Dual-energy CT Imaging Research Articles

Efficacy and prognostic value of delta radiomics on dual-energy computed tomography for gastric cancer with neoadjuvant chemotherapy: a preliminary study.

A non-invasive tool for tumor regression grade (TRG) evaluation is urgently needed for gastric cancer (GC) treated with neoadjuvant chemotherapy (NAC). To develop and validate a radiomics signature (RS) to evaluate TRG for locally advanced GC after NAC and assess its prognostic value. A total of 103 patients with GC treated with NAC were retrospectively recruited from April 2018 to December 2019 and were randomly allocated into a training cohort (n = 69) and a validation cohort (n = 34). Delineation was performed on both mixed and iodine-uptake images based on dual-energy computed tomography (DECT). A total of 4094 radiomics features were extracted from the pre-NAC, post-NAC, and delta feature sets. Spearman correlation and the least absolute shrinkage and selection operator were used for dimensionality reduction. Multivariable logistic regression was used for TRG evaluation and generated the optimal RS. Kaplan-Meier survival analysis with the log-rank test was implemented in an independent cohort of 40 patients to validate the prognostic value of the optimal RS. Three, five, and six radiomics features were finally selected for the pre-NAC, post-NAC, and delta feature sets. The delta model demonstrated the best performance in assessing TRG in both the training and the validation cohorts (AUCs=0.91 and 0.76, respectively; P>0.1). The optimal RS from the delta model showed a significant capability to predict survival in the independent cohort (P<0.05). Delta radiomics based on DECT images serves as a potential biomarker for TRG evaluation and shows prognostic value for patients with GC treated with NAC.

ROLE OF DUAL ENERGY COMPUTED TOMOGRAPHY IMAGING IN GOUT

Context: Gout is a crystal induced inammatory arthritis triggered by crystallization of uric acid in joints. The detection of urate crystals is done using polarization light microscopy after synovial uid aspiration which is approach is invasive. Dual energy CT (DECT) is a new imaging modality that is able to identify tophi based on their chemical composition. To evaluate the rol Aims: e of Dual Energy Computed Tomography imaging in gout. A cross sectional observational study, comprising of 30 clinically suspected pati Settings And Design: ents of gout underwent DECT of all peripheral joints (including hand, wrist, elbow, ankle, foot and knee). This Methods And Material: study was conducted on 128 slice SIEMENS Somatom Denition Flash CT scan. A dual energy gout colour coded protocol assessed the chemical composition of the material and an automated volume assessment software, measured the monosodium urate crystal deposition volume. Analysis was Statistical Analysis Used: done using SPSS version 21.0. In lower limb the MTP joint (73.33%) was commonest foll Results: owed by ankle (33%) and knee (26%) with mean crystal volume of 3.51 cm3. In upper limb, MCP and PIP joints (20%) of hand are most commonly affected with mean volume of 0.24cm3. High serum uric acid (&gt;6.4mg/dl) levels showed a positive correlation with MSU crystal deposition in all peripheral joints on DECT. Conclusions: DECT is useful for the diagnosis of gout in symptomatic patients. It can detect MSU crystals volume and urate burden non-invasively.

CT-Net: Cascaded T-shape network using spectral redundancy for dual-energy CT limited-angle reconstruction

Dual-energy computed tomography (DECT) shows promising clinical significance in substance identification and quantitative analysis. Mostly dual-energy CT scanning systems use two sets of x-ray sources and detectors for full scanning to simultaneously acquire X-ray data of materials at high- and low-energy levels. The reconstructed high- and low-energy CT images have spectral redundancy in the energy domain. We propose a one-step dual-energy limited-angle reconstruction scheme exploiting the energy domain spectral redundancy. The scheme consists of a sinogram domain network(SD-Net), a reconstruction unit(RU), and an image domain network(ID-Net). After SD-Net complements the dual-energy limited-angle incomplete projection data, a RU is used to reconstruct the CT images. Finally, ID-Net processes the reconstructed CT images into high-quality CT images. We propose a Cascaded T-shape Network(CT-Net) based on spectral redundancy to improve DECT image quality. The CT-Net consists of a backbone net, a low-energy branch, and a high-energy branch. CT-Net can directly map incomplete projection data into high-quality DECT images that can be used for clinical diagnosis. Qualitative and quantitative results demonstrate the excellent performance of CT-Net in preserving edges, removing artifacts, and suppressing noise. Two common DECT applications, such as virtual non-contrast (VNC) imaging and iodine contrast agent quantification, prove the clinically promising potential of CT-Net.

