy CT arthrography of the wrist. This retrospective study included 53 patients with wrist CT arthrography. Conventional polychromatic images and VMIs at four energy levels (40 to 70 keV at 10 keV intervals) were obtained. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured, and qualitative analysis of image quality and diagnostic confidence was performed. For each patient, an energy level with the best image quality was chosen by consensus. Comparisons of quantitative and qualitative parameters between VMI sets were performed. The image noise of bone and muscle were increased with decreasing energy level (p < 0.001). The noise of contrast was highest on 60 keV VMI. SNR and CNR (between contrast and muscle) were increased with decreasing energy level and were markedly increased between 60 and 50 keV (p < 0.001). The 60 keV VMI demonstrated the highest image quality and diagnostic confidence, chosen as the best diagnostic image (n = 31/53). Given that the attenuation of the contrast material was low on the conventional image, the optimal energy level of the best VMI tended to be low. Wrist dual-energy CT arthrography with VMIs at 60 keV or less could improve image quality and diagnostic performance by increasing SNR and CNR in cases with low contrast attenuation. Wrist dual-energy CT arthrography with VMIs at variable keV could be utilised to enhance SNR and CNR, thereby achieving diagnostic images of high quality.

Dual Energy Metal Artifact Reduction for Iodine-125 Seed Identification in Postimplant CT after Prostate Brachytherapy.

To assess the metal artifact reductions of dual-energy computed tomography (DECT) high-energy virtual monochromatic images (VMI) combined with the single energy metal artifact reduction (SEMER) (CANON MEDICAL SYSTEMS, Otawara, Japan) processing techniques for iodine (I)-125 seed identification in postimplant computed tomography (CT) after prostate brachytherapy. Dual-energy acquisition with fast tube voltage switching was performed on a prostate phantom with simulated seeds and six clinical cases treated with I-125 prostate brachytherapy. The images were retrospectively reconstructed at VMI energy levels of 65-200 keV and with and without SEMAR (SEMAR and non-SEMAR images). To estimate seed swelling, the caliber of iodine-125 seed was calculated as the full width at half maximum. The metal artifacts were evaluated using the artifact index (AI). The dose distributions were calculated and were compared among the high-energy VMI (SEMAR and non-SEMAR images) and low-energy VMI (SEMAR images). The blooming artifacts decreased at higher energy levels. In addition, the SEMAR process markedly reduced AI, which helped reduce overestimation of high dose ranges in the treatment planning dose map. The locations and number of iodine-125 seed were clearly identified in the dose distribution map of the treatment planning using 200keV VMI with SEMAR. The high-energy VMI of the dual energy CT in combination with SEMAR is appropriate for the postimplant planning process of I-125 prostate brachytherapy.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator(LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine(SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BackgroundThis study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.MethodsNon-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group.ResultsWhen distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).ConclusionThe AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Iodine-Based Dual-Energy Computed Tomography After Mechanical Thrombectomy Predicts Secondary Neurologic Decline from Cerebral Edema After Severe Stroke.

