Radiomics analysis of pancreas based on dual-energy computed tomography for the detection of type 2 diabetes mellitus

Objective

To utilize radiomics analysis on dual-energy CT images of the pancreas to establish a quantitative imaging biomarker for type 2 diabetes mellitus.

Materials and methods

In this retrospective study, 78 participants (45 with type 2 diabetes mellitus, 33 without) underwent a dual energy CT exam. Pancreas regions were segmented automatically using a deep learning algorithm. From these regions, radiomics features were extracted. Additionally, 24 clinical features were collected for each patient. Both radiomics and clinical features were then selected using the least absolute shrinkage and selection operator (LASSO) technique and then build classifies with random forest (RF), support vector machines (SVM) and Logistic. Three models were built: one using radiomics features, one using clinical features, and a combined model.

Results

Seven radiomic features were selected from the segmented pancreas regions, while eight clinical features were chosen from a pool of 24 using the LASSO method. These features were used to build a combined model, and its performance was evaluated using five-fold cross-validation. The best classifier type is Logistic and the reported area under the curve (AUC) values on the test dataset were 0.887 (0.73-1), 0.881 (0.715-1), and 0.922 (0.804-1) for the respective models.

Conclusion

Radiomics analysis of the pancreas on dual-energy CT images offers potential as a quantitative imaging biomarker in the detection of type 2 diabetes mellitus.

Advancing Differentiation of Hepatic Metastases in Malignant Melanoma through Dual-Energy Computed Tomography Rho/Z Maps

OBJECTIVES

The aim of this study is to evaluate the diagnostic accuracy of dual-energy computed tomography (DECT)-based Rho/Z maps in differentiating between metastases and benign liver lesions in patients diagnosed with malignant melanoma compared to conventional CT value measurements.

METHODS

This retrospective study included 73 patients (mean age, 70 ± 13 years; 43 m/30 w) suffering from malignant melanoma who had undergone third-generation DECT as part of tumor staging between December 2017 and December 2021. For this study, we measured Rho (electron density) and Z (effective atomic number) values as well as Hounsfield units (HUs) in hypodense liver lesions. Values were compared, and diagnostic accuracy for differentiation was computed using receiver operating characteristic (ROC) curve analyses. Additional performed MRI or biopsies served as a standard of reference.

RESULTS

A total of 136 lesions (51 metastases, 71 cysts, and 14 hemangiomas) in contrast-enhanced DECT images were evaluated. The most notable discrepancy (p < 0.001) between measured values and the highest diagnostic accuracy for distinguishing melanoma metastases from benign cysts was observed for the Z (0.992; 95% CI, 0.956-1) parameters, followed by Rho (0.908; 95% CI, 0.842-0.953) and finally HU120kV (0.829; 95% CI, 0.751-0.891). Conversely, when discriminating between liver metastases and hemangiomas, the HU120kV parameters showed the most significant difference (p < 0.001) and yielded the highest values for diagnostic accuracy (0.859; 95% CI, 0.740-0.937), followed by the Z parameters (0.790; 95% CI, 0.681-0.876) and finally the Rho values (0.621; 95% CI, 0.501-0.730).

CONCLUSIONS

Rho and Z measurements derived from DECT allow for improved differentiation of liver metastases and benign liver cysts in patients with malignant melanoma compared to conventional CT value measurements. In contrast, in differentiation between liver hemangiomas and metastases, Rho/Z maps show inferior diagnostic accuracy. Therefore, differentiation between these two lesions remains a challenge for CT imaging.

Feasibility of bone marrow edema detection using dual-energy cone-beam computed tomography

BACKGROUND

Dual-energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone-beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition approaches.

PURPOSE

We investigate the feasibility of BME assessment using kV-switching DE CBCT with a comprehensive CBCT artifact correction framework and a two-stage projection- and image-domain three-material decomposition algorithm.

METHODS

DE CBCT projections of quantitative BME phantoms (water containers 100-165 mm in size with inserts presenting various degrees of edema) and an animal cadaver model of BME were acquired on a CBCT test bench emulating the standard wrist imaging configuration of a Multitom Rax twin robotic x-ray system. The slow kV-switching scan protocol involved a 60 kV low energy (LE) beam and a 120 kV high energy (HE) beam switched every 0.5° over a 200° angular span. The DE CBCT data preprocessing and artifact correction framework consisted of (i) projection interpolation onto matched LE and HE projections views, (ii) lag and glare deconvolutions, and (iii) efficient Monte Carlo (MC)-based scatter correction. Virtual non-calcium (VNCa) images for BME detection were then generated by projection-domain decomposition into an Aluminium (Al) and polyethylene basis set (to remove beam hardening) followed by three-material image-domain decomposition into water, Ca, and fat. Feasibility of BME detection was quantified in terms of VNCa image contrast and receiver operating characteristic (ROC) curves. Robustness to object size, position in the field of view (FOV) and beam collimation (varied 20-160 mm) was investigated.

RESULTS

The MC-based scatter correction delivered > 69% reduction of cupping artifacts for moderate to wide collimations (> 80 mm beam width), which was essential to achieve accurate DE material decomposition. In a forearm-sized object, a 20% increase in water concentration (edema) of a trabecular bone-mimicking mixture presented as ∼15 HU VNCa contrast using 80-160 mm beam collimations. The variability with respect to object position in the FOV was modest (< 15% coefficient of variation). The areas under the ROC curve were > 0.9. A femur-sized object presented a somewhat more challenging task, resulting in increased sensitivity to object positioning at 160 mm collimation. In animal cadaver specimens, areas of VNCa enhancement consistent with BME were observed in DE CBCT images in regions of MRI-confirmed edema.

CONCLUSION

Our results indicate that the proposed artifact correction and material decomposition pipeline can overcome the challenges of scatter and limited spectral separation to achieve relatively accurate and sensitive BME detection in DE CBCT. This study provides an important baseline for clinical translation of musculoskeletal DE CBCT to quantitative, point-of-care bone health assessment.

Material decomposition with a prototype photon-counting detector CT system: expanding a stoichiometric dual-energy CT method via energy bin optimization and K-edge imaging

Objective.Computed tomography (CT) has advanced since its inception, with breakthroughs such as dual-energy CT (DECT), which extracts additional information by acquiring two sets of data at different energies. As high-flux photon-counting detectors (PCDs) become available, PCD-CT is also becoming a reality. PCD-CT can acquire multi-energy data sets in a single scan by spectrally binning the incident x-ray beam. With this, K-edge imaging becomes possible, allowing high atomic number (high-Z) contrast materials to be distinguished and quantified. In this study, we demonstrated that DECT methods can be converted to PCD-CT systems by extending the method of Bourqueet al(2014). We optimized the energy bins of the PCD for this purpose and expanded the capabilities by employing K-edge subtraction imaging to separate a high-atomic number contrast material.Approach.The method decomposes materials into their effective atomic number (Zeff) and electron density relative to water (ρe). The model was calibrated and evaluated using tissue-equivalent materials from the RMI Gammex electron density phantom with knownρevalues and elemental compositions. TheoreticalZeffvalues were found for the appropriate energy ranges using the elemental composition of the materials.Zeffvaried slightly with energy but was considered a systematic error. Anex vivobovine tissue sample was decomposed to evaluate the model further and was injected with gold chloride to demonstrate the separation of a K-edge contrast agent.Main results.The mean root mean squared percent errors on the extractedZeffandρefor PCD-CT were 0.76% and 0.72%, respectively and 1.77% and 1.98% for DECT. The tissue types in theex vivobovine tissue sample were also correctly identified after decomposition. Additionally, gold chloride was separated from theex vivotissue sample with K-edge imaging.Significance.PCD-CT offers the ability to employ DECT material decomposition methods, along with providing additional capabilities such as K-edge imaging.

Deep-learning for automated detection of MSU deposits on DECT: evaluating impact on efficiency and reader confidence

Introduction

Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Manually identifying these foci (most commonly labeled green) is tedious, and an automated detection system could streamline the process. This study aims to evaluate the impact of a deep-learning (DL) algorithm developed for detecting green pixelations on DECT on reader time, accuracy, and confidence.

Methods

We collected a sample of positive and negative DECTs, reviewed twice-once with and once without the DL tool-with a 2-week washout period. An attending musculoskeletal radiologist and a fellow separately reviewed the cases, simulating clinical workflow. Metrics such as time taken, confidence in diagnosis, and the tool's helpfulness were recorded and statistically analyzed.

Results

We included thirty DECTs from different patients. The DL tool significantly reduced the reading time for the trainee radiologist (p = 0.02), but not for the attending radiologist (p = 0.15). Diagnostic confidence remained unchanged for both (p = 0.45). However, the DL model identified tiny MSU deposits that led to a change in diagnosis in two cases for the in-training radiologist and one case for the attending radiologist. In 3/3 of these cases, the diagnosis was correct when using DL.

Conclusions

The implementation of the developed DL model slightly reduced reading time for our less experienced reader and led to improved diagnostic accuracy. There was no statistically significant difference in diagnostic confidence when studies were interpreted without and with the DL model.

