Dual-energy CT Imaging Research Articles

Usefulness of advanced monoenergetic reconstruction technique in dual-energy computed tomography for detecting bladder cancer

PurposeDetecting bladder cancer (BC) in routine CT images is important but is sometimes difficult when cancer is small. We evaluated the ability of 40-keV advanced monoenergetic images to depict BC.Materials and methodsFifty-two patients with a median age of 74 years (range 45–92) who were diagnosed as BC with transurethral resection or cystectomy, were included. They were examined with contrast-enhanced dual-energy CT (DE-CT) and advanced virtual monoenergetic images (40 keV) were reconstructed. For evaluating depictability of BC on 40-keV or virtual-120-kVp images, the difference in CT number between the cancer and bladder wall (BC–BW value) were calculated. We also subjectively assessed depictability of BC in virtual-120-kVp and 40-keV images using a 4-grade Likert scale (3: clear, 0: not visualized).ResultsIn 42 of 52 patients, BC–BW values could be calculated because BC was detected on CT images. The mean BC–BW value at 40 keV was significantly higher than that of virtual 120 kVp [80.5 ± 54 (SD) vs. 11.4 ± 12.5 HU, P < 0.01]. Average scores of subjective evaluations in the virtual-120-kVp and 40-keV images were 1.7 ± 1.2 and 2.1 ± 1.2, respectively (P < 0.001).ConclusionThe advanced monoenergetic reconstruction technique reconstructed using DE-CT image is useful to depict BC.

Exploring the best monochromatic energy level in dual energy spectral imaging for coronary stents after percutaneous coronary intervention

In this study, the optimal monochromatic energy level in dual-energy spectral CT required for imaging coronary stents after percutaneous coronary intervention (PCI) was explored. Thirty-five consecutive patients after PCI were examined using the dual-energy spectral CT imaging mode. The original images were reconstructed at 40–140 keV (10-keV interval) monochromatic levels. The in-stent and out-stent CT values at each monochromatic level were measured to calculate the signal-to-noise ratio(SNR) and contrast-to-noise ratio (CNR) for the vessel and the CT value difference between the in-stent and out-stent lumen (dCT (in–out)), which reflects the artificial CT number increase due to the beam hardening effect caused by the stents. The subjective image quality of the stent and in-stent vessel was evaluated by two radiologists using a 5-point scale. With the increase in energy level, the CT value, SNR, CNR, and dCT (in–out) all decreased. At 80 keV, the mean CT value in-stent reached (345.24 ± 93.43) HU and dCT (in–out) started plateauing. In addition, the subjective image quality of the stents and vessels peaked at 80 keV. The 80 keV monochromatic images are optimal for imaging cardiac patients with stents after PCI, balancing the enhancement and SNR and CNR in the vessels while minimizing the beam hardening artifacts caused by the stents.