Patients with severe stroke are at high risk of secondary neurologic decline (ND) from the development of malignant cerebral edema (MCE). However, early infarcts are hard to diagnose on conventional head computed tomography (CT). We hypothesize that high-energy (190keV) virtual monochromatic imaging (VMI) from dual-energy CT (DECT) imaging enables earlier detection of ND from MCE. Consecutive patients with severe stroke with National Institute of Health Stroke Scale (NIHSS) scores > 15 and DECT within 10h of mechanical thrombectomy from May 2020 to March 2022 were included. We excluded patients with parenchymal hematoma type 2 transformation. Retrospective analysis of clinical and novel variables included the VMI Alberta Stroke Program Early CT Score (ASPECTS), total iodine content, and VMI infarct volume. The primary outcome was secondary ND, defined using a composite outcome variable of clinical worsening (increase in NIHSS score ≥ 4 or decrease in Glasgow Coma Scale score > 2) or malignant radiographical edema (midline shift ≥ 5mm at the level of the septum pellucidum). Fisher's exact test and Wilcoxon's test were used for univariate analysis. Logistic regression was used to develop prediction models for categorical outcomes. Eighty-four patients with severe stroke with a median age of 67.5 (interquartile range [IQR] 57-78) years and an NIHSS score of 22 (IQR 18-25) were included. Twenty-nine patients had ND. The VMI ASPECTS, total iodine content, and VMI infarct volume were associated with ND. The VMI ASPECTS, VMI infarct volume, and total iodine content were predictors of ND after adjusting for age, sex, initial NIHSS score, and tissue plasminogen activator administration, with areas under the receiver operating characteristic curve (AUROC) of 0.691 (95% confidence interval [CI] 0.572-0.810), 0.877 (95% CI 0.800-0.954), and 0.845 (95% CI 0.750-0.940). By including all three predictors, the model achieved an AUROC of 0.903 (95% CI 0.84-0.97) and was cross-validated by the leave one out method, with an AUROC of 0.827. The VMI ASPECTS and VMI infarct volume from DECT are superior to the conventional CT ASPECTS and are novel predictors for secondary ND due to MCE after severe stroke. Clinical trial registration ClinicalTrials.gov identifier: NCT04189471.

Hybrid modality dual-energy imaging aggregating complementary advantages of kV-CT and MV-CBCT: concept proposal and clinical validation

Megavoltage cone-beam CT (MV-CBCT) is advantageous in metal artifact reduction during Image-Guided Radiotherapy (IGRT), although it is limited by poor soft tissue contrast. This study proposed and evaluated a novel hybrid modality dual-energy (DE) imaging method combining the complementary advantages of kV-CT and MV-CBCT.&#xD;Approach: The kV-CT and MV-CBCT images were acquired on a planning CT scanner and a Halcyon linear accelerator respectively. After rigid registration, images of basis materials were generated using the iterative decomposition method in the volumetric images. The decomposition accuracy was quantitatively evaluated on a Gammex 1472 phantom. The performance of contrast enhancement and metal artifact reduction in virtual monochromatic images were evaluated on both phantom and patient studies.&#xD;Main results: Using the proposed method, the mean percentage errors for RED and SPR were 0.90% and 0.81%, outperforming the clinical single-energy mapping method with mean errors of 1.28% and 1.07%, respectively. The contrasts of soft-tissue insets were enhanced by a factor of 2~3 at 40 keV compared to kV-CT. The standard deviation in the metal artifact area was reduced by ~67%, from 42 HU (kV-CT) to 14 HU (150 keV monochromatic). The head and neck patient test showed that the percent error of soft-tissue RED in the metal artifact area was reduced from 18.1% (HU-RED conversion) to less than 1.0% (the proposed method), which was equivalent to the maximum dosimetric difference of 28.7% based on the patient-specific plan.&#xD;Significance: Without hardware modification or extra imaging dose, the proposed hybrid modality method enabled kV-MV DE imaging, providing improved accuracy of quantitative analysis, soft-tissue contrast and metal artifact suppression for more accurate IGRT.&#xD.

Assessment of solitary pulmonary nodules using dual-layer spectral detector computed tomography.

We aim to quantitatively investigate the difference between benign and malignant solid pulmonary nodules that appeared on dual-energy spectral computed tomography, and assess the diagnostic accuracy of several parameters derived from computed tomography in differentiating malignant from benign pulmonary nodules. Between September 2021 and December 2022, spectral images of 71 patients (male:female = 44:27, mean age = 71.0 years) confirmed by pathology were retrospectively analyzed in the venous phase. Patients were classified into the malignant group and the benign group. The iodine concentration values of the nodules, normalized iodine concentration of the nodules to the neighboring vessels, virtual monochromatic images of 40 and 80 keV, and slope of the spectral curve were calculated and compared between the benign and malignant groups. Receiver operating characteristic curves and the area under the curve were performed to assess the diagnostic performance of the above parameters. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. The malignant pulmonary nodules have higher iodine density and slope of the spectral curve than the benign lesions. The combined model of iodine density and curve slope with an optimal cutoff of 0.39 (area under the curve = 0.82) yielded a sensitivity of 95% and a specificity of 63%. Contrast-enhanced dual-energy spectral computed tomography allows promising capability of distinguishing malignant from benign lesions, potential for avoiding unnecessary invasive procedure or surgery.

Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts.

Ischemic changes are not visible on non-contrast head CT until several hours after infarction, though deep convolutional neural networks have shown promise in the detection of subtle imaging findings. This study aims to assess if dual-energy CT (DECT) acquisition can improve early infarct visibility for machine learning. The retrospective dataset consisted of 330 DECTs acquired up to 48h prior to confirmation of a DWI positive infarct on MRI between 2016 and 2022. Infarct segmentation maps were generated from the MRI and co-registered to the CT to serve as ground truth for segmentation. A self-configuring 3D nnU-Net was trained for segmentation on (1) standard 120kV mixed-images (2) 190keV virtual monochromatic images and (3) 120kV + 190keV images as dual channel inputs. Algorithm performance was assessed with Dice scores with paired t-tests on a test set. Global aggregate Dice scores were 0.616, 0.645, and 0.665 for standard 120kV images, 190keV, and combined channel inputs respectively. Differences in overall Dice scores were statistically significant with highest performance for combined channel inputs (p < 0.01). Small but statistically significant differences were observed for infarcts between 6 and 12h from last-known-well with higher performance for larger infarcts. Volumetric accuracy trended higher with combined inputs but differences were not statistically significant (p = 0.07). Supplementation of standard head CT images with dual-energy data provides earlier and more accurate segmentation of infarcts for machine learning particularly between 6 and 12h after last-known-well.

Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction – A retrospective observational study

PurposeTo compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images from dual-energy computed tomography (DECT) for the evaluation of head and neck neoplasms with CT scans from a conventional single-energy protocol. MethodA total of 294 head and neck CT scans (98 VMIs operated at 60 keV, 102 MD iodine images, and 94 images from a 120 kVp single-energy CT (SECT) protocol) were retrospectively evaluated. VMIs and MD iodine images were generated using the Gemstone Spectral Imaging (GSI) mode using DLIR and metal artifact reduction (MAR) algorithms. SECT images were generated using adaptive statistical iterative reconstruction (ASIR-V). Images were scored by two independent readers on a 6-point Likert-type scale for overall image quality, vessel contrast, soft tissue contrast, noise texture, noise intensity, artifact reduction, and sharpness. ResultsSubjective overall image quality was rated as superior or excellent in 98 % of DLIR-based MD iodine images and VMIs, but only in 55 % of ASIR-V-based SECT images. For each individual quality criterion, image quality of VMIs and MD iodine images was rated as better than that of SECT images (p < 0.001 in each case). Noise texture and intensity were rated better in MD iodine images than in VMIs. ConclusionDECT using both DLIR and MAR for the generation of VMIs and MD iodine images resulted in higher subjective quality of oncologic head and neck images than ASIR-V-based SECT. Noise reduction and noise texture were best achieved with DLIR-based MD iodine images.

Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.

The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain. This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images. In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE). In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material. This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.

A paradigm shift in oncology imaging: a prospective cross-sectional study to assess low-dose deep learning image reconstruction versus standard-dose iterative reconstruction for comprehensive lesion detection in dual-energy computed tomography.