Differentiation of Hamartomas and Malignant Lung Tumors in Single-Phased Dual-Energy Computed Tomography

This study investigated the efficacy of single-phase dual-energy CT (DECT) in differentiating pulmonary hamartomas from malignant lung lesions using virtual non-contrast (VNC), iodine, and fat quantification. Forty-six patients with 47 pulmonary lesions (mean age: 65.2 ± 12.1 years; hamartomas-to-malignant lesions = 22:25; male: 67%) underwent portal venous DECT using histology, PET-CT and follow-up CTs as a reference. Quantitative parameters such as VNC, fat fraction, iodine density and CT mixed values were statistically analyzed. Significant differences were found in fat fractions (hamartomas: 48.9%; malignancies: 22.9%; p ≤ 0.0001) and VNC HU values (hamartomas: -20.5 HU; malignancies: 17.8 HU; p ≤ 0.0001), with hamartomas having higher fat content and lower VNC HU values than malignancies. CT mixed values also differed significantly (p ≤ 0.0001), but iodine density showed no significant differences. ROC analysis favored the fat fraction (AUC = 96.4%; sensitivity: 100%) over the VNC, CT mixed value and iodine density for differentiation. The study concludes that the DECT-based fat fraction is superior to the single-energy CT in differentiating between incidental pulmonary hamartomas and malignant lesions, while post-contrast iodine density is ineffective for differentiation.

Prediction of proton stopping power ratios using dual-energy CT basis material decomposition

BACKGROUND

Proton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single-energy computed tomography (SECT). Various studies have shown that multi-energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age-related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection-based MECT systems for SPR prediction.

PURPOSE

To present and evaluate a novel proton SPR prediction method based on MD images from dual-energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy.

METHODS

A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non-tissue materials, by minimizing the root-mean-square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD-SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data-fitting. The goodness of fit of the MD-SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head- and an abdomen-sized phantom setup, were acquired with a clinical projection-based fast-kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non-tissue materials in the phantoms, as well as used for input to the MD-SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image-based consecutive scan DECT for the same phantom setups.

RESULTS

The predicted SPRs of the tissue surrogates were similar for MD-SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non-adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between -0.7% and -1.8%. The SPRs of the non-tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD-SPR images, compared to 6.8% for DirectSPR.

CONCLUSIONS

The MD-SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non-tissue materials.

Thoracic Applications of Spectral CT Scan

TOPIC IMPORTANCE

Thoracic imaging with CT scan has become an essential component in the evaluation of respiratory and thoracic diseases. Providers have historically used conventional single-energy CT; however, prevalence of dual-energy CT (DECT) is increasing, and as such, it is important for thoracic physicians to recognize the utility and limitations of this technology.

REVIEW FINDINGS

The technical aspects of DECT are presented, and practical approaches to using DECT are provided. Imaging at multiple energy spectra allows for postprocessing of the data and the possibility of creating multiple distinct image reconstructions based on the clinical question being asked. The data regarding utility of DECT in pulmonary vascular disorders, ventilatory defects, and thoracic oncology are presented. A pictorial essay is provided to give examples of the strengths associated with DECT.

SUMMARY

DECT has been most heavily studied in chronic thromboembolic pulmonary hypertension; however, it is increasingly being used across a wide spectrum of thoracic diseases. DECT combines morphologic and functional assessments in a single imaging acquisition, providing clinicians with a powerful diagnostic tool. Its role in the evaluation and treatment of thoracic diseases will likely continue to expand in the coming years as clinicians become more experienced with the technology.

Enhancement of Stopping Power Ratio (SPR) Estimation Accuracy through Image-Domain Dual-Energy Computer Tomography for Pencil Beam Scanning System: A Simulation Study

Our study aims to quantify the impact of spectral separation on achieved theoretical prediction accuracy of proton-stopping power when the volume discrepancy between calibration phantom and scanned object is observed. Such discrepancy can be commonly seen in our CSI pediatric patients. One of the representative image-domain DECT models is employed on a virtual phantom to derive electron density and effective atomic number for a total of 34 ICRU standard human tissues. The spectral pairs used in this study are 90 kVp/140 kVp, without and with 0.1 mm to 0.5 mm additional tin filter. The two DECT images are reconstructed via a conventional filtered back projection algorithm (FBP) on simulated noiseless projection data. The best-predicted accuracy occurs at a spectral pair of 90 kVp/140 kVp with a 0.3 mm tin filter, and the root-mean-squared average error is 0.12% for tissue substitutes. The results reveal that the selected image-domain model is sensitive to spectral pair deviation when there is a discrepancy between calibration and scanning conditions. This study suggests that an optimization process may be needed for clinically available DECT scanners to yield the best proton-stopping power estimation.

Clinical Implementation of Dual-Energy CT Technical for Hepatobiliary Imaging

Dual-energy computed tomography (DECT) applies two energy spectra distributions to collect raw data based on traditional CT imaging. The application of hepatobiliary imaging, has the advantages of optimizing the scanning scheme, improving the imaging quality, highlighting the disease characterization, and increasing the detection rate of lesions. In order to summarize the clinical application value of DECT in hepatobiliary diseases, we searched the technical principles of DECT and its existing studies, case reports, and clinical guidelines in hepatobiliary imaging from 2010 to 2023 (especially in the past 5 years) through PubMed and CNKI, focusing on the clinical application of DECT in hepatobiliary diseases, including liver tumors, diffuse liver lesions, and biliary system lesions. The first part of this article briefly describes the basic concept and technical advantages of DECT. The following will be reviewed:the detection of lesions, diagnosis and differential diagnosis of lesions, hepatic steatosis, quantitative analysis of liver iron, and analyze the advantages and disadvantages of DECT in hepatobiliary imaging. Finally, the contents of this paper are summarized and the development prospect of DECT in hepatobiliary imaging is prospected.

The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma

OBJECTIVE

We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC).

METHODS

A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed.

RESULTS

The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency.

CONCLUSION

DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

MB-DECTNet: a model-based unrolling network for accurate 3D dual-energy CT reconstruction from clinically acquired helical scans

Objective.Over the past several decades, dual-energy CT (DECT) imaging has seen significant advancements due to its ability to distinguish between materials. DECT statistical iterative reconstruction (SIR) has exhibited potential for noise reduction and enhanced accuracy. However, its slow convergence and substantial computational demands render the elapsed time for 3D DECT SIR often clinically unacceptable. The objective of this study is to accelerate 3D DECT SIR while maintaining subpercentage or near-subpercentage accuracy.Approach.We incorporate DECT SIR into a deep-learning model-based unrolling network for 3D DECT reconstruction (MB-DECTNet), which can be trained end-to-end. This deep learning-based approach is designed to learn shortcuts between initial conditions and the stationary points of iterative algorithms while preserving the unbiased estimation property of model-based algorithms. MB-DECTNet comprises multiple stacked update blocks, each containing a data consistency layer (DC) and a spatial mixer layer, with the DC layer functioning as a one-step update from any traditional iterative algorithm.Main results.The quantitative results indicate that our proposed MB-DECTNet surpasses both the traditional image-domain technique (MB-DECTNet reduces average bias by a factor of 10) and a pure deep learning method (MB-DECTNet reduces average bias by a factor of 8.8), offering the potential for accurate attenuation coefficient estimation, akin to traditional statistical algorithms, but with considerably reduced computational costs. This approach achieves 0.13% bias and 1.92% mean absolute error and reconstructs a full image of a head in less than 12 min. Additionally, we show that the MB-DECTNet output can serve as an initializer for DECT SIR, leading to further improvements in results.Significance.This study presents a model-based deep unrolling network for accurate 3D DECT reconstruction, achieving subpercentage error in estimating virtual monoenergetic images for a full head at 60 and 150 keV in 30 min, representing a 40-fold speedup compared to traditional approaches. These findings have significant implications for accelerating DECT SIR and making it more clinically feasible.

Tophaceous gouty arthritis with spondylolysis: a case report

Spinal gout is a rare occurrence, and the combination of gout with lumbar spondylolysis has not been reported. We present a unique case involving a 29-year-old male who complained of low back pain for 1 month. Computed tomography and magnetic resonance imaging revealed articular subchondral erosions and a mass in the left L5-S1 facet joints. Initially treated for a spinal infection, the patient subsequently underwent lumbar spinal canal decompression and fusion, achieving complete relief. Postoperative pathology confirmed the spinal lesions to be tophaceous gout. Dual-energy CT or biopsy can assist in confirming the diagnosis. This report discusses another rare case of tophaceous gouty arthritis with spondylolysis to be added to the literature.

Occult contrast retention post-thrombectomy on 24-h follow-up dual-energy CT: Associations and impact on imaging analysis

BACKGROUND

Following reperfusion treatment in ischemic stroke, computed tomography (CT) imaging at 24 h is widely used to assess radiological outcomes. Even without visible hyperattenuation, occult angiographic contrast may persist in the brain and confound Hounsfield unit-based imaging metrics, such as net water uptake (NWU).

AIMS

We aimed to assess the presence and factors associated with retained contrast post-thrombectomy on 24-h imaging using dual-energy CT (DECT), and its impact on the accuracy of NWU as a measure of cerebral edema.