Projection decomposition via univariate optimization for dual-energy CT.

Dual-energy computed tomography (DECT) acquires two x-ray projection datasets with different x-ray energy spectra, performs material-specific image reconstruction based on the energy-dependent non-linear integral model, and provides more accurate quantification of attenuation coefficients than single energy spectrum CT. In the diagnostic energy range, x-ray energy-dependent attenuation is mainly caused by photoelectric absorption and Compton scattering. Theoretically, these two physical components of the x-ray attenuation mechanism can be determined from two projection datasets with distinct energy spectra. Practically, the solution of the non-linear integral equation is complicated due to spectral uncertainty, detector sensitivity, and data noise. Conventional multivariable optimization methods are prone to local minima. In this paper, we develop a new method for DECT image reconstruction in the projection domain. This method combines an analytic solution of a polynomial equation and a univariate optimization to solve the polychromatic non-linear integral equation. The polynomial equation of an odd order has a unique real solution with sufficient accuracy for image reconstruction, and the univariate optimization can achieve the global optimal solution, allowing accurate and stable projection decomposition for DECT. Numerical and physical phantom experiments are performed to demonstrate the effectiveness of the method in comparison with the state-of-the-art projection decomposition methods. As a result, the univariate optimization method yields a quality improvement of 15% for image reconstruction and substantial reduction of the computational time, as compared to the multivariable optimization methods.

Evaluating image quality and optimal parameters for non-linear blending dual-energy computed tomography images of hepatic portal veins by blending-property-map.

Blending technology is usually used to improve quality of dual-energy computed (DECT) images. To evaluate the blended DECT image qualities by employing the Blending-Property-Map (BP-Map) and elucidating the optimal parameters with the highest signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Sixty pairs of 80 kV and 140 kV CT images are blended non-linearly by four methods. Protocol A uses the fixed values of blending width (BW) and blending center (BC); Protocol B uses the values of BW = (CThepatic portal vein - CThepatic parenchymal) / 2 and BC = (CThepatic portal vein + CThepatic parenchymal) / 2; Protocol C uses a BW ranging from 10 to 100 HU at an interval of 10 HU and BC = (CThepatic portal vein + CThepatic parenchymal) / 2; Protocol D uses the BP-Map that covers all possible values of BW and BC. When using CT value of adipose tissue as noise, the calculated SNR and CNR of optimal blending width and blending center were 123.22±41.73 and 9.00±3.52, respectively, by the BP-Map in the protocol D. By employing the CT value of back muscle as noise, the SNR and CNR of the best-blended images were 75.90±14.52 and 6.39±2.37, respectively. The subjective score of protocol D was 4.88±0.12. Compared to traditional blending methods, the BP-Map technique can determine the optimal blending parameter and provide the best-blended images with the highest SNR and CNR.

Non-Invasive characterisation of renal stones using dual energy CT: A method to differentiate calcium stones

BackgroundNon-invasive DECT based characterization of renal stones using their effective atomic number (Zeff) and the electron density (ρe) in patients. AimThis paper aims to develop a method for in-vivo characterization of renal stone. Differentiation of renal stones in-vivo especially sub types of calcium stones have very important advantage for better judgement of treatment modality. Materials and Methods50 extracted renal stones were scanned ex-vivo using dual energy CT scanner. A method was developed to characterize these renal stones using effective atomic number and electron density obtained from dual energy CT data. The method and formulation developed in ex-vivo experiments was applied in in-vivo study of 50 randomly selected patients of renal stones who underwent dual energy CT scan. ResultsThe developed method was able to characterize Calcium Oxalate Monohydrate (COM) and the combination of COM and Calcium Oxalate Dihydrate (COD) stones non-invasively in patients with a sensitivity of 81% and 83%respectively. The method was also capable of differentiating Uric, Cystine and mixed stones with the sensitivity of 100, 100 and 85.71% respectively. ConclusionThe developed dual energy CT based method was capable of differentiating sub types of calcium stones which is not differentiable on single energy or dual energy CT images.

