Dual-energy CT Imaging Research Articles

Unsupervised deep learning based image outpainting for dual-source, dual-energy computed tomography

Due to a physical constraint, dual-energy computed tomography (DECT) performed on a dual-source CT scanner has a restricted field of view (FOV) in one imaging chain (i.e. tube B, 140 kV with tin filter). This indicates that dual-energy analysis cannot be performed outside the limited FOV. As a result, the dual-source DECT scanner may not be beneficial for larger patients. To address this issue, we study the feasibility of using an unsupervised deep learning (DL) based method to outpaint the missing image outside the limited FOV. Brain DECT images acquired on the dual-source DECT scanner were used to simulate a restricted FOV scan. First, the whole brain of DECT images was shifted to the corner of the image. Then, the restricted 140-kV CT image was generated by multiplying the shifted 140-kV CT image with a small circular mask. As a result, we can evaluate the proposed DL-based method without scanning the patient twice. Moreover, the non-truncated 140-kV CT images can be considered as ground truth for comparison. Our results show that the proposed DL-based method can reconstruct missing data and produce 140-kV CT images that appear similar to the true 140-kV CT images. Moreover, the mean CT number differences between the true and DL-based 140-kV CT images for brain, muscle, fat and bone were less than 3 HU. We also observed that virtual monoenergetic images obtained from true and DL-based DECT images were visually similar. Our preliminary study shows the feasibility of using an unsupervised DL-based method to yield the out-of-field imaging data in dual-source DECT.

Assessment of thoracic disk herniation by using virtual noncalcium dual-energy CT in comparison with standard grayscale CT

ObjectivesTo determine the diagnostic accuracy of dual-energy CT (DECT) virtual noncalcium (VNCa) reconstructions for assessing thoracic disk herniation compared to standard grayscale CT.MethodsIn this retrospective study, 87 patients (1131 intervertebral disks; mean age, 66 years; 47 women) who underwent third-generation dual-source DECT and 3.0-T MRI within 3 weeks between November 2016 and April 2020 were included. Five blinded radiologists analyzed standard DECT and color-coded VNCa images after a time interval of 8 weeks for the presence and degree of thoracic disk herniation and spinal nerve root impingement. Consensus reading of independently evaluated MRI series served as the reference standard, assessed by two separate experienced readers. Additionally, image ratings were carried out by using 5-point Likert scales.ResultsMRI revealed a total of 133 herniated thoracic disks. Color-coded VNCa images yielded higher overall sensitivity (624/665 [94%; 95% CI, 0.89–0.96] vs 485/665 [73%; 95% CI, 0.67–0.80]), specificity (4775/4990 [96%; 95% CI, 0.90–0.98] vs 4066/4990 [82%; 95% CI, 0.79–0.84]), and accuracy (5399/5655 [96%; 95% CI, 0.93–0.98] vs 4551/5655 [81%; 95% CI, 0.74–0.86]) for the assessment of thoracic disk herniation compared to standard CT (all p < .001). Interrater agreement was excellent for VNCa and fair for standard CT (ϰ = 0.82 vs 0.37; p < .001). In addition, VNCa imaging achieved higher scores regarding diagnostic confidence, image quality, and noise compared to standard CT (all p < .001).ConclusionsColor-coded VNCa imaging yielded substantially higher diagnostic accuracy and confidence for assessing thoracic disk herniation compared to standard CT.Key Points• Color-coded VNCa reconstructions derived from third-generation dual-source dual-energy CT yielded significantly higher diagnostic accuracy for the assessment of thoracic disk herniation and spinal nerve root impingement compared to standard grayscale CT.• VNCa imaging provided higher diagnostic confidence and image quality at lower noise levels compared to standard grayscale CT.• Color-coded VNCa images may potentially serve as a viable imaging alternative to MRI under circumstances where MRI is unavailable or contraindicated.

Dual-Energy Computed Tomography Proton-Dose Calculation with Scripting and Modified Hounsfield Units.