MP61-02 DIFFERENTIATION OF RENAL MASS HISTOLOGY AND GRADE UTILIZING DUAL-ENERGY COMPUTED TOMOGRAPHY

You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance III (MP61)1 Sep 2021MP61-02 DIFFERENTIATION OF RENAL MASS HISTOLOGY AND GRADE UTILIZING DUAL-ENERGY COMPUTED TOMOGRAPHY Rishi Sekar, Giuseppe Toia, Achille Mileto, and George Schade Rishi SekarRishi Sekar More articles by this author , Giuseppe ToiaGiuseppe Toia More articles by this author , Achille MiletoAchille Mileto More articles by this author , and George SchadeGeorge Schade More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002101.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Dual-Energy Computed Tomography (DECT) has been shown to more accurately characterize renal mass vascularity, however its ability to differentiate tumor subtypes remains unclear. Herein, we investigate the role of DECT and iodine concentration as a derived imaging biomarker in predicting tumor histology and Fuhrman Nuclear Grade (FNG). METHODS: Our institutional database was queried for patients who underwent contrast enhanced DECT to characterize renal masses with subsequent pathologic diagnosis within our institution from 2018 to present. Tumor histology and FNG were obtained from internal pathologic evaluation. Tumor enhancement was measured in Hounsfield Units (HU) using standard techniques. Tumor iodine concentration (mg/mL) was calculated from the maximally enhancing region within each tumor in the nephrogenic phase. Mean enhancement (ME) and mean iodine concentration (MIC) were stratified by histology, and across FNG for clear cell renal cell carcinoma (ccRCC) and compared between groups using Student’s t-test. RESULTS: Our study cohort consisted of 39 separate renal solid enhancing masses. Diagnosis was ccRCC in 22 (56.4%), non-ccRCC in 12 (30.8%), and oncocytoma in 5 (12.8%). Among RCCs, both ME and MIC were statistically significantly higher for ccRCCs versus non-ccRCCs (ME: 116.0 vs. 65.9 HU, p=0.02; MIC: 5.5 vs 3.8 mg/mL, p=0.02).Among ccRCCs, ME was lower for high grade (FNG 3 and 4) versus low grade (FNG 1 and 2) tumors (86.2 vs. 145.9 HU, p=0.1), while MIC was higher (5.7 vs 5.4 mg/mL, p=0.7), however these relationships did not reach statistical significance. Further, ME was higher for chromophobe RCC versus oncocytoma (115 vs. 104.4 HU), while MIC was lower (5.3 vs 6.7 mg/mL, no statistical analysis performed due to small sample size). CONCLUSIONS: DECT imaging with tumor MIC may improve the diagnostic ability to differentiate between renal mass histologies compared to standard contrast enhanced CT imaging. Our preliminary data shows a trend towards higher iodine concentration in high grade ccRCCs compared to low grade tumors and benign histologies. Further investigation with larger cohorts is needed to delineate diagnostic thresholds and allow correlation with oncologic outcomes that could lead to improved prognostic tools. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1083-e1083 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rishi Sekar More articles by this author Giuseppe Toia More articles by this author Achille Mileto More articles by this author George Schade More articles by this author Expand All Advertisement Loading ...

Lung perfusion changes in COVID-19 pneumonia: a dual energy computerized tomography study.

Objective:There is limited and contradictory information about pulmonary perfusion changes detected with dual energy computed tomography (DECT) in COVID-19 cases. The purpose of this study was to define lung perfusion changes in COVID-19 cases with DECT, as well as to reveal any possible links between perfusion changes and laboratory findings.Methods:Patients who had a positive RT-PCR for SARS-CoV-2 and a contrast-enhanced chest DECT examination were included in the study. The pattern and severity of perfusion deficits were evaluated, as well as the relationships between perfusion deficit severity and laboratory results and CT severity ratings. The paired t-test, Wilcoxon test, and Student’s t-test were used to examine the changes in variables and perfusion deficits. p < 0.05 was regarded as statistically significant.Results:Study population consisted of 40 patients. Mean age was 60.73 ± 14.73 years. All of the patients had perfusion deficits at DECT images. Mean perfusion deficit severity score of the population was 8.45 ± 4.66 (min.-max, 1–19). In 24 patients (60%), perfusion deficits and parenchymal lesions matched completely. In 15 patients (37.5%), there was partial match. D dimer, CRP levels, CT severity score, and perfusion deficit severity score all had a positive correlationConclusions:Perfusion deficits are seen not only in opacification areas but also in parenchyma of normal appearance. The CT severity score, CRP, D-dimer, and SpO2 levels of the patients were determined to be related with perfusion deficit severity.Advances in knowledge:Findings of the current study may confirm the presence of micro-thrombosis in COVID-19 pneumonia.

Determination of the optimal range for virtual monoenergetic images in dual-energy CT based on physical quality parameters.