Effects of different virtual monoenergetic CT image data on chest wall post-processing "unfolded ribs" and proposal of an algorithm improvement.

To find out if the use of different virtual monoenergetic data sets enabled by DECT technology might have a negative impact on post-processing applications, specifically in case of the "unfolded ribs" algorithm. Metal or beam hardening artifacts are suspected to generate image artifacts and thus reduce diagnostic accuracy. This paper tries to find out how the generation of "unfolded rib" CT image reformates is influenced by different virtual monoenergetic CT images and looks for possible improvement of the post-processing tool. Between March 2021 and April 2021, thin-slice dual-energy CT image data of the chest were used creating "unfolded rib" reformates. The same data sets were analyzed in three steps: first the gold standard with the original algorithm on mixed image data sets followed by the original algorithm on different keV levels (40-120keV) and finally using a modified algorithm which in the first step used segmentation based on mixed image data sets, followed by segmentation based on different keV levels. Image quality (presence of artifacts), lesion and fracture detectability were assessed for all series. Both, the original and the modified algorithm resulted in more artifact-free image data sets compared to the gold standard. The modified algorithm resulted in significantly more artifact-free image data sets at the keV-edges (40-120keV) compared the original algorithm. Especially "black artifacts" and pseudo-lesions, potentially inducing false positive findings, could be reduced in all keV level with the modified algorithm. Detection of focal sclerotic, lytic or mixed (k = 0.990-1.000) lesions was very good for all keV levels. The Fleiss-kappa test for detection of fresh and old rib fractures was ≥ 0.997. The use of different virtual monoenergetic keVs for the "unfolded rib" algorithm is generating different artifacts. Segmentation-based artifacts could be eliminated by the proposed new algorithm, showing the best results at 70-80keV.

Improved GAN: Using a transformer module generator approach for material decomposition

Dual-energy computed tomography (CT) can be used for material decomposition, allowing for the precise quantitative mapping of body substances; this has a wide range of clinical applications, including disease diagnosis, treatment response evaluation and prognosis prediction. However, dual-energy CT has not yet become the mainstream technique in most clinical settings due to its limited accessibility. To fully take advantage of material quantification, researchers have attempted to use deep learning to generate material decomposition maps from conventional single-energy CT images, mainly by synthesizing another single-energy CT image from a conventional single-energy CT image to form a dual-energy CT image first and then generate material decomposition maps. This is not a straightforward process, and it potentially introduces many inaccuracies after multiple steps. In this work, we proposed a generative adversarial network (GAN) framework as the base and improved its generator; this approach combines convolutional neural networks (CNNs) and a transformer module to directly generate material decomposition maps from conventional single-energy CT images. Our model pays attention to both local and global information. Then, we compared our method with 6 competitive deep learning methods on water (calcium) and calcium (water) substrate density image datasets. The average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the water (calcium) substrate density images were 32.7207, 0.9685, 0.0323, and 0.0555, respectively. Furthermore, the average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the calcium (water) substrate density images were 30.2823, 0.9449, 0.0652, and 0.0715, respectively. Our model achieved better performance and stronger stability than competing approaches.

Metal artifact reduction in cervix brachytherapy with titanium applicators using dual‐energy CT through virtual monoenergetic images and an iterative algorithm: A phantom study