PurposeTo describe an implementation of dual-energy computed tomography (DECT) for calculation of proton stopping-power ratios (SPRs) in a commercial treatment-planning system. The process for validation and the workflow for safe deployment of DECT is described, using single-energy computed tomography (SECT) as a safety check for DECT dose calculation.Materials and MethodsThe DECT images were acquired at 80 kVp and 140 kVp and were processed with computed tomography scanner software to derive the electron density and effective atomic number images. Reference SPRs of tissue-equivalent plugs from Gammex (Middleton, Wisconsin) and CIRS (Computerized Imaging Reference Systems, Norfolk, Virginia) electron density phantoms were used for validation and comparison of SECT versus DECT calculated through the Eclipse treatment planning system (Varian Medical Systems, Palo Alto, California) application programming interface scripting tool. An in-house software was also used to create DECT SPR computed tomography images for comparison with the script output. In the workflow, using the Eclipse system application programming interface script, clinical plans were optimized with the SECT image set and then forward-calculated with the DECT SPR for the final dose distribution. In a second workflow, the plans were optimized using DECT SPR with reduced range-uncertainty margins.ResultsFor the Gammex phantom, the root mean square error in SPR was 1.08% for DECT versus 2.29% for SECT for 10 tissue-surrogates, excluding the lung. For the CIRS Phantom, the corresponding results were 0.74% and 2.27%. When evaluating the head and neck plan, DECT optimization with 2% range-uncertainty margins achieved a small reduction in organ-at-risk doses compared with that of SECT plans with 3.5% range-uncertainty margins. For the liver case, DECT was used to identify and correct the lipiodol SPR in the SECT plan.ConclusionIt is feasible to use DECT for proton-dose calculation in a commercial treatment planning system in a safe manner. The range margins can be reduced to 2% in some sites, including the head and neck.

The Role of Dual-Energy Computed Tomography in Locating Gastrointestinal Tract Perforations.

ObjectiveWith each passing day, dual-energy computed tomography (DECT) is being used more frequently in the evaluation of abdominal pathologies. In this article, we aimed to assess the role of dual-energy CT in locating gastrointestinal perforations, which are among the causes of acute abdomen.Materials and methodsAll patients who underwent DECT due to acute abdomen in a COVID-19 designated hospital between June 1st, 2020 and December 31st, 2020, who were found to have gastrointestinal tract (GIT) perforation and underwent surgery were included in the study. DECT results and intraoperative findings of the patients were compared.ResultsThirteen patients (nine males and four females) who underwent DECT for acute abdomen and were diagnosed with perforation in the gastrointestinal system were included in the study. The mean age of the patients was 57.6 years (range: 11-85 years). Two patients had gastric perforation, three had duodenal perforations, and one patient had a perforation in the gallbladder wall. Two patients were diagnosed with jejunal perforations, one patient with Meckel's diverticulum, and three patients with colorectal perforation. Although free air was detected in the abdomen of one patient, perforation could not be located. In patients with GIT perforation who were operated on following DECT imaging, the perforation location shown on DECT correlated 100% with the perforation locations detected during surgery.ConclusionDECT is significantly effective in planning surgical treatment and determining the foci of perforation in GIT perforations.

Multimaterial decomposition algorithm for quantification of fat in hepatocellular carcinoma using rapid kilovoltage-switching dual-energy CT: A comparison with chemical-shift MR imaging.

Understanding intratumoral fat in hepatocellular carcinoma (HCC) is clinically important to elucidate prognosis. We sought to quantify HCC and liver fat with a multimaterial decomposition (MMD) algorithm with rapid kilovoltage-switching dual-energy computed tomography (DECT) relative to chemical-shift magnetic resonance imaging (CSI).In this retrospective study, 40 consecutive patients with HCC underwent non-contrast-enhanced (non-CE) and four-phases contrast-enhanced (four-CE) DECT (80 and 140 kVp) and abdominal MR imaging (including CSI) between April 2011 and December 2012. Fat volume fraction (FVFDECT) maps were generated by MMD algorithm to quantify HCC and liver fat. Fat fraction measured by CSI (FFCSI) was determined for HCC and liver on dual-echo sequence using 1.5- or 3-Tesla MR systems. The correlation between FVFDECT and FFCSI was evaluated using Pearson correlation test, while non-CE FVFDECT and four-CE FVFDECT were compared by one-way ANOVA and Bland–Altman analysis.Forty patients (mean age, 70.1 years ± 7.8; 25 males) were evaluated. FVFDECT and FFCSI exhibited weak to moderate correlations for HCC in non-CE and four-CE except in equilibrium phase (r = 0.42, 0.44, 0.35, and 0.33; all P < .05), and very strong correlations for liver in all phases (r = 0.86, 0.83, 0.85, 0.87, and 0.84; all P < .05). Those correlation coefficients were significantly higher for liver for each phase (all P < .05). FVFDECT did not differ significantly across scan phases regarding HCC or liver (P = .076 and 0.56). Bland–Altman analysis showed fixed bias in all phases between non- and four-CE FVFDECT in HCC and liver.As compared with liver, correlations between FVF measured by DECT-based MMD and FF measured by CSI were weak in HCC in all phases. FVF is reproducible across all scan phases in HCC and liver. The MMD algorithm requires modification for HCC fat quantification given the heterogeneous components of HCC.

Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks

Organ delineation is crucial to diagnosis and therapy, while it is also labor-intensive and observer-dependent. Dual energy CT (DECT) provides additional image contrast than conventional single energy CT (SECT), which may facilitate automatic organ segmentation. This work aims to develop an automatic multi-organ segmentation approach using deep learning for head-and-neck region on DECT. We proposed a mask scoring regional convolutional neural network (R-CNN) where comprehensive features are firstly learnt from two independent pyramid networks and are then combined via deep attention strategy to highlight the informative ones extracted from both two channels of low and high energy CT. To perform multi-organ segmentation and avoid misclassification, a mask scoring subnetwork was integrated into the Mask R-CNN framework to build the correlation between the class of potential detected organ’s region-of-interest (ROI) and the shape of that organ’s segmentation within that ROI. We evaluated our model on DECT images from 127 head-and-neck cancer patients (66 training, 61 testing) with manual contours of 19 organs as training target and ground truth. For large- and mid-sized organs such as brain and parotid, the proposed method successfully achieved average Dice similarity coefficient (DSC) larger than 0.8. For small-sized organs with very low contrast such as chiasm, cochlea, lens and optic nerves, the DSCs ranged between around 0.5 and 0.8. With the proposed method, using DECT images outperforms using SECT in almost all 19 organs with statistical significance in DSC (p<0.05). Meanwhile, by using the DECT, the proposed method is also significantly superior to a recently developed FCN-based method in most of organs in terms of DSC and the 95th percentile Hausdorff distance. Quantitative results demonstrated the feasibility of the proposed method, the superiority of using DECT to SECT, and the advantage of the proposed R-CNN over FCN on the head-and-neck patient study. The proposed method has the potential to facilitate the current head-and-neck cancer radiation therapy workflow in treatment planning.

A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks.

Dual-energy computed tomography (DECT) is highly promising for material characterization and identification, whereas reconstructed material-specific images are affected by magnified noise and beam-hardening artifacts. Although various DECT material decomposition methods have been proposed to solve this problem, the quality of the decomposed images is still unsatisfactory, particularly in the image edges. In this study, a data-driven approach using dual interactive Wasserstein generative adversarial networks (DIWGAN) is developed to improve DECT decomposition accuracy and perform edge-preserving images. In proposed DIWGAN, two interactive generators are used to synthesize decomposed images of two basis materials by modeling the spatial and spectral correlations from input DECT reconstructed images, and the corresponding discriminators are employed to distinguish the difference between the generated images and labels. The DECT images reconstructed from high- and low-energy bins are sent to two generators separately, and each generator synthesizes one material-specific image, thereby ensuring the specificity of the network modeling. In addition, the information from different energy bins is exploited through the feature sharing of two generators. During decomposition model training, a hybrid loss function including L1 loss, edge loss, and adversarial loss is incorporated to preserve the texture and edges in the generated images. Additionally, a selector is employed to define the generator that should be trained in each iteration, which can ensure the modeling ability of two different generators and improve the material decomposition accuracy. The performance of the proposed method is evaluated using digital phantom, XCAT phantom, and real data from a mouse. On the digital phantom, the regions of bone and soft tissue are strictly and accurately separated using the trained decomposition model. The material densities in different bone and soft-tissue regions are near the ground truth, and the error of material densities is lower than 3mg/ml. The results from XCAT phantom show that the material-specific images generated by directed matrix inversion and iterative decomposition methods have severe noise and artifacts. Regarding to the learning-based methods, the decomposed images of fully convolutional network (FCN) and butterfly network (Butterfly-Net) still contain varying degrees of artifacts, while proposed DIWGAN can yield high quality images. Compared to Butterfly-Net, the root-mean-square error (RMSE) of soft-tissue images generated by the DIWGAN decreased by 0.01g/ml, whereas the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the soft-tissue images reached 31.43dB and 0.9987, respectively. The mass densities of the decomposed materials are nearest to the ground truth when using the DIWGAN method. The noise standard deviation of the decomposition images reduced by 69%, 60%, 33%, and 21% compared with direct matrix inversion, iterative decomposition, FCN, and Butterfly-Net, respectively. Furthermore, the performance of the mouse data indicates the potential of the proposed material decomposition method in real scanned data. A DECT material decomposition method based on deep learning is proposed, and the relationship between reconstructed and material-specific images is mapped by training the DIWGAN model. Results from both the simulation phantoms and real data demonstrate the advantages of this method in suppressing noise and beam-hardening artifacts.

Split-filter dual-energy CT pulmonary angiography for the diagnosis of acute pulmonary embolism: a study on image quality and radiation dose.