Low-kiloelectron volt (keV) virtual monochromatic images (VMIs) from low-dose (LD) dual-energy computed tomography (DECT) can enhance lesion contrast but suffer from high image noise. Recently, a deep learning image reconstruction (DLIR) algorithm has been developed and shown significant potential in suppressing image noise and improving image quality. To date, the capacity of LD low-keV thoracic-abdominal-pelvic DECT with DLIR to detect various types of tumor lesions have not been assessed. Hence, this study aimed to evaluate the image quality and lesion detection capabilities of LD VMIs using DLIR with thoracic-abdominal-pelvic DECT versus standard-dose (SD) iterative reconstruction (IR) in oncology patients. This prospective intraindividual study included 56 oncology patients who received a SD (13.86 mGy) and a consecutive LD (7.15 mGy) thoracic-abdominal-pelvic DECT from April 2022 to July 2023 at The First Affiliated hospital of Zhengzhou University. SD VMIs were reconstructed using IR at 50 keV (SD-IR50 keV), while LD VMIs were processed using DLIR at 50 keV (LD-DL50 keV) and 40 keV (LD-DL40 keV), respectively. Quantitative image parameters [computed tomography (CT) values, image noise, and contrast-to-noise ratios (CNRs)], qualitative metrics (image noise, vessel conspicuity, image contrast, artificial sensation, and overall image quality), and lesion CNRs and conspicuity were compared. The lesion detection rates in the SD-IR50 keV, LD-DL50 keV, and LD-DL40 keV VMIs were assessed according to lesion location (lung, liver, and lymph), type, and size. Repeated measures analysis of variance and the Friedman test were applied for comparing quantitative and qualitative measures, respectively. The Cochran Q test was used for comparing lesion detection rates. Compared to SD-IR50 keV VMIs, LD-DL50 keV VMIs showed similar CT values and image noise (P>0.05), similar (P>0.05) or higher(P<0.05) CNRs, similar (P>0.05) or superior (P<0.05) perceptual image quality, and similar (P>0.05) or higher (P<0.001) lesion CNR and conspicuity. LD-DL40 keV VMIs exhibited higher CT values (by 40.4-47.1%) and CNRs (by 21.8-39.8%) (P<0.001), equivalent image noise, similar (P>0.05) or superior (P<0.05) perceptual image quality except for artificial sensation, and similar (P>0.05) or higher (P<0.001) lesion CNRs (by 16.5-46.3%) and conspicuity. The VMIs of LD-DL50 keV and LD-DL40 keV were consistent with those of SD-IR50 keV in terms of lesion detection capability in pulmonary nodules [SD-IR50 keV vs. LD-DL50 keV vs. LD-DL40 keV: 88/88 (100.0%) vs. 88/88 (100.0%) vs. 88/88 (100.0%); P>0.99], for lymph nodes [125/126 (99.2%) vs. 123/126 (97.6%) vs. 124/126 (98.4%); P>0.05], and high-contrast liver lesions [12/12 (100.0%) vs. 12/12 (100.0%) vs. 12/12 (100.0%); P>0.05], but not for small liver lesions (≤0.5 cm) [63/65 (96.9%) vs. 43/65 (66.2%) vs. 51/65 (78.5%); P<0.05] or low-contrast liver lesions [198/200 (99.0%) vs. 174/200 (87.0%) vs. 183/200 (91.5%); P<0.05]. VMIs at 40 keV with DLIR enables a 50% decrease in the radiation dose while largely maintaining diagnostic capabilities for multidetection of pulmonary nodules, lymph nodes, and liver lesions in oncology patients.

Spectral CT in the evaluation of perineural invasion status in rectal cancer.

To investigate whether preoperative spectral CT quantitative parameters can assess perineural invasion (PNI) status in rectal cancer. Sixty-two patients diagnosed with rectal cancer who underwent preoperative spectral CT were retrospectively enrolled and divided into positive and negative PNI groups according to histopathologic results. The CT attenuation value (HU) of virtual monochromatic images (40-70keV), spectral curve slope (K(HU)), effective atomic number (Zeff), and iodine concentration (IC) from spectral CT were compared between these two groups using t test or rank sum test. A nomogram was established by incorporating the independent predictors to assess the overall diagnostic efficacy. The area under the ROC curves (AUCs) were compared using the DeLong test. The preoperative spectral CT parameters (40-70keV attenuation, K(HU), Zeff, and IC) were significantly higher in the PNI-positive group compared to the PNI-negative group (all p < 0.05). The highest predictive efficiency of PNI was observed at 40keV attenuation, with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.847, 81.8%, 72.5%, and 75.8%, respectively. Binary logistic regression demonstrated that the clinical feature (cN stage) and 40keV attenuation were independent predictors of PNI status. The nomogram incorporating these two predictors (cN stage and 40keV attenuation) exhibited the best evaluation efficacy, with an AUC, sensitivity, specificity, and accuracy of 0.885, 86.4%, 77.5%, and 80.6%. Spectral CT quantitative parameters proved valuable in the preoperative assessment of PNI status in rectal cancer patients. The combination of spectral CT parameters and clinical features could further enhance the diagnostic efficiency.