To evaluate an iterative metal-artifact reduction (iMAR) algorithm, dual-energy CT (DECT) through virtual monoenergetic images (VMI), and a combination of iMAR and DECT for reducing metal artifact severity (AS) induced by Fletcher titanium applicators used in cervix brachytherapy, the efficacy of which are hitherto unreported. 120 kVp single-energy CT (SECT) (Siemens) of BEBIG tandem applicators, varying in shape (straight or curved) and diameter (3.5 mm or 5 mm) in a custom-made water-filled phantom, and their DECT images obtained from extrapolation of 80 kVp and 140 kVp, were reconstructed using four methods: DECT through VMI±iMAR, and SECT±iMAR. The DECT images were reconstructed monoenergetically at 70, 150, and 190 keV. AS was evaluated using measured values and statistical analysis. iMAR, DECT, and combined DECT and iMAR reduced AS (p < 0.05). DECT had a lower AS than SECT, even without iMAR (p < 0.025). SECT+iMAR was more effective than DECT-iMAR with VMI at 70 and 190 keV (p < 0.05), whereas showing no statistically significant difference at 150 keV. With DECT and iMAR combined, AS was reduced more effectively compared to the SECT+iMAR or DECT alone. It also reduced the mean interobserver uncertainty by 0.2 mm. These findings indicate that iMAR reduces the AS caused by Fletcher titanium applicators for both SECT and DECT, a combination of iMAR and DECT is superior to either strategy alone, and at low energies, DECT+iMAR also produces similar artifact reduction. These practical strategies promise more accurate source-position and structure definitions in CT-based gynecological brachytherapy treatment planning.

Pulmonary Blood Volume Among Older Adults in the Community: The MESA Lung Study.

The pulmonary vasculature is essential for gas exchange and impacts both pulmonary and cardiac function. However, it is difficult to assess and its characteristics in the general population are unknown. We measured pulmonary blood volume (PBV) noninvasively using contrast enhanced, dual-energy computed tomography to evaluate its relationship to age and symptoms among older adults in the community. The MESA (Multi-Ethnic Study of Atherosclerosis) is an ongoing community-based, multicenter cohort. All participants attending the most recent MESA exam were selected for contrast enhanced dual-energy computed tomography except those with estimated glomerular filtration rate <60 mL/min per 1.73 m2. PBV was calculated by material decomposition of dual-energy computed tomography images. Multivariable models included age, sex, race/ethnicity, education, height, weight, smoking status, pack-years, and scanner model. The mean age of the 727 participants was 71 (range 59-94) years, and 55% were male. The race/ethnicity distribution was 41% White, 29% Black, 17% Hispanic, and 13% Asian. The mean±SD PBV in the youngest age quintile was 547±180 versus 433±194 mL in the oldest quintile (P<0.001), with an approximately linear decrement of 50 mL per 10 years of age ([95% CI, 32-67]; P<0.001). Findings were similar with multivariable adjustment. Lower PBV was associated independently with a greater dyspnea after a 6-minute walk (P=0.04) and greater composite dyspnea symptom scores (P=0.02). Greater PBV was also associated with greater height, weight, lung volume, Hispanic race/ethnicity, and nonsmoking history. Pulmonary blood volume was substantially lower with advanced age and was associated independently with greater symptoms scores in the elderly.

Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT.

To evaluate a novel deep learning image reconstruction (DLIR) technique for dual-energy CT (DECT) derived virtual monoenergetic (VM) images compared to adaptive statistical iterative reconstruction (ASIR-V) in low kiloelectron volt (keV) images. We analyzed 30 venous phase acute abdominal DECT (80/140 kVp) scans. Data were reconstructed to ASIR-V and DLIR-High at four different keV levels (40, 50, 74, and 100) with 1- and 3-mm slice thickness. Quantitative Hounsfield unit (HU) and noise assessment were measured within the liver, aorta, fat, and muscle. Subjective assessment of image noise, sharpness, texture, and overall quality was performed by two board-certified radiologists. DLIR reduced image noise by 19.9-35.5% (p < 0.001) compared to ASIR-V in all reconstructions at identical keV levels. Contrast-to-noise ratio (CNR) increased by 49.2-53.2% (p < 0.001) in DLIR 40-keV images compared to ASIR-V 50 keV, while no significant difference in noise was identified except for 1 and 3 mm in aorta and for 1-mm liver measurements, where ASIR-V 50 keV showed 5.5-6.8% (p < 0.002) lower noise levels. Qualitative assessment demonstrated significant improvement particularly in 1-mm reconstructions (p < 0.001). Lastly, DLIR 40 keV demonstrated comparable or improved image quality ratings when compared to ASIR-V 50 keV (p < 0.001 to 0.22). DLIR significantly reduced image noise compared to ASIR-V. Qualitative assessment showed that DLIR significantly improved image quality particularly in thin sliced images. DLIR may facilitate 40 keV as a new standard for routine low-keV VM reconstruction in contrast-enhanced abdominal DECT. • DLIR enables 40 keV as the routine low-keV VM reconstruction. • DLIR significantly reduced image noise compared to ASIR-V, across a wide range of keV levels in VM DECT images. • In low-keV VM reconstructions, improvements in image quality using DLIR were most evident and consistent in 1-mm sliced images.