Computed tomography (CT) pulmonary angiography is the diagnostic reference standard in suspected pulmonary embolism (PE). Favorable results for dual-energy CT (DECT) images have been reported for this condition. Nowadays, dual-energy data acquisition is feasible with different technical options, including a single-source split-filter approach. Therefore, the aim of this retrospective study was to investigate image quality and radiation dose of thoracic split-filter DECT in comparison to conventional single-energy CT in patients with suspected PE. A total of 110 CT pulmonary angiographies were accomplished either as standard single-energy CT with automatic tube voltage selection (ATVS) (n=58), or as split-filter DECT (n=52). Objective [pulmonary artery CT attenuation, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)] and subjective image quality [four-point Likert scale; three readers (R)] were compared among the two study groups. Size-specific dose estimates (SSDE), dose-length-product (DLP) and volume CT dose index (CTDIvol) were assessed for radiation dose analysis. Split-filter DECT images yielded 67.7% higher SNR (27.0 vs. 16.1; P<0.001) and 61.9% higher CNR (22.5 vs. 13.9; P<0.001) over conventional single-energy images, whereas CT attenuation was significantly lower (344.5 vs. 428.2 HU; P=0.013). Subjective image quality was rated good or excellent in 93.0%/98.3%/77.6% (R1/R2/R3) of the single-energy CT scans, and 84.6%/82.7%/80.8% (R1/R2/R3) of the split-filter DECT scans. SSDE, DLP and CTDIvol were significantly lower for conventional single-energy CT compared to split-filter DECT (all P<0.05), which was associated with 26.7% higher SSDE. In the diagnostic workup of acute PE, the split-filter allows for dual-energy data acquisition from single-source single-layer CT scanners. The existing opportunity to assess pulmonary "perfusion" based on analysis of iodine distribution maps is associated with higher radiation dose in terms of increased SSDE than conventional single-energy CT with ATVS. Moreover, a proportion of up to 3.8% non-diagnostic examinations in the current reference standard test for PE is not negligible.

Influence of beam hardening in dual-energy CT imaging: phantom study for iodine mapping, virtual monoenergetic imaging, and virtual non-contrast imaging

In this study, we investigated the influence of beam hardening on the dual-energy computed tomography (DECT) values of iodine maps, virtual monoenergetic (VME) images, and virtual non-contrast (VNC) images. 320-row DECT imaging was performed by changing the x-ray tube energy for the first and second rotations. DECT values of 5 mg/mL iodine of the multi-energy CT phantom were compared with and without a 2-mm-thick attenuation rubber layer (~700 HU) wound around the phantom. It was found that the CT density values UH, with/without the rubber layer had statistical differences in the iodine map (184 ± 0.7 versus 186 ± 1.8), VME images (125 ± 0.3 versus 110 ± 0.4), and VNC images (−58 ± 0.7 versus −76 ± 1.7) (p < 0.010 for all). This suggests that iodine mapping may be underestimated by DECT and overestimated by VME imaging because of x-ray beam hardening. The use of VNC images instead of plain CT images requires further investigation because of underestimation.

A New Outlook on the Ability to Accumulate an Iodine Contrast Agent in Solid Lung Tumors Based on Virtual Monochromatic Images in Dual Energy Computed Tomography (DECT): Analysis in Two Phases of Contrast Enhancement.

For some time, dual energy computed tomography (DECT) has been an established method used in a vast array of clinical applications, including lung nodule assessment. The aim of this study was to analyze (using monochromatic DECT images) how the X-ray absorption of solitary pulmonary nodules (SPNs) depends on the iodine contrast agent and when X-ray absorption is no longer dependent on the accumulated contrast agent. Sixty-six patients with diagnosed solid lung tumors underwent DECT scans in the late arterial phase (AP) and venous phase (VP) between January 2017 and June 2018. Statistically significant correlations (p ≤ 0.001) of the iodine contrast concentration were found in the energy range of 40–90 keV in the AP phase and in the range of 40–80 keV in the VP phase. The strongest correlation was found between the concentrations of the contrast agent and the scanning energy of 40 keV. At the higher scanning energy, no significant correlations were found. We concluded that it is most useful to evaluate lung lesions in DECT virtual monochromatic images (VMIs) in the energy range of 40–80 keV. We recommend assessing SPNs in only one phase of contrast enhancement to reduce the absorbed radiation dose.

DECT in Detection of Vertebral Fracture-associated Bone Marrow Edema: A Systematic Review and Meta-Analysis with Emphasis on Technical and Imaging Interpretation Parameters.