METHODS

Consecutive patients with anterior circulation large vessel occlusion who had post-thrombectomy DECT performed 24-h post-treatment from two thrombectomy stroke centers were retrospectively studied. NWU was calculated by interside comparison of HUs of the infarct lesion and its mirror homolog. Retained contrast was quantified by the difference in NWU values with and without adjustment for iodine. Patients with visible hyperdensities from hemorrhagic transformation or visible contrast retention and bilateral infarcts were excluded. Cerebral edema was measured by relative hemispheric volume (rHV) and midline shift (MLS).

RESULTS

Of 125 patients analyzed (median age 71 (IQR = 61-80), baseline National Institutes of Health Stroke Scale (NIHSS) 16 (IQR = 9.75-21)), reperfusion (defined as extended-Thrombolysis-In-Cerebral-Infarction 2b-3) was achieved in 113 patients (90.4%). Iodine-subtracted NWU was significantly higher than unadjusted NWU (17.1% vs 10.8%, p < 0.001). In multivariable median regression analysis, increased age (p = 0.024), number of passes (p = 0.006), final infarct volume (p = 0.023), and study site (p = 0.021) were independently associated with amount of retained contrast. Iodine-subtracted NWU correlated with rHV (rho = 0.154, p = 0.043) and MLS (rho = 0.165, p = 0.033) but unadjusted NWU did not (rHV rho = -0.035, p = 0.35; MLS rho = 0.035, p = 0.347).

CONCLUSIONS

Angiographic iodine contrast is retained in brain parenchyma 24-h post-thrombectomy, even without visually obvious hyperdensities on CT, and significantly affects NWU measurements. Adjustment for retained iodine using DECT is required for accurate NWU measurements post-thrombectomy. Future quantitative studies analyzing CT after thrombectomy should consider occult contrast retention.

Microtrabecular Fracture with Intramedullary Fat Globules

Teaching Point: In rare cases, trauma may result in intramedullary fat globules which have a characteristic aspect both on MRI and dual-energy CT.

Promotion of higher rates of early fusion using activated titanium versus polyetheretherketone cages in adults undergoing 1- and 2-level transforaminal lumbar interbody fusion procedures: a randomized controlled trial

OBJECTIVE

There is ongoing debate on the relative benefits and drawbacks of polyetheretherketone (PEEK) versus titanium (Ti) in generating a bone-to-implant surface microenvironment conducive to osseointegration. Micro- and nanoscale internal and topographic cage modifications have recently been posited to facilitate osseointegration and fusion, but human in vivo confirmation remains lacking. The authors of this study sought to directly compare early radiological outcomes in adults undergoing 1- and 2-level transforaminal lumbar interbody fusion (TLIF) procedures using either PEEK or nano-etched Ti interbody cages with an incorporated microlattice structure.

METHODS

Patients were enrolled in a single academic center using a single-blind randomized controlled superiority design. Screening was undertaken from a pool of consecutive patients eligible for TLIF to undergo placement in a 1:1 ratio of either lordotic PEEK or activated Ti cages at each level of 1- or 2-level procedures. An a priori power analysis was performed and a preplanned interim analysis was undertaken once 50 of 70 patients were enrolled. Patient study data were collected perioperatively and uploaded to a Research Electronic Data Capture (REDCap) registry. Interbody fusion was assessed based on 6-month postoperative lumbar dual-energy CT (DECT) studies using the method of Brantigan and Steffee, as modified to describe the Fraser definition of locked pseudarthrosis (Brantigan-Steffee-Fraser [BSF] scale).

RESULTS

In the final cohort of 50 patients, 40 interbody levels implanted with PEEK cages were compared with 34 interbody levels with activated Ti cages. The trial was stopped early given the results of an interim analysis with respect to the primary outcome. Surgical parameters including number of levels treated, average cage height, and position were not different between groups. For the PEEK and activated Ti groups, 20.6% versus 84.0% demonstrated BSF grade 3 fusion on 6-month postoperative DECT imaging (p < 0.001). Subsidence at 6 months on DECT was identified in 12 (41.4%) of PEEK levels versus 5 (20.8%) of activated Ti levels (p < 0.001). BSF-3 grading was predictive of segmental stability and numeric rating scale (NRS) leg pain improvement at 1 year postoperatively. Oswestry Disability Index and NRS back and leg pain scores all improved similarly in both cohorts at 1 year postoperatively.

CONCLUSIONS

Activated Ti interbody cages mediate early fusion at significantly higher rates with lower rates of subsidence as compared with PEEK cages. These findings support the idea that interbody cage microscale properties, including surface topography, may play a primary role in facilitating osseointegration and fusion.

Comparison of Dual-Energy Computed Tomography Pulmonary Angiography-Derived Contrast Enhancement with Standard Dual-Energy Pulmonary Angiography in Diagnosing Subsegmental Pulmonary Embolism: A Prospective Study

Objective  In this study, we compare the diagnostic accuracy of dual-energy (DE) computed tomography pulmonary angiography (CTPA) derived contrast enhancement (DECTPA, CTPA images with iodine maps) with standard dual-energy pulmonary angiography (SCTPA) for diagnosis of subsegmental pulmonary embolism in the cases with clinical suspicion of acute pulmonary embolism (APE). Materials and Methods  We included 50 cases with clinical suspicion of APE that were referred for CTPA. All the patients underwent CTPA in the dual-energy protocol. Two radiologists evaluated the images. The first radiologist interpreted the SCTPA images (vascular images) and the second radiologist interpreted the DECTPA (CTPA images with iodine maps) for findings of APE. We calculated the sensitivity, specificity, and negative predictive value of DECTPA vis-à-vis SCTPA images. Results  The DECTPA with the advantage of iodine map utilization yielded higher detection of thrombi in peripheral subsegmental arteries (72 vs. 99; p  = - 0.001) as compared to the SCTPA images by identification of 18 new perfusion defects (interquartile range [IQR]: 0-1) that were consistent with APE. Filling defects were identified in 27 (IQR: 0-4) more subsegmental arteries supplying these 18 areas, which were not detected on SCTPA alone. These 18 perfusion defects were identified in 13 cases. In these 13 cases, 4 new cases were diagnosed that were negative on CTPA ( p  = -0.125). In the evaluation of the APE, sensitivity and specificity were calculated and it was found that DECTPA showed 100% sensitivity and 86% specificity with 100% negative predictive value in the detection of thrombi as compared to the routine CTPA. Conclusion  DECTPA has higher sensitivity and negative predictive value in the detection of the subsegmental perfusion defect identification as compared to SCTPA.

Dual-Energy CT for Pediatric Thoracic Imaging: A Review

Dual-energy CT has expanded the potential of thoracic imaging in both children and adults. Data processing allows material- and energy-specific reconstructions, which improve material differentiation and tissue characterization compared with single-energy CT. Material-specific reconstructions include iodine, virtual unenhanced, perfusion blood volume, and lung vessel images, which can improve assessment of vascular, mediastinal, and parenchymal abnormalities. The energy-specific reconstruction algorithm allows virtual monoenergetic reconstructions, including low-energy images to increase iodine conspicuity and high-energy images to reduce beam-hardening and metal artifacts. This review highlights dual-energy CT principles, hardware, and postprocessing algorithms; the clinical applications of dual-energy CT; and the potential benefits of photon counting (the most recently introduced iteration of spectral imaging) in pediatric thoracic imaging.

Establishing quality control action limits for CT number accuracy in spectral images using an American College of Radiology phantom

BACKGROUND

Computed tomography (CT) number accuracy is important for quality assurance in CT imaging. However, in dual-energy CT imaging, there are no widely used action limits for CT number accuracy in spectral images, information that is urgently needed.

PURPOSE

To establish action limits for spectral CT images using longitudinal spectral data and an American College of Radiology (ACR) phantom.

METHODS

An ACR accreditation phantom was scanned routinely as part of a quality control program in our institution. We selected and analyzed 57 continuous weekly scans. The CT numbers or the density values of conventional and spectral images, including virtual monoenergetic images (40, 50, 70, 120, and 200 keV), iodine maps, calcium suppressed, and virtual non-contrast images, were measured in the four inserts (solid water, bone, polyethylene, and acrylic) of the phantom. Longitudinal data were analyzed for correlation using Pearson's correlation coefficient (r) and standard deviation (SD). The SD ratios between spectral images and conventional images were calculated and the action limits for spectral images were established based on the action limits from the ACR.

RESULTS

Strong to very strong correlations (r > 0.70 or r < -0.70) were found among most spectral image types except the 200 keV images using solid water, polyethylene, and acrylic inserts (r = [-0.45, 0.64]). The SD ratio was highest for the 40 keV images, ranging from 2.8 to 6.5. The action limits of the bone insert were baseline ± 5.3 mg/mL for the iodine map and ranged from baseline ± 23.0 HU to baseline ± 391.9 HU for the other image types. The action limits for solid water ranged from baseline ± 4.1 HU to baseline ± 25.3 HU. The results for the polyethylene and the acrylic insert were close to those for solid water. Baselines can be established using the average of the initial 5∼10 measurements.

CONCLUSIONS

Using longitudinal data, we estimated the action limits for CT number accuracy in the spectral images. This paves the way for establishing a comprehensive quality control program for spectral CT imaging.