Extreme gradient boosting model to assess risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual prediction using SHapley Additive exPlanations

Background and ObjectivesCentral cervical lymph node metastasis (CLNM) is considered a risk factor for recurrence in patients with papillary thyroid carcinoma (PTC). Traditional machine learning models suffered from “black-box” problems, which could not exactly explain the interactive effects of the risk factors. We aimed to develop an eXtreme Gradient Boosting (XGBoost) model to assess CLNM, including positive and negative effects. Methods1,122 patients with PTC admitted at Tianjin First Central Hospital from 2016 to 2020 were retrospectively selected. They were randomly divided into the training and test datasets with an 8:2 ratio. 108 patients with PTC admitted at Binzhou Medical University Hospital in 2020 served as the validation dataset. The XGBoost model was used to assess CLNM. The 10-fold cross-validation was utilized for model selection, and the metric used to evaluate classification performance was the average area under the curve (AUC) of 10-fold cross-validation. Interpretation and transparency of the “black-box” problem were performed. SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) were used to ensure the stability and reliability of the model. ResultsThe XGBoost model based on ultrasound and dual-energy computed tomography images of the solitary primary lesion had an excellent performance for assessing CLNM, with average AUCs of 0.918, 0.903, and 0.881 in the training, test, and validation datasets, respectively. SHAP plots showed the influence of each parameter on the XGBoost model, including positive (i.e., capsular invasion, diameter, iodine concentration in the venous phase, and calcification) and negative (i.e., sex and age) impacts. For all cases, the capsular invasion prediction weight was the highest; for individual cases, different predictors were assigned different weights. Moreover, the performance of the XGBoost model was better than classical machine-learning models. ConclusionsThis study developed and validated an XGBoost model for assessing CLNM in patients with PTC. The ability to visually interpret the positive and negative effects made the XGBoost model an effective tool for guiding clinical treatment.

Iodine images in dual energy CT: A monocentric study benchmarking quantitative iodine concentration values of the healthy liver.

Dual energy computed tomography (DECT) allows the quantification of specific materials such as iodine contrast agent in human body tissue, potentially providing additional diagnostic data. Yet full diagnostic value can only be achieved if physiological normal values for iodine concentrations are known. We retrospectively evaluated abdominal DECT scans of 105 patients with healthy liver between March and August 2018 (age 17 to 86 years, 43 female and 62 male). The iodine concentrations within ROIs of the liver parenchyma as well as of the abdominal aorta and main portal vein were obtained. We evaluated the absolute iodine concentration and blood-normalized iodine concentrations relating the measured iodine concentration of the liver parenchyma to those of the supplying vessels. The influence of age and gender on the iodine uptake was assessed. The absolute iodine concentration was significantly different for the male and female cohort, but the difference was eliminated by the blood-normalized values. The average blood-normalized iodine concentrations were 2.107 mg/ml (+/- 0.322 mg/ml), 2.125 mg/ml (+/- 0.426 mg/ml) and 2.103 mg/ml (+/- 0.317 mg/ml) for the portal vein normalized, aorta normalized and mixed blood normalized iodine concentrations, respectively. A significant negative correlation between the patients’ age and the iodine concentration was detected only for the blood-normalized values. A physiological range for iodine concentration in portal venous phase contrast enhanced DECT images can be defined for absolute and blood-normalized values. Deviations of blood-normalized iodine concentration values might be a robust biomarker for diagnostic evaluation. Patient age but not the gender influences the blood-normalized iodine concentrations in healthy liver parenchyma.