The Usefulness of Low-Kiloelectron Volt Virtual Monochromatic Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction Technique in Improving the Delineation of Pancreatic Ductal Adenocarcinoma.

To evaluate the usefulness of low-keV multiphasic computed tomography (CT) with deep learning image reconstruction (DLIR) in improving the delineation of pancreatic ductal adenocarcinoma (PDAC) compared to conventional hybrid iterative reconstruction (HIR). Thirty-five patients with PDAC who underwent multiphasic CT were retrospectively evaluated. Raw data were reconstructed with two energy levels (40keV and 70keV) of virtual monochromatic imaging (VMI) using HIR (ASiR-V50%) and DLIR (TrueFidelity-H). Contrast-to-noise ratio (CNRtumor) was calculated from the CT values within regions of interest in tumor and normal pancreas in the pancreatic parenchymal phase images. Lesion conspicuity of PDAC in pancreatic parenchymal phase on 40-keV HIR, 40-keV DLIR, and 70-keV DLIR images was qualitatively rated on a 5-point scale, using 70-keV HIR images as reference (score 1 = poor; score 3 = equivalent to reference; score 5 = excellent) by two radiologists. CNRtumor of 40-keV DLIR images (median 10.4, interquartile range (IQR) 7.8-14.9) was significantly higher than that of the other VMIs (40keV HIR, median 6.2, IQR 4.4-8.5, P < 0.0001; 70-keV DLIR, median 6.3, IQR 5.1-9.9, P = 0.0002; 70-keV HIR, median 4.2, IQR 3.1-6.1, P < 0.0001). CNRtumor of 40-keV DLIR images were significantly better than those of the 40-keV HIR and 70-keV HIR images by 72 ± 22% and 211 ± 340%, respectively. Lesion conspicuity scores on 40-keV DLIR images (observer 1, 4.5 ± 0.7; observer 2, 3.4 ± 0.5) were significantly higher than on 40-keV HIR (observer 1, 3.3 ± 0.9, P < 0.0001; observer 2, 3.1 ± 0.4, P = 0.013). DLIR is a promising reconstruction method to improve PDAC delineation in 40-keV VMI at the pancreatic parenchymal phase compared to conventional HIR.

Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study.

To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems. A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems. The radiomics features were non-reproducible with significant variability in values among different CT techniques. Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine. CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.

Improving diagnostic confidence in low-dose dual-energy CTE with low energy level and deep learning reconstruction