Automatic Kidney Stone Composition Analysis Method Based on Dual-energy CT

BACKGROUND

The composition of kidney stones is related to the hardness of the stones. Knowing the composition of the stones before surgery can help plan the laser power and operation time of percutaneous nephroscopic surgery. Moreover, patients can be treated with medications if the kidney stone is compounded by uric acid before treatment, which can relieve the patients of the pain of surgery. However, although the literature generally reports the kidney stone composition analysis method base on dual-energy CT images, the accuracy of these methods is not enough; they need manual delineation of the kidney stone location, and these methods cannot analyze mixed composition kidney stones.

OBJECTIVE

This study aimed to overcome the problem related to identifying kidney stone composition; we need an accurate method to analyze the composition of kidney stones.

METHODS

In this paper, we proposed the automatic kidney stone composition analysis algorithm based on a dual-energy CT image. The algorithm first segmented the kidney stone mask by deep learning model, then analyzed the composition of each stone by machine learning model.

RESULTS

The experimental results indicate that the proposed algorithm can segment kidney stones accurately (AUC=0.96) and predict kidney stone composition accurately (mean Acc=0.86, mean Se=0.75, mean Sp=0.9, mean F1=0.75, mean AUC=0.83, MR (Exact match ratio)=0.6).

CONCLUSION

The proposed method can predict the composition and location of kidney stones, which can guide its treatment. Experimental results show that the weighting strategy can improve kidney stone segmentation performance. In addition, the multi-label classification model can predict kidney stone composition precisely, including the mixed composition kidney stones.

Comparison of diagnostic efficacy of dual-energy CT and ultrasound in gouty arthritis

OBJECTIVE

To explore the performance and effect of dual-energy computed tomography (CT) and ultrasound in the diagnosis of gouty arthritis and to provide a reference for the clinical diagnosis of gouty arthritis.

METHODS

A retrospective analysis of 76 patients with gouty arthritis admitted to the hospital from June 2020 to June 2022 was conducted. Patients were diagnosed with gouty arthritis using ultrasound and dual-energy CT technology. The accuracy of diagnosis by different imaging techniques was analyzed along with the imaging findings of ultrasound and dual-energy CT.

RESULTS

Seventy-six patients, 60 men and 16 women, ranging in age from 20 to 77 years (mean age 50.8 ± 10.92 years), presented with uric acid levels of 254.1-720.05 μmol/L (mean 482.17 ± 105.06 μmol/L) and C-reactive protein levels ranging from 4.25 to 10.3 mg/L. The receiver operating characteristic curve showed that the area under the curve and specificity of serum uric acid were higher by dual-energy CT than those of ultrasound in the diagnosis of gouty arthritis. The dual-energy CT detection rate of tophi was significantly higher than the ultrasound detection rate (p < .05). For inflammatory effusion and synovial thickening, the ultrasound detection rates were significantly higher than the dual-energy CT detection rates (p < .05). Regarding soft-tissue edema, the detection rate of the two methods was not significantly different (p > .05).

CONCLUSION

Compared with ultrasound, dual-energy CT has increased accuracy in the diagnosis of gouty arthritis.

Usefulness of Three-Dimensional Iodine Mapping Quantified by Dual-Energy CT for Differentiating Thymic Epithelial Tumors

Background: Dual-energy CT has been reported to be useful for differentiating thymic epithelial tumors. The purpose is to evaluate thymic epithelial tumors by using three-dimensional (3D) iodine density histogram texture analysis on dual-energy CT and to investigate the association of extracellular volume fraction (ECV) with the fibrosis of thymic carcinoma. Methods: 42 patients with low-risk thymoma (n = 20), high-risk thymoma (n = 16), and thymic carcinoma (n = 6) were scanned by dual-energy CT. 3D iodine density histogram texture analysis was performed for each nodule on iodine density mapping: Seven texture features (max, min, median, average, standard deviation [SD], skewness, and kurtosis) were obtained. The iodine effect (average on DECT180s-average on unenhanced DECT) and ECV on DECT180s were measured. Tissue fibrosis was subjectively rated by one pathologist on a three-point grade. These quantitative data obtained by examining associations with thymic carcinoma and high-risk thymoma were analyzed with univariate and multivariate logistic regression models (LRMs). The area under the curve (AUC) was calculated by the receiver operating characteristic curves. p values < 0.05 were significant. Results: The multivariate LRM showed that ECV > 21.47% in DECT180s could predict thymic carcinoma (odds ratio [OR], 11.4; 95% confidence interval [CI], 1.18-109; p = 0.035). Diagnostic performance was as follows: Sensitivity, 83.3%; specificity, 69.4%; AUC, 0.76. In high-risk thymoma vs. low-risk thymoma, the multivariate LRM showed that the iodine effect ≤1.31 mg/cc could predict high-risk thymoma (OR, 7; 95% CI, 1.02-39.1; p = 0.027). Diagnostic performance was as follows: Sensitivity, 87.5%; specificity, 50%; AUC, 0.69. Tissue fibrosis significantly correlated with thymic carcinoma (p = 0.026). Conclusions: ECV on DECT180s related to fibrosis may predict thymic carcinoma from thymic epithelial tumors, and the iodine effect on DECT180s may predict high-risk thymoma from thymoma.

Can virtual non-contrast imaging replace true non-contrast imaging in multiphase scanning of the neck region?

Background

Dual-energy computed tomography (DECT) is an advanced imaging method that enables reconstruction of virtual non-contrast (VNC) images from a contrast-enhanced acquisition. This has the potential to reduce radiation exposure by eliminating the need for a true non-contrast (TNC) phase.

Purpose

The purpose is to evaluate the feasibility of VNC images in the neck region.

Materials and methods

A total of 100 patients underwent a DECT scan as part of diagnostic workup of primary hyperparathyroidism. VNC images were reconstructed from 30 s (arterial) and 50 s (venous) post-contrast scans. Regions of interest (ROIs) were placed in thyroid tissue, lymph node, carotid artery, jugular vein, fat, and sternocleidomastoid muscle. Mean densities of all anatomical structures were compared between VNC and TNC images.

Results

For all anatomical structures except the thyroid gland, the difference in mean density between TNC and VNC images was less than 15 HU. The mean difference in density between TNC and VNC images of the thyroid was 53.2 HU (95% CI 46.8; 59.6, p = <0.001).

Conclusion

This study demonstrated an acceptable agreement in density between true non-contrast and virtual non-contrast images for most anatomical structures in the neck region. Therefore, VNC images may have the potential to replace TNC images in the neck. However, due to significant differences in CT density of thyroid tissue, true non-contrast imaging cannot be directly substituted by virtual non-contrast imaging when examining the thyroid and its surrounding tissue.

Hemorrhagic Transformation Assessment Based on Dual Energy CT of Immediately and Twenty-Four Hours after Endovascular Thrombectomy for Acute Ischemic Stroke

BACKGROUND

Dual-energy CT (DECT) shows good performance in differentiating hemorrhage from contrast staining (CS). However, no guidelines have standardized the post-endovascular thrombectomy (EVT) examination time. We evaluated the value of performing DECT immediately and 24 h post-EVT in the diagnosis and prediction of hemorrhagic transformation (HT).

METHODS

Two readers evaluated simulated conventional CT (sCCT) images compared with a second reading with DECT, establishing the diagnosis of HT immediately and 24 h post-EVT. Another reader's diagnosis 2-7 days post-EVT using non-contrast CT was identified as the final diagnostic criteria.

RESULTS

DECT performed immediately and 24 h post-EVT changed 22.4% (52/232) and 12.5% (29/232) of sCCT-based HT diagnoses, respectively (χ2 = 10.7, p < 0.05). The sensitivity, negative predictive value (NPV), and accuracy of DECT performed immediately post-EVT for predicting the final diagnosis of HT were 33.6%, 58.9%, and 65.9%, respectively, whereas those for DECT performed 24 h post-EVT were 82.4%, 84.3%, and 90.9%, respectively (χ2 = 58.0, χ2 = 42.9, χ2 = 13.6; p < 0.05). The specificity and positive predictive value were both 100.0%. Delayed HT occurred in 50.0% (78/156) and 42.2% (19/45) of patients with CS diagnosed immediately and 24 h post-EVT, respectively.

CONCLUSIONS

DECT performed immediately post-EVT changed a greater proportion of real-time HT diagnoses, whereas that performed 24 h post-EVT had higher sensitivity, NPV, and accuracy in predicting the final diagnosis of HT. A substantial proportion of patients with CS diagnosed at these two post-EVT timepoints subsequently developed delayed HT.

Thoracic Diseases: Technique and Applications of Dual-Energy CT

Dual-energy computed tomography (DECT) is one of the most promising technological innovations made in the field of imaging in recent years. Thanks to its ability to provide quantitative and reproducible data, and to improve radiologists' confidence, especially in the less experienced, its applications are increasing in number and variety. In thoracic diseases, DECT is able to provide well-known benefits, although many recent articles have sought to investigate new perspectives. This narrative review aims to provide the reader with an overview of the applications and advantages of DECT in thoracic diseases, focusing on the most recent innovations. The research process was conducted on the databases of Pubmed and Cochrane. The article is organized according to the anatomical district: the review will focus on pleural, lung parenchymal, breast, mediastinal, lymph nodes, vascular and skeletal applications of DECT. In conclusion, considering the new potential applications and the evidence reported in the latest papers, DECT is progressively entering the daily practice of radiologists, and by reading this simple narrative review, every radiologist will know the state of the art of DECT in thoracic diseases.