Virtual monoenergetic images (VMI) obtained from Dual-Energy Computed Tomography (DECT) with iodinated contrast are used in radiotherapy of the Head and Neck to improve the delineation of target volumes and organs at-risk (OAR). The energies used to vary from 40 to 70keV, but noise at low keV and the use of Single Energy CT (SECT) at low kVp settings may shrink this interval. There is no guide about how to find out the optimal range where VMI has a significant improvement related to SECT images. Our study proposes a procedure to determine this optimal range, based on common image quality parameters, and establishes this range in a Siemens Somatom Confidence and a Head and Neck protocol. We compared the quality of the VMI series at 40-60keV versus single X-ray tube voltage computed tomography (SECT) at 80 and 120kVp . Our reference was 120kVp . DECT images were sequentially acquired using the Siemens Somatom Confidence RT Pro CT according to the head and neck protocol in our department. VMI series were constructed using the Syngo Via software Monoenergetic+ algorithm. Quality parameters were: image uniformity, high- and low-contrast resolution, noise, and sensitivity to the iodinated contrast. We used the Catphan 604 phantom for quality control, except when assessing iodine sensitivity. To evaluate high contrast resolution, we calculated the modulation transfer function (MTF) using the point spread function estimation of a point bead and the slanted edge methods. For the low-contrast resolution, we used a statistical method for assessing differences between contrast structures and local noise. To measure the absolute value of noise and compare its texture, we used the standard deviation and the noise power spectrum. We measured iodine sensitivity by dissolving the Optiray Ultraject iodinated contrast in water in concentrations of 0 to 4500mg/l and then compared the contrast to noise ratio (CNR) and analyzed the linear correlation between concentration and HU. The entire series met the minimum quality requirements. However, the one at 40keV presented uniformity at the limits of acceptability. The high- and low-contrast resolutions were similar between series. The noise of the VMI series decreased with increasing energy, while sensitivity to the contrast displayed the opposite behavior. All series showed linearity of HUs from very low iodine concentrations. Images at 60keV presented lower iodine sensitivity than SECT at 80kVp , while those at 55keV were similar to them. Our method of image comparison based on standard quality parameters in phantom gave clear results about the optimal range and can be used as a guide to characterize any other DECT imaging protocols. The optimal range for using VMI images in iodinated contrasts in the Siemens system was 45-55keV. Lower energies lacked noise and uniformity, while higher ones could be substituted by SECT images at low kilovoltage (80kVp ).

A prospective study of dual-energy computed tomography for differentiating metastatic and non-metastatic lymph nodes of colorectal cancer.

Colorectal cancer (CRC) is the third most common malignancy worldwide, and lymph node metastasis is considered to be a risk factor for local recurrence and a poor prognosis in colorectal cancer. However, there remains a lack of reliable and non-invasive biomarkers to identify the lymph node status of CRC patients preoperatively. The purpose of this study was to explore the ability of dual-energy computed tomography (DECT) to differentiate metastatic from non-metastatic lymph nodes in colorectal cancer. Seventy-one patients with primary colorectal cancer underwent contrast-enhanced dual-energy computed tomography imaging preoperatively. The colorectal specimen was scanned postoperatively, and lymph nodes were matched to the pathology report. The following dual-energy computed tomography quantitative parameters were analyzed: dual-energy curve slope value (λHU), standardized iodine concentration (n△HU), iodine water ratio (nIWR), electron density value (nρeff), and effective atom-number (nZ), based on metastatic and non-metastatic lymph node differentiation. Also, sensitivity and specificity analyses were performed using receiver operating characteristic curves. In all patients, one hundred and fifty lymph nodes, including 66 non-metastatic and 84 metastatic lymph nodes, were matched using the radiological-pathological correlation. Metastatic nodes had significantly greater λHU, n△HU, and nIWR values than non-metastatic nodes in both the arterial and venous phases (P<0.01). The area under curve (AUC), sensitivity, and specificity were 0.80, 80%, and 66% for λHU; 0.86, 70%, and 95% for n△HU; and 0.88, 71%, and 95% for nIWR in the arterial phase. There was no significant difference in electron density and effective Z values between metastatic and non-metastatic lymph nodes. DECT quantitative parameters may help differentiate between metastatic and normal lymph nodes in patients with CRC.