Feasibility study of three-material decomposition in dual-energy cone-beam CT imaging with deep learning

Objective. In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstructing three different material distribution volumes from the dual-energy CBCT projection data. Approach. In Triple-CBCT, the features of the sinogram and the CT image are independently extracted and cascaded via a customized domain transform network module. This Triple-CBCT network was trained by numerically synthesized dual-energy CBCT data, and was tested with experimental dual-energy CBCT data of the Iodine-CaCl2 solution and pig leg specimen scanned on an in-house benchtop system. Main results. Results show that the information stored in both the sinogram and CT image domains can be used together to improve the decomposition quality of multiple materials (water, iodine, CaCl2 or bone) from the dual-energy projections. In addition, both the numerical and experimental results demonstrate that the Triple-CBCT is able to generate high-fidelity dual-energy CBCT basis images. Significance. An innovative end-to-end network that joints the sinogram and CT image domain information is developed to facilitate high quality automatic decomposition from the dual-energy CBCT scans.

Assessment of Correlation between Dual-Energy Ct (De-Ct)-Derived Iodine Concentration and Local Flourodeoxyglucose (Fdg) Uptake in Patients with Primary Non-Small-Cell Lung Cancer.

(1) The current literature contains several studies investigating the correlation between dual-energy-derived iodine concentration (IC) and positron emission tomography (PET)-derived Flourodeoxyglucose (18F-FDG) uptake in patients with non-small-cell lung cancer (NSCLC). In previously published studies, either the entire tumor volume or a region of interest containing the maximum IC or 18F-FDG was assessed. However, the results have been inconsistent. The objective of this study was to correlate IC with FDG both within the entire volume and regional sub-volumes of primary tumors in patients with NSCLC. (2) In this retrospective study, a total of 22 patients with NSCLC who underwent both dual-energy CT (DE-CT) and 18F-FDG PET/CT were included. A region of interest (ROI) encircling the entire primary tumor was delineated, and a rigid registration of the DE-CT, iodine maps and FDG images was performed for the ROI. The correlation between tumor measurements and area-specific measurements of ICpeak and the peak standardized uptake value (SUVpeak) was found. Finally, a correlation between tumor volume and the distance between SUVpeak and ICpeak centroids was found. (3) For the entire tumor, moderate-to-strong correlations were found between SUVmax and ICmax (R = 0.62, p = 0.002), and metabolic tumor volume vs. total iodine content (R = 0.91, p < 0.001), respectively. For local tumor sub-volumes, a negative correlation was found between ICpeak and SUVpeak (R = −0.58, p = 0.0046). Furthermore, a strong correlation was found between the tumor volume and the distance in millimeters between SUVpeak and ICpeak centroids (R = 0.81, p < 0.0001). (4) In patients with NSCLC, high FDG uptakes and high DE-CT-derived iodine concentrations correlated on a whole-tumor level, but the peak areas were positioned at different locations within the tumor. 18F-FDG PET/CT and DE-CT provide complementary information and might represent different underlying patho-physiologies.

Preoperative Prediction of the Aggressiveness of Oral Tongue Squamous Cell Carcinoma with Quantitative Parameters from Dual-Energy Computed Tomography

ObjectivesTo determine whether quantitative parameters derived from dual-energy computed tomography (DECT) were predictive of the aggressiveness of oral tongue squamous cell carcinoma (OTSCC) including the pathologic stages, histologic differentiation, lymph node status, and perineural invasion (PNI).MethodsBetween August 2019 and March 2021, 93 patients (mean age, 54.6 ± 13.8 years; 66 men) with pathologically diagnosed OTSCC were enrolled in this prospective study. Preoperative DECT was performed and quantitative parameters (e.g., slope of the spectral Hounsfield unit curve [λHu], normalized iodine concentration [nIC], normalized effective atomic number [nZeff], and normalized electron density [nRho]) were measured on arterial phase (AP) and venous phase (VP) DECT imaging. Quantitative parameters from DECT were compared between patients with different pathologic stages, histologic differentiation, lymph node statuses, and perineural invasion statuses. Logistic regression analysis was utilized to assess independent parameters and the diagnostic performance was analyzed by the receiver operating characteristic curves (ROC).ResultsλHu and nIC in AP and λHu, nZeff, and nIC in VP were significantly lower in stage III–IV lesions than in stage I–II lesions (p < 0.001 to 0.024). λHu in VP was an independent predictor of tumor stage with an odds ratio (OR) of 0.29, and area under the curve (AUC) of 0.80. λHu and nIC were higher in well-differentiated lesions than in poorly differentiated lesions (p < 0.001 to 0.021). The nIC in VP was an independent predictor of histologic differentiation with OR of 0.31, and AUC of 0.78. λHu and nIC in VP were lower in OTSCCs with lymph node metastasis than those without metastasis (p < 0.001 to 0.005). λHu in VP was the independent predictor of lymph node status with OR of 0.42, and AUC of 0.74. No significant difference was found between OTSCCs without PNI and those with PNI in terms of the quantitative DECT parameters.ConclusionDECT can be a complementary means for the preoperative prediction of the aggressiveness of OTSCC.