Motion-compensated scheme for sequential scanned statistical iterative dual-energy CT reconstruction

Objective.Dual-energy computed tomography (DECT) has been widely used to reconstruct numerous types of images due its ability to better discriminate tissue properties. Sequential scanning is a popular dual-energy data acquisition method as it requires no specialized hardware. However, patient motion between two sequential scans may lead to severe motion artifacts in DECT statistical iterative reconstructions (SIR) images. The objective is to reduce the motion artifacts in such reconstructions.Approach.We propose a motion-compensation scheme that incorporates a deformation vector field into any DECT SIR. The deformation vector field is estimated via the multi-modality symmetric deformable registration method. The precalculated registration mapping and its inverse or adjoint are then embedded into each iteration of the iterative DECT algorithm.Main results.Results from a simulated and clinical case show that the proposed framework is capable of reducing motion artifacts in DECT SIRs. Percentage mean square errors in regions of interest in the simulated and clinical cases were reduced from 4.6% to 0.5% and 6.8% to 0.8%, respectively. A perturbation analysis was then performed to determine errors in approximating the continuous deformation by using the deformation field and interpolation. Our findings show that errors in our method are mostly propagated through the target image and amplified by the inverse matrix of the combination of the Fisher information and Hessian of the penalty term.Significance.We have proposed a novel motion-compensation scheme to incorporate a 3D registration method into the joint statistical iterative DECT algorithm in order to reduce motion artifacts caused by inter-scan motion, and successfully demonstrate that interscan motion corrections can be integrated into the DECT SIR process, enabling accurate imaging of radiological quantities on conventional SECT scanners, without significant loss of either computational efficiency or accuracy.

Dose uncertainty due to energy dependence in dual-energy computed tomography

Purpose

To evaluate the absolute dose uncertainty at 2 different energies and for the large and small bowtie filters in dual-energy computed tomography (DECT).

Material and methods

Measurements were performed using DECT at 80 kV and 140 kilovoltage peak (kVp), and single-energy computed tomography (CT) at 120 kV. The absolute dose was calculated from the mass-energy absorption obtained from the half-value layer (HVL) of aluminium.

Results

The difference in the water-to-air ratio of the mean mass energy-absorption coefficients at 80 kV and 140 kV was 2.0% for the small bow-tie filter and 3.0% for the large bow-tie filter. At lower tube voltages, the difference in the absorbed dose with the large and small bow-tie filters was larger.

Conclusions

The absolute dose uncertainty due to energy dependence was 3.0%, which could be reduced with single-energy beams at 120 kV or by using the average effective energy measurement with dual-energy beams.

Minimal Fat Content in Papillary Renal Cell Carcinoma Diagnosed with Dual-Layer Dual-Energy CT

A 56-year-old man with a previous right nephrectomy for multiple papillary renal cell carcinomas (pRCC) underwent a follow-up CT scan. Using a dual-layer dual-energy CT (dlDECT), we demonstrated the presence of a small amount of fat in a 2.5 cm pRCC that mimicked the diagnosis of angiomyolipoma (AML). Histological examination demonstrated the absence of macroscopic intratumoral adipose tissue, showing a fair amount of enlarged foam macrophages loaded with intracytoplasmic lipids. The presence of fat density in an RCC is an extremely rare occurrence in the literature. To our knowledge, this is the first description using dlDECT of a minimal amount of fat tissue in a small RCC due to the presence of tumor-associated foam macrophages. Radiologists should be aware of this possibility when characterizing a renal mass with DECT. The option of RCCs must be considered, especially in the case of masses with an aggressive character or a positive history of RCC.

Spectral CT: Current Liver Applications

Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.

Obesity-Related Pitfalls of Virtual versus True Non-Contrast Imaging-An Intraindividual Comparison in 253 Oncologic Patients

OBJECTIVES

Dual-source dual-energy CT (DECT) facilitates reconstruction of virtual non-contrast images from contrast-enhanced scans within a limited field of view. This study evaluates the replacement of true non-contrast acquisition with virtual non-contrast reconstructions and investigates the limitations of dual-source DECT in obese patients.

MATERIALS AND METHODS

A total of 253 oncologic patients (153 women; age 64.5 ± 16.2 years; BMI 26.6 ± 5.1 kg/m²) received both multi-phase single-energy CT (SECT) and DECT in sequential staging examinations with a third-generation dual-source scanner. Patients were allocated to one of three BMI clusters: non-obese: <25 kg/m² (n = 110), pre-obese: 25-29.9 kg/m² (n = 73), and obese: >30 kg/m² (n = 70). Radiation dose and image quality were compared for each scan. DECT examinations were evaluated regarding liver coverage within the dual-energy field of view.

RESULTS

While arterial contrast phases in DECT were associated with a higher CTDI_vol than in SECT (11.1 vs. 8.1 mGy; p < 0.001), replacement of true with virtual non-contrast imaging resulted in a considerably lower overall dose-length product (312.6 vs. 475.3 mGy·cm; p < 0.001). The proportion of DLP variance predictable from patient BMI was substantial in DECT (R² = 0.738) and SECT (R² = 0.620); however, DLP of SECT showed a stronger increase in obese patients (p < 0.001). Incomplete coverage of the liver within the dual-energy field of view was most common in the obese subgroup (17.1%) compared with non-obese (0%) and pre-obese patients (4.1%).

CONCLUSION

DECT facilitates a 30.8% dose reduction over SECT in abdominal oncologic staging examinations. Employing dual-source scanner architecture, the risk for incomplete liver coverage increases in obese patients.

Dual-energy CT-based stopping power prediction for dental materials in particle therapy

Radiotherapy with protons or light ions can offer accurate and precise treatment delivery. Accurate knowledge of the stopping power ratio (SPR) distribution of the tissues in the patient is crucial for improving dose prediction in patients during planning. However, materials of uncertain stoichiometric composition such as dental implant and restoration materials can substantially impair particle therapy treatment planning due to related SPR prediction uncertainties. This study investigated the impact of using dual-energy computed tomography (DECT) imaging for characterizing and compensating for commonly used dental implant and restoration materials during particle therapy treatment planning. Radiological material parameters of ten common dental materials were determined using two different DECT techniques: sequential acquisition CT (SACT) and dual-layer spectral CT (DLCT). DECT-based direct SPR predictions of dental materials via spectral image data were compared to conventional single-energy CT (SECT)-based SPR predictions obtained via indirect CT-number-to-SPR conversion. DECT techniques were found overall to reduce uncertainty in SPR predictions in dental implant and restoration materials compared to SECT, although DECT methods showed limitations for materials containing elements of a high atomic number. To assess the influence on treatment planning, an anthropomorphic head phantom with a removable tooth containing lithium disilicate as a dental material was used. The results indicated that both DECT techniques predicted similar ranges for beams unobstructed by dental material in the head phantom. When ion beams passed through the lithium disilicate restoration, DLCT-based SPR predictions using a projection-based method showed better agreement with measured reference SPR values (range deviation: 0.2 mm) compared to SECT-based predictions. DECT-based SPR prediction may improve the management of certain non-tissue dental implant and restoration materials and subsequently increase dose prediction accuracy.

Quantitative Assessment of Lung Volumes and Enhancement in Patients with COVID-19: Role of Dual-Energy CT

Dual-energy computed tomography (DECT) has been used for detecting pulmonary embolism, but the role of lung perfusion DECT as a predictor of prognosis of coronavirus disease 2019 (COVID-19) has not been defined yet. The aim of our study was to explore whether the enhancement pattern in COVID-19+ patients relates to the disease outcome. A secondary aim was to compare the lung volumes in two subgroups of patients. In this observational study, we considered all consecutive COVID-19+ patients who presented to the emergency room between January 2021 and December 2021 with respiratory symptoms (with mild to absent lung consolidation) and were studied by chest contrast-enhanced DECT to be eligible. Two experienced radiologists post-processed the images using the "lung-analysis" software (SyngoVia). Absolute and relative enhancement lung volumes were assessed. Patients were stratified in two subgroups depending on clinical outcome at 30 days: (i) good outcome (i.e., discharge, absence of clinical or imaging signs of disease); (ii) bad outcome (i.e., hospitalization, death). Patient sub-groups were compared using chi-square test or Fisher test for qualitative parameters, chi-square test or Spearman's Rho test for quantitative parameters, Students' t-test for parametric variables and Wilcoxon test for non-parametric variables. We enrolled 78 patients (45M), of whom, 16.7% had good outcomes. We did not observe any significant differences between the two groups, both in terms of the total enhancement evaluation (p = 0.679) and of the relative enhancement (p = 0.918). In contrast, the average lung volume of good outcome patients (mean value of 4262 mL) was significantly larger than that of bad outcome patients (mean value of 3577.8 mL), p = 0.0116. All COVID-19+ patients, with either good or bad outcomes, presented similar enhancement parameters and relative enhancements, underlining no differences in lung perfusion. Conversely, a significant drop in lung volume was identified in the bad outcome subgroup eligible compared to the good outcome subgroup.