Background Dual-energy CT (DECT) shows promising performance in detecting bone marrow edema (BME) associated with vertebral body fractures. However, the optimal technical and image interpretation parameters are not well described. Purpose To conduct a systematic review and meta-analysis to determine the diagnostic performance of DECT in detecting BME associated with vertebral fractures (VFs), using different technical and image interpretation parameters, compared with MRI as the reference standard. Materials and Methods A systematic literature search was performed on July 9, 2020, to identify studies evaluating DECT performance for in vivo detection of vertebral BME. A random-effects model was used to derive estimates of the diagnostic accuracy parameters of DECT. The impact of relevant covariates in technical, image interpretation, and study design parameters on the diagnostic performance of DECT was investigated using subgroup analyses. Results Seventeen studies (with 742 of 2468 vertebrae with BME at MRI) met inclusion criteria. Pooled estimates of sensitivity, specificity, and area under the curve of DECT for vertebral body BME were 89% (95% CI: 84%, 92%), 96% (95% CI: 92%, 98%), and 96% (95% CI: 94%, 97%), respectively. Single-source consecutive scanning showed poor specificity (78%) compared with the dual-source technique (98%, P < .001). Specificity was higher using bone and soft-tissue kernels (98%) compared with using only soft-tissue kernels (90%, P = .001). Qualitative assessment had a better specificity (97%) versus quantitative assessment (90%) of DECT images (P = .01). Experienced readers showed considerably higher specificity (96%) compared with trainees (79%, P = .01). DECT sensitivity improved using a higher difference between low- and high-energy spectra (90% vs 83%, P = .04). Conclusion Given its high specificity, the detection of vertebral bone marrow edema with dual-energy CT (DECT) associated with vertebral fracture may obviate confirmatory MRI in an emergency setting. Technical parameters, such as the dual-source technique, both bone and soft-tissue kernels, and qualitative assessment by experienced readers, can ensure the high specificity of DECT. © RSNA, 2021 Online supplemental material is available for this article.

Detection of bone marrow edema in osteonecrosis of the femoral head using virtual noncalcium dual-energy computed tomography

PurposeTo determine the diagnostic performance of virtual noncalcium (VNCa) dual-energy computed tomography (DECT) in the detection of bone marrow edema (BME) in participants with osteonecrosis of the femoral head (ONFH). MethodsIn this prospective study, 24 consecutive participants (15 men, 9 women; mean age, 44 years, range, 21–72 years) diagnosed with ONFH who underwent DECT and magnetic resonance imaging (MRI) between September 2019 and January 2020 were involved. Two independent readers visually evaluated color-coded VNCa images using a binary classification (0 = normal bone marrow, 1 = BME). MRI served as the reference standard for the presence of BME. Interobserver agreement for the visual evaluation of VNCa DECT images was calculated with κ statistics. We determined computed tomography (CT) numbers on VNCa images and weighted-average CT sets using region-of-interest-based quantitative analysis. The t-test was used to compare the differences of CT values between BME areas and normal bone marrow areas. Receiver operating characteristic (ROC) curve was used to select an optimal CT values of VNCa images for detecting BME. A p value of <0.05 was considered as statistically significant. ResultsThe sensitivity, specificity, and accuracy of Reader 1 and Reader 2, respectively, in the identification of BME at DECT were 95 % and 89 % (18 and 17 of 19), 96 % and 96 % (25 and 25 of 26), and 93 % (43 and 42 of 45). Interobserver agreement was excellent (κ = 0.86). The VNCa CT numbers of the BME area and the normal bone marrow area were −28.6 (−17.9–−39.4) HU and −97.9 (−91.3–−104.4) HU, respectively, with statistical significance (t = −10.6, p < 0.001). The weighted-average CT numbers of the BME area and the normal bone marrow area were 152.4(122.2–182.7) HU and 121.1(103.6–183.6) HU, respectively, with no statistical significance (t = −2.0, p > 0.05). The area under the receiver operating characteristic curve was 0.99 in differentiation of the BME from normal bone marrow. A cut-off value of −57.2 HU yielded overall sensitivity, specificity, and accuracy, respectively, of 95 % (18 of 19), 100 % (26 of 26), and 98 % (44 of 45) detection of BME in participants with ONFH. ConclusionVisual and quantitative analyses of VNCa images shows excellent diagnostic performance for assessing BME in participants with ONFH.