ObjectiveTo demonstrate the value of using 50 keV virtual monochromatic images with deep learning image reconstruction (DLIR) in low-dose dual-energy CT enterography (CTE). MethodsIn this prospective study, 114 participants (62 % M; 41.9 ± 16 years) underwent dual-energy CTE. The early-enteric phase was performed using standard-dose (noise index (NI): 8) and images were reconstructed at 70 keV and 50 keV with 40 % strength ASIR-V (ASIR-V40%). The late-enteric phase used low-dose (NI: 12) and images were reconstructed at 50 keV with ASIR-V40%, and DLIR at medium (DLIR-M) and high strength (DLIR-H). Image standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge-rise-slope (ERS) were computed. The quantitative comb sign score was calculated for the 27 patients with Crohn’s disease. The subjective noise, image contrast, display of rectus artery were scored using a 5-point scale by two radiologists blindly. ResultsEffective dose was reduced by 50 % (P < 0.001) in the late-enteric phase to 3.26 mSv. The lower-dose 50 keV-DLIR-H images (SD:17.7 ± 0.5HU) had similar image noise (P = 0.97) as the standard-dose 70 keV-ASIR-V40% images (SD:17.7 ± 0.73HU), but with higher (P < 0.001) SNR, CNR, ERS and quantitative comb sign score (5.7 ± 0.17, 1.8 ± 0.12, 156.04 ± 5.21 and 5.05 ± 0.73, respectively). Furthermore, the lower-dose 50 keV-DLIR-H images obtained the highest score in the rectus artery visibility (4.27 ± 0.6). ConclusionsThe 50 keV images in dual-energy CTE with DLIR provides high-quality images, with a 50 % reduction in radiation dose. Images with high contrast and density resolutions significantly enhance the diagnostic confidence of Crohn’s disease and are essential for the clinical development of individualized treatment plans.

The utility of virtual monochromatic dual-energy computed tomography (DECT) in meniscal imaging: a technical evaluation.

To determine the feasibility of meniscal imaging using virtual monochromatic images obtained through dualenergy computed tomography (DECT) technique, and to determine which keV levels optimise contrast resolution. All DECT exams were performed on a Discovery CT750 HD system from GE Healthcare. Virtual monochromatic images were reconstructed at 40 keV, 73 keV, 106 keV, and 139 keV. Contrast resolution of the medial and lateral menisci using a 5-point Likert scale at each keV level was determined through a consensus agreement by 2 fellowship-trained musculoskeletal radiologists. Friedman's and Wilcoxon signed rank tests were used to compare visualisation scores across different keV levels. Seventeen knee exams from 10 patients met criteria for inclusion in the study. All patients included in the study cohort were male. The median age of patients was 46 years (interquartile range, 35-53 years). Virtual monochromatic images at 40 keV demonstrated highest contrast resolution of the menisci, with a statistically significant difference between contrast resolution scores at 40 keV and 76 keV, Friedman test: p < 0.0001. Meniscal imaging is feasible using DECT virtual monochromatic images at low keV levels. Improved contrast resolution at these specified KeV values could pave the way for further research in this field to determine its role in the future as an alternative option for assessment of the menisci in patients with contraindications to MRI or in the setting of a periarticular ferromagnetic foreign body obscuring the field of view.

Accurate Reconstruction of Multiple Basis Images Directly From Dual Energy CT Data.

We develop optimization-based algorithms to accurately reconstruct multiple ( 2) basis images directly from dual-energy (DE) data in CT. In medical and industrial CT imaging, some basis materials such as bone, metals, and contrast agents of interest are confined often spatially within regions in the image. Exploiting this observation, we develop an optimization-based algorithm to reconstruct, directly from DE data, basis-region images from which multiple ( 2) basis images and virtual monochromatic images (VMIs) can be obtained over the entire image array. We conduct experimental studies using simulated and real DE data in CT, and evaluate basis images and VMIs obtained in terms of visual inspection and quantitative metrics. The study results reveal that the algorithm developed can accurately and robustly reconstruct multiple ( 2) basis images directly from DE data. The developed algorithm can yield accurate multiple ( 2) basis images, VMIs, and physical quantities of interest from DE data in CT. The work may provide insights into the development of practical procedures for reconstructing multiple basis images, VMIs, and physical quantities from DE data in applications. The work can be extended to reconstruct multiple basis images in multi-spectral or photon-counting CT.