Differentiation of adrenal adenomas from adrenal metastases in single-phased staging dual-energy CT and radiomics.

PURPOSE Differentiation of incidental adrenal lesions remains a challenge in diagnostic imaging, especially on single-phase portal venous computed tomography (CT) in the oncological setting. The aim of the study was to explore the ability of dual-energy CT (DECT)-based iodine quantification and virtual non-contrast (VNC) imaging and advanced radiomic analysis of DECT for differentiation of adrenal adenomas from metastases. METHODS A total of 46 patients with 49 adrenal lesions underwent clinically indicated staging DECT and magnetic resonance imaging. Median values of quantitative parameters such as VNC, fat frac- tion, and iodine density in DECT images were collected and compared between adenomas and metastases using non-parametric tests. Magnetic resonance imaging, washout CT, and clinical follow-up were used as a reference standard. Diagnostic accuracy was assessed by calculat- ing receiver operating characteristics. A DECT tumor analysis prototype software was used for semiautomatic segmentation of adrenal lesions and extraction of radiomic features. A radiomics prototype was used to analyze the data with multiple logistic regression and random forest clas- sification to determine the area under the curve (AUC). RESULTS The study cohort (60.87% women; mean age: 66.91 ± 12.93 years) consisted of 32 adenomas and 17 metastases. DECT-based VNC imaging (AUC=0.89) and fat quantification (AUC=0.86) differentiate between adrenal adenomas and metastases with high diagnostic accuracy (P < .001). Analysis of radiomic features revealed that DECT features such as VNC imaging and fat fraction (AUC = 0.87-0.89; < .001) and radiomic features such as 90th percentile and total energy (AUC = 0.88-0.93; P < .001) differentiate with high diagnostic accuracy between adrenal adeno- mas and metastases. Random forest classification revealed an AUC of 0.83 for separating adrenal adenomas from metastases. CONCLUSION Virtual non-contrast imaging and fat quantification as well as extraction of radiomic features accurately differentiate between adrenal adenomas and metastases on single-phase oncologic staging DECT.

Quantitative dual-energy computed tomography texture analysis predicts the response of primary small hepatocellular carcinoma to radiofrequency ablation

BackgroundRadiofrequency ablation (RFA) is one of the effective therapeutic modalities in patients with hepatocellular carcinoma (HCC). However, there is no proper method to evaluate the HCC response to RFA. This study aimed to establish and validate a clinical prediction model based on dual-energy computed tomography (DECT) quantitative-imaging parameters, clinical variables, and CT texture parameters. MethodsWe enrolled 63 patients with small HCC. Two to four weeks after RFA, we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients’ clinical baseline variables. DECT images were manually segmented, and 56 CT texture features were extracted. We used LASSO algorithm for feature selection and data dimensionality reduction; logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters; we then added texture features to build a clinical-texture model based on clinical model. ResultsA total of six optimal CT texture analysis (CTTA) features were selected, which were statistically different between patients with or without tumor progression (P < 0.05). When clinical variables and DECT-quantitative parameters were included, the clinical models showed that albumin-bilirubin grade (ALBI) [odds ratio (OR) = 2.77, 95% confidence interval (CI): 1.35-6.65, P = 0.010], λAP (40-100 keV) (OR = 3.21, 95% CI: 3.16-5.65, P = 0.045) and ICAP (OR = 1.25, 95% CI: 1.01-1.62, P = 0.028) were associated with tumor progression, while the clinical-texture models showed that ALBI (OR = 2.40, 95% CI: 1.19-5.68, P = 0.024), λAP (40-100 keV) (OR = 1.43, 95% CI: 1.10-2.07, P = 0.019), and CTTA-score (OR = 2.98, 95% CI: 1.68-6.66, P = 0.001) were independent risk factors for tumor progression. The clinical model, clinical-texture model, and CTTA-score all performed well in predicting tumor progression within 12 months after RFA (AUC = 0.917, 0.962, and 0.906, respectively), and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957, respectively. ConclusionsDECT-quantitative parameters, CTTA, and clinical variables were helpful in predicting HCC progression after RFA. The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.