Dual Energy-Derived Metrics for Differentiating Adrenal Adenomas From Nonadenomas on Single-Phase Contrast-Enhanced CT

BACKGROUND. Adrenal masses are often indeterminate on single-phase postcontrast CT. Dual-energy CT (DECT) with three-material decomposition algorithms may aid characterization. OBJECTIVE. The purpose of this study was to compare the diagnostic performance of metrics derived from portal venous phase DECT, including virtual noncontrast (VNC) attenuation, fat fraction, iodine density, and relative enhancement ratio, for characterizing adrenal masses. METHODS. This retrospective study included 128 patients (82 women, 46 men; mean age, 64.6 ± 12.7 [SD] years) who between January 2016 and December 2019 underwent portal venous phase abdominopelvic DECT that showed a total of 139 adrenal lesions with an available reference standard based on all imaging, clinical, and pathologic records (87 adenomas, 52 nonadenomas [48 metastases, two adrenal cortical carcinomas, one ganglioneuroma, one hematoma]). Two radiologists placed ROIs to determine the following characteristics of the masses: VNC attenuation, fat fraction, iodine density normalized to portal vein, and for masses with VNC greater than 10 HU, relative enhancement ratio (ratio of portal venous phase attenuation to VNC attenuation). Readers' mean measurements were used for ROC analyses, and clinically optimal thresholds were derived as thresholds yielding the highest sensitivity at 100% specificity. RESULTS. Adenomas and nonadenomas were significantly different (all p < .001) in VNC attenuation (mean ± SD, 18.5 ± 12.9 vs 34.1 ± 8.9 HU), fat fraction (mean ± SD, 24.3% ± 8.2% vs 14.2% ± 5.6%), normalized iodine density (mean ± SD, 0.34 ± 0.15 vs 0.17 ± 0.17), and relative enhancement ratio (mean ± SD, 186% ± 96% vs 58% ± 59%). AUCs for all metrics ranged from 0.81 through 0.91. The metric with highest sensitivity for adenoma at the clinically optimal threshold (i.e., 100% specificity) was fat fraction (threshold, ≥ 23.8%; sensitivity, 59% [95% CI, 48-69%]) followed by VNC attenuation (≤ 15.2 HU; sensitivity, 39% [95% CI, 29-50%]), relative enhancement ratio (≥ 214%; sensitivity, 37% [95% CI, 25-50%]), and normalized iodine density (≥ 0.90; sensitivity, 1% (95% CI, 0-60%]). VNC attenuation at the traditional true noncontrast attenuation threshold of 10 HU or lower had sensitivity of 28% (95% CI, 19-38%) and 100% specificity. Presence of fat fraction 23.8% or greater or relative enhancement ratio 214% or greater yielded sensitivity of 68% (95% CI, 57-77%) with 100% specificity. CONCLUSION. For adrenal lesions evaluated with single-phase DECT, fat fraction had higher sensitivity than VNC attenuation at both the clinically optimal threshold and the traditional threshold of 10 HU or lower. CLINICAL IMPACT. By helping to definitively diagnose adenomas, DECT-derived metrics can help avoid downstream imaging for incidental adrenal lesions.

Advanced imaging of head and neck infections

When head and neck infection is suspected, appropriate imaging contributes to treatment decisions and prognosis. While contrast-enhanced CT is the standard imaging modality for evaluating head and neck infections, MRI can better characterize the skull base, intracranial involvement, and osteomyelitis, implying that these are complementary techniques for a comprehensive assessment. Both CT and MRI are useful in the evaluation of abscesses and thrombophlebitis, while MRI is especially useful in the evaluation of intracranial inflammatory spread/abscess formation, differentiation of abscess from other conditions, evaluation of the presence and activity of inflammation and osteomyelitis, evaluation of mastoid extension in middle ear cholesteatoma, and evaluation of facial neuritis and labyrinthitis. Apparent diffusion coefficient derived from diffusion-weighted imaging is useful for differential diagnosis and treatment response of head and neck infections in various anatomical sites. Dynamic contrast-enhanced MRI perfusion may be useful in assessing the activity of skull base osteomyelitis. MR bone imaging may be of additional value in evaluating bony structures of the skull base and jaw. Dual-energy CT is helpful in reducing metal artifacts, evaluating deep neck abscess, and detecting salivary stones. Subtraction CT techniques are used to detect progressive bone-destructive changes and to reduce dental amalgam artifacts. This article provides a region-based approach to the imaging evaluation of head and neck infections, using both conventional and advanced imaging techniques.

Improving the Visualization of the Adrenal Veins Using Virtual Monoenergetic Images from Dual-Energy Computed Tomography before Adrenal Venous Sampling

(1) Background: This study explored the optimal energy level in advanced virtual monoenergetic images (VMI+) from dual-energy computed tomography angiography (DE-CTA) for adrenal veins visualization before adrenal venous sampling (AVS). (2) Methods: Thirty-nine patients were included in this prospective single-center study. The CT value, noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured in both adrenal veins and abdominal solid organs and were then compared between VMI+ within the range of 40-80 kiloelectron volt (keV). The visualization rate of the adrenal veins and the overall image quality of solid organs were subjectively compared among different keV VMI+. The AVS success rate was recorded for 20 patients. (3) Results: For the adrenal veins, 40 keV VMI+ had the peak CT value, noise and CNR (p < 0.05). Subjectively, the visualization rate was the highest at 40 keV (100% for the right adrenal vein, and 97.4% for the left adrenal vein) (p < 0.05). For solid organs, the CT value, noise and CNR at 50 keV were lower than those at 40 keV (p < 0.05), but the SNR was similar between 40 keV and 50 keV. The overall subjective image quality of solid organs at 50 keV was the best (p < 0.05). The AVS success rate was 95%. (4) Conclusions: For VMI+, 40 keV was the preferential energy level to obtain a high visualization rate of the adrenal veins and a high success rate of AVS, while 50 keV was the favorable energy level for the depiction of abdominal organs.

Dual-Energy-CT for Osteitis and Fat Lesions in Axial Spondyloarthritis: How Feasible Is Low-Dose Scanning?

BACKGROUND

To assess the ability of low-dose dual-energy computed tomography (ld-DECT) virtual non-calcium (VNCa) images for detecting bone marrow pathologies of the sacroiliac joints (SIJs) in patients with axial spondyloarthritis (axSpA). Material and Methods: Sixty-eight patients with suspected or proven axSpA underwent ld-DECT and MRI of the SIJ. VNCa images were reconstructed from DECT data and scored for the presence of osteitis and fatty bone marrow deposition by two readers with different experience (beginner and expert). Diagnostic accuracy and correlation (Kohen's k) with magnetic resonance imaging (MRI) as the reference standard were calculated for the overall and for each reader separately. Furthermore, quantitative analysis was performed using region-of-interest (ROI) analysis. Results: Twenty-eight patients were classified as positive for osteitis, 31 for fatty bone marrow deposition. DECT's sensitivity (SE) and specificity (SP) were 73.3% and 44.4% for osteitis and 75% and 67.3% for fatty bone lesions, respectively. The expert reader achieved higher diagnostic accuracy for both osteitis (SE 93.33%; SP: 51.85%) and fatty bone marrow deposition (SE: 65%; SP: 77.55%) than the beginner (SE: 26.67%; SP: 70.37% for osteitis; SE: 60%; SP: 44.9% for fatty bone marrow deposition). Overall correlation with MRI was moderate (r = 0.25, p = 0.04) for osteitis and fatty bone marrow deposition (r = 0.25, p = 0.04). Fatty bone marrow attenuation in VNCa images (mean: -129.58 HU; ±103.61 HU) differed from normal bone marrow (mean: 118.84 HU, ±99.91 HU; p < 0.01) and from osteitis (mean: 172 HU, ±81.02 HU; p < 0.01) while osteitis did not differ significantly from normal bone marrow (p = 0.27). Conclusion: In our study, low-dose DECT failed to detect osteitis or fatty lesions in patients with suspected axSpA. Thus, we conclude that higher radiation might be needed for DECT-based bone marrow analysis.

Diagnosis of an Acute Anterior Wall Infarction in Dual-Energy CT

Due to its high morbidity and mortality, myocardial infarction is the leading cause of death worldwide. Against this background, rapid diagnosis is of immense importance. Especially in case of an atypical course, the correct diagnosis may be delayed and thus lead to increased mortality rates. In this report, we present a complex case of acute coronary syndrome. A triple-rule-out CT examination was performed in dual-energy CT (DECT) mode. While pulmonary artery embolism and aortic dissection could be ruled out with conventional CT series, the presence of anterior wall infarction was only detectable on DECT reconstructions. Subsequently, adequate and rapid therapy was then initiated leading to survival of the patient.

Osteomyelitis of the Lower Limb: Diagnostic Accuracy of Dual-Energy CT versus MRI

BACKGROUND

MRI is the preferred imaging technique for the identification of osteomyelitis. The key element for diagnosis is the presence of bone marrow edema (BME). Dual-energy CT (DECT) is an alternative tool which is able to identify BME in the lower limb.