Skeletal muscle fat quantification by dual-energy computed tomography in comparison with 3T MR imaging

ObjectivesTo quantify the proportion of fat within the skeletal muscle as a measure of muscle quality using dual-energy CT (DECT) and to validate this methodology with MRI.MethodsTwenty-one patients with abdominal contrast-enhanced DECT scans (100 kV/Sn 150 kV) underwent abdominal 3-T MRI. The fat fraction (DECT-FF), determined by material decomposition, and HU values on virtual non-contrast-enhanced (VNC) DECT images were measured in 126 regions of interest (≥ 6 cm2) within the posterior paraspinal muscle. For validation, the MR-based fat fraction (MR-FF) was assessed by chemical shift relaxometry. Patients were categorized into groups of high or low skeletal muscle mean radiation attenuation (SMRA) and classified as either sarcopenic or non-sarcopenic, according to the skeletal muscle index (SMI) and cut-off values from non-contrast-enhanced single-energy CT. Spearman’s and intraclass correlation, Bland-Altman analysis, and mixed linear models were employed.ResultsThe correlation was excellent between DECT-FF and MR-FF (r = 0.91), DECT VNC HU and MR-FF (r = - 0.90), and DECT-FF and DECT VNC HU (r = − 0.98). Intraclass correlation between DECT-FF and MR-FF was good (r = 0.83 [95% CI 0.71–0.90]), with a mean difference of - 0.15% (SD 3.32 [95% CI 6.35 to − 6.66]). Categorization using the SMRA yielded an eightfold difference in DECT VNC HU values between both groups (5 HU [95% CI 23–11], 42 HU [95% CI 33–56], p = 0.05). No significant relationship between DECT-FF and SMI-based classifications was observed.ConclusionsFat quantification within the skeletal muscle using DECT is both feasible and reliable. DECT muscle analysis offers a new approach to determine muscle quality, which is important for the diagnosis and therapeutic monitoring of sarcopenia, as a comorbidity associated with poor clinical outcome.Key Points• Dual-energy CT (DECT) material decomposition and virtual non-contrast-enhanced DECT HU values assess muscle fat reliably.• Virtual non-contrast-enhanced dual-energy CT HU values allow to differentiate between high and low native skeletal muscle mean radiation attenuation in contrast-enhanced DECT scans.• Measuring muscle fat by dual-energy computed tomography is a new approach for the determination of muscle quality, an important parameter for the diagnostic confirmation of sarcopenia as a comorbidity associated with poor clinical outcome.

A quantitative assessment of dual energy computed tomography-based material decomposition for imaging bone marrow edema associated with acute knee injury.

This study developed methods to quantify and improve the accuracy of dual-energy CT (DECT)-based bone marrow edema imaging using a clinical CT system. Objectives were: (a) to quantitatively compare DECT with gold-standard, fluid-sensitive MRI for imaging of edema-like marrow signal intensity (EMSI) and (b) to identify image analysis parameters that improve delineation of EMSI associated with acute knee injury on DECT images. DECT images from ten participants with acute knee injury were decomposed into estimated fractions of bone, healthy marrow, and edema based on energy-dependent differences in tissue attenuation. Fluid-sensitive MR images were registered to DECT for quantitative, voxel-by-voxel comparison between the two modalities. An optimization scheme was developed to find attenuation coefficients for healthy marrow and edema that improved EMSI delineation, compared to MRI. DECT method accuracy was evaluated by measuring dice coefficients, mutual information, and normalized cross correlation between the DECT result and registered MRI. When applying the optimized three-material decomposition method, dice coefficients for EMSI identified through DECT vs MRI were 0.32 at the tibia and 0.13 at the femur. Optimization of attenuation coefficients improved dice coefficient, mutual information, and cross-correlation between DECT and gold-standard MRI by 48%-107% compared to three-material decomposition using non-optimized parameters, and improved mutual information and cross-correlation by 39%-58% compared to the manufacturer-provided two-material decomposition. This study quantitatively evaluated the performance of DECT in imaging knee injury-associated EMSI and identified a method to optimize DECT-based visualization of complex tissues (marrow and edema) whose attenuation parameters cannot be easily characterized. Further studies are needed to improve DECT-based EMSI imaging at the femur.

Observation of targeted gold nanoparticles in nasopharyngeal tumour nude mice model through dual-energy computed tomography.