The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

ObjectiveTo investigate the value of monochromatic dual-energy CT (DECT) images based on radiomics in differentiating benign from malignant solitary pulmonary nodules.Materials and methodsThis retrospective study was approved by the institutional review board, and informed consent was waived. Pathologically confirmed lung nodules smaller than 3 cm with integrated arterial phase and venous phase (AP and VP) gemstone spectral imaging were retrospectively identified. After extracting the radiomic features of each case, principal component analysis (PCA) was used for feature selection, and after training with the logistic regression method, three classification models (ModelAP, ModelVP and ModelCombination) were constructed. The performance was assessed by the area under the receiver operating curve (AUC), and the efficacy of the models was validated using an independent cohort.ResultsA total of 153 patients were included and divided into a training cohort (n = 107) and a validation cohort (n = 46). A total of 1130 radiomic features were extracted from each case. The PCA method selected 22, 25 and 35 principal components to construct the three models. The diagnostic accuracy of ModelAP, ModelVP and ModelCombination was 0.8043, 0.6739, and 0.7826 in the validation set, with AUCs of 0.8148 (95% CI 0.682–0.948), 0.7485 (95% CI 0.602–0.895), and 0.8772 (95% CI 0.780–0.974), respectively. The DeLong test showed that there were significant differences in the AUCs between ModelAP and ModelCombination (P = 0.0396) and between ModelVP and ModelCombination (P = 0.0465). However, the difference in AUCs between ModelAP and ModelVP was not significant (P = 0.5061). These results demonstrate that ModelCombination shows a better performance than the other models. Decision curve analysis proved the clinical utility of this model.ConclusionsWe developed a radiomics model based on monochromatic DECT images to identify solitary pulmonary nodules. This model could serve as an effective tool for discriminating benign from malignant pulmonary nodules in patients. The combination of arterial phase and venous phase imaging could significantly improve the model performance.

A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images

To propose a nonlocal spectral similarity-induced material decomposition network (NSSD-Net) to reduce the correlation noise in the low-dose spectral CT decomposed images. We first built a model-driven iterative decomposition model for dual-energy CT, optimized the objective function solving process using the iterative shrinking threshold algorithm (ISTA), and cast the ISTA decomposition model into the deep learning network. We then developed a novel cost function based on the nonlocal spectral similarity to constrain the training process. To validate the decomposition performance, we established a material decomposition dataset by real patient dual-energy CT data. The NSSD-Net was compared with two traditional model-driven material decomposition methods, one data-based material decomposition method and one data-model coupling-driven material decomposition supervised learning method. The quantitative results showed that compared with the two traditional methods, the NSSD-Net method obtained the highest PNSR values (31.383 and 31.444) and SSIM values (0.970 and 0.963) and the lowest RMSE values (2.901 and 1.633). Compared with the datamodel coupling-driven supervised decomposition method, the NSSD-Net method obtained the highest SSIM values on water and bone decomposed results. The results of subjective image quality assessment by clinical experts showed that the NSSD-Net achieved the highest image quality assessment scores on water and bone basis material (8.625 and 8.250), showing significant differences from the other 4 decomposition methods (P < 0.001). The proposed method can achieve high-precision material decomposition and avoid training data quality issues and model unexplainable issues.