PURPOSE

To compare the diagnostic performance of DECT and MRI for osteomyelitis, using clinical, microbiological, and imaging data as reference standards.

MATERIALS AND METHODS

This prospective single-center study enrolled consecutive patients with suspected bone infections undergoing DECT and MRI imaging from December 2020 to June 2022. Four blinded radiologists with various experience levels (range of 3-21 years) evaluated the imaging findings. Osteomyelitis was diagnosed in the presence of BMEs, abscesses, sinus tracts, bone reabsorption, or gaseous elements. The sensitivity, specificity, and AUC values of each method were determined and compared using a multi-reader multi-case analysis. A p value < 0.05 was considered significant.

RESULTS

In total, 44 study participants (mean age 62.5 years ± 16.5 [SD], 32 men) were evaluated. Osteomyelitis was diagnosed in 32 participants. For the MRI, the mean sensitivity and specificity were 89.1% and 87.5%, while for the DECT they were 89.0% and 72.9%, respectively. The DECT demonstrated a good diagnostic performance (AUC = 0.88), compared with the MRI (AUC = 0.92) (p = 0.12). When considering each imaging finding alone, the best accuracy was achieved by considering BME (AUC for DECT 0.85 versus AUC of MRI of 0.93, with p = 0.07), followed by the presence of bone erosions (AUC 0.77 for DECT and 0.53 for MRI, with p = 0.02). The inter-reader agreement of the DECT (k = 88) was similar to that of the MRI (k = 90).

CONCLUSION

Dual-energy CT demonstrated a good diagnostic performance in detecting osteomyelitis.

Patterns of Postoperative Changes in Lung Volume and Perfusion Assessed by Dual-Energy CT: Comparison of Lobectomy and Limited Resection

BACKGROUND. Pulmonary function tests (PFTs) and perfusion scintigraphy have limited utility for evaluating postoperative changes in regional pulmonary function after lung cancer resection surgery. OBJECTIVE. The purpose of this study is to compare postoperative changes in lung volume and perfusion, as assessed by dual-energy CT (DECT), between patients undergoing surgical resection of lung cancer by lobectomy versus limited resection as well as to assess associations between such changes and the lobar location of the resected tumor. METHODS. This study entailed a retrospective post hoc analysis of a prospective study that enrolled patients awaiting lung cancer resection surgery between March 2019 and February 2020. Eighty-one patients (38 men and 43 women; mean age, 60.5 ± 8.9 [SD] years), 43 of whom underwent lobectomy and 38 of whom underwent limited resection, were included. Patients underwent thoracic DECT and PFT evaluation preoperatively and at 6 months postoperatively. Pulmonary lobes were segmented. Lobar lung volume and lung perfusion ratios (both relative to whole-lung values) were computed. Perfusion measures reflected DECT-derived iodine content. Patients completed 6-month postoperative quality-of-life (QOL) questionnaires. RESULTS. Patients undergoing lobectomy, compared with those undergoing limited resection, had greater increases in the lung volume ratio of the ipsilateral nonresected lobe(s) (mean, 42.3% ± 24.2% [SD] vs 22.9% ± 13.2%, p < .001) and the contralateral lung (mean, 14.6% ± 14.0% vs 6.4% ± 6.9%, p = .002) as well as greater increases in the lung perfusion ratio of the ipsilateral nonresected lobe(s) (mean, 39.9% ± 20.7% [SD] vs 22.8% ± 17.8%, p < .001) and the contralateral lung (mean, 20.9% ± 9.4% vs 4.3% ± 5.6%, p < .001). In patients with right lower lobe tumors, the largest postoperative increases in the lung volume ratio were in the right middle lobe in those undergoing lobectomy (mean, 44.1% ± 21.0%) and limited resection (mean, 24.6% ± 14.5%), whereas the largest postoperative increase in the lung perfusion ratio was in the left lower lobe in those undergoing lobectomy (mean, 53.9% ± 8.6%) and in the right middle lobe in those undergoing limited resection (mean, 32.5% ± 24.1%). Otherwise, the largest increases in lung volume and perfusion ratios occurred in the ipsilateral nonresected lobes (vs the contra-lateral lobes), regardless of the operative approach used and the lobar location. Changes in the lung volume and perfusion ratios in the ipsilateral lobe(s) and the contralateral lung showed weak correlations with certain quality-of-life scores (e.g., for role functioning: ρ = 0.234-0.279 [volume] and -0.233 to -0.284 [perfusion]). CONCLUSION. DECT depicts patterns of lung volume and perfusion changes after lung cancer surgery, depending on the surgical approach (lobectomy vs limited resection) used and the lobar location of the tumor. CLINICAL IMPACT. DECT-derived metrics can help understand variable physiologic impacts of lung cancer resection surgeries.

Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential

Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρ_s) within patients. Currently, theρ_sdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρ_susing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρ_sshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρ_sprediction has been shown to be effective in reducing the uncertainty inρ_sestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρ_sestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρ_sestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρ_sestimation.

Quantitative Dual-Energy Imaging of Bone Marrow Edema Using Multisource Cone-Beam CT with Model-Based Decomposition

Purpose

We investigated the feasibility of dual-energy (DE) detection of bone marrow edema (BME) using a dedicated extremity cone-beam CT (CBCT) with a unique three-source x-ray unit. The sources can be operated at different energies to enable single-scan DE acquisitions. However, they are arranged parallel to the axis of rotation, resulting in incomplete sampling and precluding the application of DE projection-domain decompositions (PDD) for beam-hardening reduction. Therefore, we propose a novel combination of a model-based "one-step" DE two-material decomposition followed by a constrained image-domain change-of-basis to obtain virtual non-calcium (VNCa) images for BME detection.

Methods

DE projections were obtained using an "alternating-kV" protocol by operating the peripheral two sources of the CBCT system at low-energy (60 kV, 0.105 mAs/frame) and the central source at high-energy (100 kV, 0.028 mAs/frame), for a total of 600 frames over 216° of gantry rotation. Projections were processed with detector lag, glare and fast Monte Carlo (MC)-based iterative scatter corrections. Model-based material decomposition (MBMD) was then implemented to obtain aluminum (Al) and polyethylene (PE) volume fraction images with minimal beam-hardening. Statistical ray weights in MBMD were modified to account for regions with highly oblique sampling by the peripheral sources. To generate the VNCa maps, image-domain decomposition (IDD) constrained by the volume conservation principle (VCP) was performed to convert the Al and PE MBMD images into volume fractions of water, fat and cortical bone. Accuracy of BME detection was evaluated using physical phantom data acquired on the multi-source extremity CBCT scanner.

Results

The proposed framework estimated the volume of BME with ~10% error. The MC-based scatter corrections and the modified MBMD ray weights were essential to achieve such performance - the error without MC scatter corrections was >30%, whereas the uniformity of estimated VNCa images was 3x improved using the modified weights compared to the conventional weights.

Conclusions

The proposed DE decomposition framework was able to overcome challenges of high scatter and incomplete sampling to achieve BME detection on a CBCT system with axially-distributed x-ray sources.

Imaging of Bowel Ischemia: An Update, From the AJR Special Series on Emergency Radiology

Acute mesenteric ischemia is a life-threatening condition that results from abrupt reduction in or cessation of blood flow to the bowel. Characterized by nonspecific abdominal symptoms, mesenteric ischemia is infrequently encountered and commonly misdiagnosed, with potentially catastrophic consequences. Prompt clinical diagnosis and early implementation of therapeutic interventions are critical to improving patient outcomes. Because cross-sectional imaging plays a key role in the diagnosis of mesenteric ischemia, radiologists must be familiar with the varied imaging manifestations of intestinal ischemia. Thus, the objectives of this article are to review the various types and common causes of mesenteric ischemia and to describe its spectrum of multimodality imaging findings, with special attention to novel imaging techniques and emerging diagnoses.

Correlation between Pancreatic Duct Variation and Related Diseases: An Effective Method Observing the Dual-Energy CT with Low-keV Monoenergetic Images

PURPOSE

Pancreatic duct variation can affect the secretory function of the pancreas. We aimed to explore the pancreatic duct variation, observed using low-keV monoenergetic images [MEI (+)] of dual-energy CT (DECT), and its relationship with related diseases. We further sought to compare pancreatic duct imaging using low-keV MEI (+) of DECT and magnetic resonance cholangiopancreatography (MRCP).

MATERIALS AND METHODS

The DECT and MRCP images of 854 patients were evaluated retrospectively. The 808 patients' pancreatic duct types were classified according to the anatomy and the opening of the pancreatic ducts, and the correlation with related diseases was analyzed. The DECT and MRCP images of 852 patients were graded according to the sharpness of the pancreatic ducts for evaluation.

RESULTS

A higher prevalence of acute pancreatitis (AP), chronic pancreatitis (CP), and duodenal papillary carcinoma (DPC) was observed in the variant group. Of the 27 AP cases in the variant group, 9 patients (33.3%) were Type 3c. Additionally, Type 4a was significantly correlated with AP and CP (p < 0.05). Low-keV MEI (+) of DECT outperformed the MRCP images in the sharpness of the pancreatic ducts in 852 patients.

CONCLUSIONS

Pancreatic duct variation is associated with AP, CP, and DPC. Low-keV MEI (+) DECT is an effective method to observe the pancreatic duct system.