Low energy virtual monochromatic CT with deep learning image reconstruction to improve delineation of endoleaks

This study aimed to investigate the utility of low-energy virtual monochromatic imaging (VMI) combined with deep-learning image reconstruction (DLIR) in improving the delineation of endoleaks (ELs) after endovascular aortic repair (EVAR) in contrast-enhanced dual-energy CT (DECT). A total of 61 consecutive patients (mean age, 77 years; 46 men) after EVAR who underwent contrast-enhanced DECT were enrolled. Virtual monochromatic 40- and 70-keV images were reconstructed using DLIR (TrueFidelity-H) and conventional hybrid iterative reconstruction (IR). Contrast-to-noise ratio (CNR) of the EL on the venous-phase CT was calculated. Four different reconstructed image series (hybrid IR and DLIR at two energy levels, 40- and 70-keV) were displayed side-by-side and visually assessed for EL conspicuity on a 5-point comparative scale from 0 (best) to -4 (significantly inferior). Two experienced radiologists independently conducted a qualitative evaluation of the CT images. A total of 30 out of 61 patients presented with an EL. On both 40- and 70-keV images, the CNR of the EL was significantly higher in DLIR than in hybrid IR (40-keV, 14.5±7.3 vs 8.6±4.2, P<0.001; 70-keV, 8.7±4.5 vs 5.5±2.6, P<0.001). The comparative scale of EL conspicuity in the 40-keV DLIR images (Observer1, -0.2±0.4; Observer2, 0.0±0.0) was significantly higher than 40-keV hybrid IR (Observer1, -0.5±0.5; Observer2, -1.0±0.0; P<0.05), 70-keV DLIR (Observer1, -1.8±0.4; Observer2, -2.0±0.0; P<0.001) and 70-keV hybrid IR images (Observer1, -1.8±0.4; Observer2, -2.4±0.5; P<0.001), respectively. Using 40-keV VMI in combination with DLIR improves EL delineation after EVAR compared with the 70-keV VMI with hybrid IR or DLIR.

Comparison of dual-energy computed tomography (DECT) polychromatic and monochromatic images with and without iterative metal artifact reduction algorithm in patients with dental implants.

Dual-energy computed tomography (DECT) and iterative metal artifact reduction (iMAR) algorithms are valuable tools for reducing metal artifacts. Different parameters of these technologies and their combination can achieve different performance. This study compared various polychromatic and monochromatic images obtained via DECT with and without using iMAR algorithm to reduce artifacts in patients with dental implants. This study included 30 patients with dental implants who underwent DECT for head and neck imaging. The computed tomography (CT) image sets comprised DECT polychromatic image sets [dual-energy (DE) polychromatic] that linearly blended 100 kV and tin-filtered 140 kV images using composition ratios of -1, -0.6, -0.3, 0, and 0.6, and virtual monochromatic images (DE monochromatic) at 90, 110, 130, 150, and 170 keV. These image sets were obtained with and without using iMAR, resulting in a total of 20 image sets. For subjective analysis, metal artifacts and image quality were assessed using a 5-point Likert scale. For objective analysis, CT attenuation, standard deviation (SD), contrast-to-noise ratio (CNR) and artifact index (AI) were evaluated. In addition, subgroup analysis was performed based on implant size. In the subjective evaluation, iMAR + DE polychromatic (-0.3) images exhibited the lowest metal artifact scores [median (interquartile range): 2 (2-3)]. iMAR + DE monochromatic (110 keV) images demonstrated optimal image quality scores [median (interquartile range): 2 (2-3)]. In the objective evaluation, none of the images demonstrated a significant difference in the CNR, except polychromatic images with a composition of -1 and 0.6. iMAR + DE polychromatic (0) exhibited the lowest AI [median (interquartile range): 8.7 (5.9-14.5)]. There was no significant difference between the two groups with different implant sizes for the techniques combined with iMAR (all P>0.05). iMAR + DE polychromatic (-0.3 and 0) and iMAR + DE monochromatic (110 keV) images exhibited better image quality and substantial metal artifact reduction (MAR) compared with the other image sets. The performance of the techniques combined with iMAR was not affected by the size of the implant.

Virtual Monochromatic Image Quality from Dual-Layer Dual-Energy Computed Tomography for Detecting Brain Tumors

Virtual Monochromatic Image Quality from Dual-Layer Dual-Energy Computed Tomography for Detecting Brain Tumors