This study was performed to specify the efficiency of imaging nanoparticle concentration as contrast media in dual‐energy computed tomography (DECT). Gold nanoparticles (AuNPs) and gold nanoparticles‐conjugated folic acid through cysteamine (FA‐Cya‐AuNPs) were both considered as contrast agents. Characterization of NPs was performed using Dynamic Light Scattering (DLS) and zeta potential. The hemocompatibility of NPs was confirmed by different blood parameters such as white blood cell, red cell distribution width, hemoglobin, lymphocytes counts and haemolysis assay. DECT algorithm was confirmed using calibration phantom at different concentrations of NPs and tube potentials (80 and 140 kVp). Then, DECT was used to quantify the concentration of both AuNPs and FA‐Cys‐AuNPs in human nasopharyngeal cancer cells. Mice were injected with non‐targeted AuNPs and targeted AuNps at a concentration of 3 × 103 μg/ml. Then, they were scanned with different tube potentials. The concentration of nanoparticles in the various organs of nude mice was measured through DECT imaging and inductively coupled plasma mass spectrometry (ICP‐MS) analysis. The results of DECT images were compared with ICP‐MS analysis and indicated that they were approximately similar. In sum, FA‐Cys‐AuNPs can be a proper candidate for targeted contrast media in DECT molecular scanning of human nasopharyngeal tumours.

Prediction of ipsilateral lateral cervical lymph node metastasis in papillary thyroid carcinoma: a combined dual-energy CT and thyroid function indicators study

BackgroundPredicting the possibility of ipsilateral lateral cervical lymph node metastasis (ipsi-LLNM) was crucial to the operation plan for patients with papillary thyroid carcinoma (PTC). This study aimed to investigate the independent risk factors for ipsi-LLNM in PTC patients by combining dual-energy computed tomography (DECT) with thyroid function indicators.MethodsWe retrospectively enrolled 406 patients with a pathological diagnosis of PTC from Jan 2016 to Dec 2019. Ensure the DECT images were clear and the thyroid function indicators were complete. Univariate and multivariate logistic analyses explored the independent risk factors for ipsi-LLNM. To evaluate the cutoff value of each risk factor by using receiver operating characteristic (ROC) curves.ResultsA total of 406 patients with PTC were analyzed, including 128 with ipsi-LLNM and 278 without ipsi-LLNM. There were statistical differences of parameters between the two groups (P < .0001), including serum Tg, Anti-Tg, Anti-TPO, the volume of the primary lesion, calcification, extrathyroidal extension (ETE), and iodine concentration (IC) in the arterial and the venous phases. Independent risk factors for ipsi-LLNM included serum Tg, Anti-Tg, ETE, and IC in the arterial and the venous phases (P < .05). The combined application of the above independent risk factors can predict the possibility of ipsi-LLNM, with an AUC of 0.834. Ipsi-LLNM was more likely to occur when the following conditions were met: with ETE, Tg > 100.01 ng/mL, Anti-Tg > 89.43 IU/mL, IC in arterial phase > 3.4 mg/mL and IC in venous phase > 3.1 mg/mL.ConclusionsThe combined application of DECT quantitative parameters and thyroid function indicators can help clinicians accurately predict ipsi-LLNM before surgery, thereby assisting the individualized formulation of surgical procedures.

Dual-Energy Computed Tomography Virtual Noncalcium Imaging for the Detection of Acute Bone Marrow Edema in Vertebrae: Qualitative and Quantitative Analysis

Purpose: To evaluate the feasibility of dual-energy computed tomography (DECT) virtual noncalcium (VNCa) images for detecting vertebral bone marrow edema (BME), both in qualitative and quantitative analyses. Materials and Methods: Nineteen cases with acute spinal trauma were enrolled. DECT and MR Imaging were performed over an interval of 0–3 days within 3 weeks of injury. Both gray-scale and color-coded VNCa images were evaluated in an independent and blind manner using a three-scale grading system (2 = distinct BME, 1 = suspicious BME, and 0 = no edema) by two radiologists. CT value of bone marrow were measured for each region and subjected to receiver operating characteristic (ROC) curve analysis for diagnosis of BME on VNCa images from DECT. Subsequently, as a standard of reference, MR imaging was evaluated by a third radiologist for the presence of traumatic BME. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the detection of BME using VNCa images were evaluated. Kappa analysis and one-way ANOVA were performed to evaluate the consistency and variance of VNCa images in the BME. Results: For two observers, the sensitivity of VNCa images for detecting BME (grade 2) were 97.8% and 96.7%, with the specificity 76% and 72%, the PPV 93.8% and 92.7%, the NPV 90.5% and 85.7%, respectively. And the sensitivity for detecting BME (grade 1–2) were 94.3% and 94.3%, with the specificity 96.7% and 93.3%, the PPV 98.8% and 97.6%, the NPV 85.3% and 84.8%, respectively. The consistency test between the two observers showed that Kappa value was 0.650 (P &lt; 0.001). The VNCa images had higher CT value in the positive regions than in the negative regions (P &lt; 0.05). Statistically significant differences in the CT value were identified in different regions with the three-scale grading system (thoracic levels: F = 136.690, P &lt; 0.001; lumbar levels: F = 92.689, P &lt; 0.001). Conclusions: Acute traumatic BME in the vertebrae can be detected on VNCa images in both qualitative and quantitative analyses. DECT VNCa images are expected to serve as an additional method for the evaluation of BME.