Pros and Cons of Dual-Energy CT Systems: "One Does Not Fit All"

Dual-energy computed tomography (DECT) uses different energy spectrum x-ray beams for differentiating materials with similar attenuation at a certain energy. Compared with single-energy CT, it provides images with better diagnostic performance and a potential reduction of contrast agent and radiation doses. There are different commercially available DECT technologies, with machines that may display two x-ray sources and two detectors, a single source capable of fast switching between two energy levels, a specialized detector capable of acquiring high- and low-energy data sets, and a filter splitting the beam into high- and low-energy beams at the output. Sequential acquisition at different tube voltages is an alternative approach. This narrative review describes the DECT technique using a Q&A format and visual representations. Physical concepts, parameters influencing image quality, postprocessing methods, applicability in daily routine workflow, and radiation considerations are discussed. Differences between scanners are described, regarding design, image quality variabilities, and their advantages and limitations. Additionally, current clinical applications are listed, and future perspectives for spectral CT imaging are addressed. Acknowledging the strengths and weaknesses of different DECT scanners is important, as these could be adapted to each patient, clinical scenario, and financial capability. This technology is undoubtedly valuable and will certainly keep improving.

Accurate Image Reconstruction in Dual-Energy CT with Limited-Angular-Range Data Using a Two-Step Method

Dual-energy CT (DECT) with scans over limited-angular ranges (LARs) may allow reductions in scan time and radiation dose and avoidance of possible collision between the moving parts of a scanner and the imaged object. The beam-hardening (BH) and LAR effects are two sources of image artifacts in DECT with LAR data. In this work, we investigate a two-step method to correct for both BH and LAR artifacts in order to yield accurate image reconstruction in DECT with LAR data. From low- and high-kVp LAR data in DECT, we first use a data-domain decomposition (DDD) algorithm to obtain LAR basis data with the non-linear BH effect corrected for. We then develop and tailor a directional-total-variation (DTV) algorithm to reconstruct from the LAR basis data obtained basis images with the LAR effect compensated for. Finally, using the basis images reconstructed, we create virtual monochromatic images (VMIs), and estimate physical quantities such as iodine concentrations and effective atomic numbers within the object imaged. We conduct numerical studies using two digital phantoms of different complexity levels and types of structures. LAR data of low- and high-kVp are generated from the phantoms over both single-arc (SA) and two-orthogonal-arc (TOA) LARs ranging from 14∘ to 180∘. Visual inspection and quantitative assessment of VMIs obtained reveal that the two-step method proposed can yield VMIs in which both BH and LAR artifacts are reduced, and estimation accuracy of physical quantities is improved. In addition, concerning SA and TOA scans with the same total LAR, the latter is shown to yield more accurate images and physical quantity estimations than the former. We investigate a two-step method that combines the DDD and DTV algorithms to correct for both BH and LAR artifacts in image reconstruction, yielding accurate VMIs and estimations of physical quantities, from low- and high-kVp LAR data in DECT. The results and knowledge acquired in the work on accurate image reconstruction in LAR DECT may give rise to further understanding and insights into the practical design of LAR scan configurations and reconstruction procedures for DECT applications.

A Health System's Response to the Ongoing Global Shortage of Iodinated Contrast Media

A production facility shutdown related to containment measures during the COVID-19 pandemic has resulted in a global shortage of iodinated contrast media. This article describes the strategies implemented at one large U.S. health system to maintain care continuity during the ongoing shortage. The strategies have included attempts to procure additional stock, repackage existing stock for use in larger numbers of patients, use noncontrast CT or alternative imaging modalities in place of contrast-enhanced CT, and collaborate with specialties outside of radiology to participate in conservation efforts. In addition, individual CT protocols underwent tailored modifications to use dual-energy technique and/or lower tube voltages, to allow lower contrast media doses with maintained visualization of tissue enhancement. The experiences during this period provide insights to facilitate long-term reductions in contrast media doses and ongoing CT protocol optimization after supplies return to normal levels. Critical throughout the efforts to mitigate the impact of the shortage have been system-level action, operational flexibility, and close communication by the health system's radiologists, technologists, physicists, pharmacists, and ordering providers.

Dual-Energy Computed Tomography Collagen Density Mapping of the Cranio-Cervical Ligaments-A Retrospective Feasibility Study

The objectives of this study were to investigate the mean collagen content of the atlanto-axial joint (AAJ) ligaments in a cohort without inflammatory disease and to analyze clinical confounders such as age, sex, and presence of ligamentous calcifications. A total of 153 patients who underwent dual-energy computed tomography (DECT) due to various reasons (e.g., suspected cancer or infection) were included in this retrospective study. Reconstruction of collagen density maps from the DECT dataset was performed. Region of interest (ROI) analysis was performed to assess densities in the following regions: ligamentum transversum atlantis (LTA), ligamenta alaria, fasciculi longitudinales, ligamentum nuchae, and retro-odontoid soft tissue (RDS). Osteoarthritis (OA) and the presence of calcifications were assessed by two experienced readers blinded to clinical data. Subgroup comparisons were performed using unpaired t-tests. The correlation of collagen density and clinical factors was investigated using Pearson's correlation coefficient. Mean LTA collagen density was 141.7 (SD 35.7). Ligamentous calcifications were rare (14.4 %). OA of the AAJ was common (91.5 %). LTA collagen density was not associated with age (Pearson's r of 0.109; p = 0.180) and was not significantly higher in patients with OA (p = 0.070). No correlations between RDS thickness, collagen density or calcifications were found. Our results show collagen density mapping of the cranio-cervical joint ligaments to be feasible; collagen densities are not significantly associated with age, sex, AAJ degeneration, or asymptomatic ligamentous calcification.

Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung

Objective

Based on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) - adenocarcinoma (ADC) and squamous cell carcinoma (SCC).

Methods

A total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients' pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility.

Results

A total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it's good calibration, and decision curve analysis (DCA) proved its clinical utility.

Conclusion

The combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.

Contrast thresholds for detection of various iodine concentrations in subtraction CT and dual-energy CT systems

OBJECTIVE

To estimate the minimum iodine concentrations detectable in simulated vessels of various diameters for both subtraction computed tomography (CT) and dual-energy CT systems.

METHODS

Fillable tubes (diameters: 1, 3, and 5 mm) were filled with a variety of iodine concentrations (range: 0-20 mg/ml), placed in the center of 28-mm cylindrical rods and surrounded with water. Rods with and without fillable tubes were placed in a 20-cm cylindrical solid-water phantom to simulate administration of iodine in blood vessels. The phantom was scanned with clinical subtraction CT (SCT) and dual-energy CT (DECT) head protocols to assess the detection of minimum iodine concentrations in both systems. The SCT and DECT images were evaluated quantitatively with a MATLAB script to extract regions of interest (ROIs) of each simulated vessel. ROI measurements were used to calculate the limit of detectability (LOD) and signal-to-noise ratio of Rose criteria for the assessment of the contrast thresholds.

RESULTS

Both SNR_Rose and LOD methods agreed and determined the minimum detectable iodine concentration to be 0.4 mg/ml in the 5-mm diameter vessel for SCT. However, the minimum detectable concentration in the 5-mm vessel with DECT was 1 mg/ml. The 3-mm vessel had a minimum detectable concentration of 0.8 mg/ml for SCT and 2 mg/ml for DECT. Lastly, the minimum detectable iodine concentration for the 1-mm vessel was 10 mg/ml for SCT and 10 mg/ml for DECT.

CONCLUSION

In this phantom study, SCT showed the capability to detect lower iodine concentrations compared to DECT. Contrast thresholds varied for vessels of different diameters and the smaller vessels required a higher iodine concentration for detection. Based on this knowledge, radiologists can modify their protocols to increase contrast enhancement.

Dual-Energy Computed Tomography-Based Iodine Concentration Estimation for Evaluating Choroidal Malignant Melanoma Response to Treatment: Optimization and Primary Validation

Contrast-enhanced imaging for choroidal malignant melanoma (CMM) is mostly limited to detecting metastatic tumors, possibly due to difficulties in fixing the eye position. We aimed to (1) validate the appropriateness of estimating iodine concentration based on dual-energy computed tomography (DECT) for CMM and optimize the calculation parameters for estimation, and (2) perform a primary clinical validation by assessing the ability of this technique to show changes in CMM after charged-particle radiation therapy. The accuracy of the optimized estimate (eIC_optimized) was compared to an estimate obtained by commercial software (eIC_commercial) by determining the difference from the ground truth. Then, eIC_optimized, tumor volume, and CT values (80 kVp, 140 kVp, and synthesized 120 kVp) were measured at pre-treatment and 3 months and 1.5−2 years after treatment. The difference from the ground truth was significantly smaller in eIC_optimized than in eIC_commercial (p < 0.01). Tumor volume, CT values, and eIC_optimized all decreased significantly at 1.5−2 years after treatment, but only eIC_commercial showed a significant reduction at 3 months after treatment (p < 0.01). eIC_optimized can quantify contrast enhancement in primary CMM lesions and has high sensitivity for detecting the response to charged-particle radiation therapy, making it potentially useful for treatment monitoring.