Abstract P327: Utilizing Iodine Contrast Extravasation Post Reperfusion and Dual Energy Ct Imaging to Predict Risk of Hemorrhagic Conversion in Acute Stroke Patients

Introduction: Hyperintense acute reperfusion marker (HARM) has been identified on post-contrast magnetic resonance imaging (MRI) to be a marker of hemorrhagic conversion (HC) post reperfusion therapy in acute stroke patients. We have previously described a case where MRI HARM was mimicked on post contrast computed topography (CT) imaging in an acute stroke patient post reperfusion. Dual-Energy (DECT) allows for differentiation between acute blood and iodine contrast extravasation (ICE), and thus can have utility when ICE is present. Here we sought to validate whether post-intervention ICE/CT hyperdensity reperfusion maker (CT HARM), and contrast subtracted on DECT is associated with HC in acute stroke patients. Method: Data was obtained from our Institutional Review Board approved stroke admission database from January 2017 to November 2019, including ischemic stroke patients that received thrombolysis or thrombectomy, had evaluable images within 24 hours of admission, and received a DECT. Ischemic volumes of the stroke was measured on diffusion-weighted image (DWI). ICE was measured on CT head and DECT using the freehand 3D region of interest tool on the Visage Imaging PACS System. Susceptibility weighted MRI sequences were used to grade HC. Data analysis was conducted with regression modeling. Results: A total of 82 patients were included, 49% women, median age 73 (interquartile range (IQR), 61- 77), admission NIHSS 12 (IQR, 7 - 21), 24 hour change in NIHSS 4 (IQR, 0 -13), glucose 125 (IQR, 106 -158), creatinine 1.0 (IQR, 0.8 - 1.2), infarct volume 50.6 ± 7.1 mL, 48% treated with thrombectomy, 7% with PH-1 or PH-2 identified on MRI, and 56% with MCA infarcts. ICE volume was 2.6 ± 1.0 mL and DECT volume was 2.2 ± 1.1mL. ICE increased the likelihood of MRI confirmed PH-1 or PH-2 hemorrhagic conversion (odds ratio (OR) 14.34, 95% confidence interval (CI) 5.74 - 22.94) and decreased likelihood of increase in NIHSS at 24 hours (OR 0.20, 95% CI 0.01 to 0.40). There were no other significant associations with ICE or DECT volumes. Conclusion: Our results are supportive of our proposed association between CT HARM and risk of HC. More studies are needed to study whether quantitative of DECT can be predictive of stroke outcomes post reperfusion therapy.

Recognizing and Minimizing Artifacts at Dual-Energy CT.

Dual-energy CT (DECT) is an exciting innovation in CT technology with profound capabilities to improve diagnosis and add value to patient care. Significant advances in this technology over the past decade have improved our ability to successfully adopt DECT into the clinical routine. To enable effective use of DECT, one must be aware of the pitfalls and artifacts related to this technology. Understanding the underlying technical basis of artifacts and the strategies to mitigate them requires optimization of scan protocols and parameters. The ability of radiologists and technologists to anticipate their occurrence and provide recommendations for proper selection of patients, intravenous and oral contrast media, and scan acquisition parameters is key to obtaining good-quality DECT images. In addition, choosing appropriate reconstruction algorithms such as image kernel, postprocessing parameters, and appropriate display settings is critical for preventing quantitative and qualitative interpretive errors. Therefore, knowledge of the appearances of these artifacts is essential to prevent errors and allows maximization of the potential of DECT. In this review article, the authors aim to provide a comprehensive and practical overview of possible artifacts that may be encountered at DECT across all currently available commercial clinical platforms. They also provide a pictorial overview of the diagnostic pitfalls and outline strategies for mitigating or preventing the occurrence of artifacts, when possible. The broadening scope of DECT applications necessitates up-to-date familiarity with these technologies to realize their full diagnostic potential.

Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network.

Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT imaging from fully-sampled low-energy data together with single-view high-energy data. We demonstrate the feasibility of the approach with two independent cohorts (the first cohort including contrast-enhanced DECT scans of 5753 image slices from 22 patients and the second cohort including spectral CT scans without contrast injection of 2463 image slices from other 22 patients) and show its superior performance on DECT applications. The deep-learning-based approach could be useful to further significantly reduce the radiation dose of current premium DECT scanners and has the potential to simplify the hardware of DECT imaging systems and to enable DECT imaging using standard SECT scanners.