Prediction of Intracerebral Hemorrhage After Endovascular Treatment of Acute Ischemic Stroke: Combining Quantitative Parameters on Dual-Energy CT with Clinical Related Factors

ObjectivesTo evaluate the predictive value of dual-energy CT (DECT) quantitative parameters and clinical influence factors for intracerebral hemorrhage (ICH) complications after endovascular treatment in patients with acute ischemic stroke (AIS). MethodsSeventy-two consecutive patients who underwent brain DECT immediately after endovascular treatment for AIS from November 2017 to October 2019 were included. Retrospectively, the volume of brain parenchymal hyperdensity area (HDA), the maximum iodine concentration, and maximum CT value on DECT images was evaluated and measured by two radiologists blinded to any clinical information independently. Follow-up CT imaging (24–72 h) were used to assess the development of ICH complications. DECT parameters and clinical influence factors were analyzed by Chi-square test or Fisher's exact test and Mann-Whitney U test. Receiver operating characteristic curves were generated for continuous variables. ResultsFollow-up CT images confirmed that forty of 72 patients (55.6%) developed ICH. The volume of HDA, median maximum iodine concentration and maximum CT value between ICH group and non-ICH group were significantly different (P < 0.001). Combining the DECT quantitative parameters with clinical predictors, receiver operating characteristic analysis revealed an area under the curve of 0.985, for identifying patients developing ICH with sensitivity, specificity, positive predictive value and negative predictive value were 90%, 100%, 100% and 88.9%, respectively. ConclusionsThree quantitative parameters of DECT and clinical predictors showed great predictive performance in identifing ICH complications in patients with brain parenchyma HDA after endovascular therapy, which may contribute to better clinical decision-making.

Improvement of image quality of laryngeal squamous cell carcinoma using noise-optimized virtual monoenergetic image and nonlinear blending image algorithms in dual-energy computed tomography.

Dual-energy computed tomography (DECT) has been used to improve image quality of head and neck squamous cell carcinoma (SCC). This study aimed to assess image quality of laryngeal SCC using linear blending image (LBI), nonlinear blending image (NBI), and noise-optimized virtual monoenergetic image (VMI+) algorithms. Thirty-four patients with laryngeal SCC were retrospectively enrolled between June 2019 and December 2020. DECT images were reconstructed using LBI (80 kV and M_0.6), NBI, and VMI+ (40 and 55 keV) algorithms. Contrast-to-noise ratio (CNR), tumor delineation, and overall image quality were assessed and compared. VMI+ (40 keV) had the highest CNR and provided better tumor delineation than VMI+ (55 keV), LBI, and NBI, while NBI provided better overall image quality than VMI+ and LBI (all corrected p < 0.05). VMI+ (40 keV) and NBI improve image quality of laryngeal SCC and may be preferable in DECT examination.

Fat Content Quantification Using Dual-Energy CT for Differentiation of Anterior Mediastinal Lesions from Normal or Hyperplastic Thymus

BackgroundDetection of fat content in thymic lesions is crucial to differentiate thymic hyperplasia from thymic tumors or other anterior mediastinal pathologies. PurposeTo assess the feasibility of dual-energy CT (DECT) fat content quantification for the differentiation of anterior mediastinal lesions from benign thymic lesions and the normal spectrum of the thymus. Materials and MethodsChest DECT images of 465 patients (median 61 years, 63% female) were visually evaluated by two radiologists and semiquantitatively scored based on the degree of fatty degeneration ranging from completely fatty (score 0) to predominantly soft-tissue (score 3), and anterior mediastinal mass (score 4). A subset of scans (n =134 including all cases with scores 2-4 and 20 randomly-selected cases from scores 0 and 1) underwent quantitative DECT analysis (fat fraction, iodine density, and conventional CT value). DECT values were compared across the semiquantitative scores. ResultsResults of visual evaluation included 35 with predominantly solid thymus (score 3) and 15 with anterior mediastinal mass (score 4). The most common clinical diagnoses of the 15 masses (including 8 with pathologic confirmation) were metastases (n = 10) and lymphoma (n = 4). CT values in the abnormal thymus were significantly higher than those in score 3 (median: 69.7 HU versus 19.9 HU, P <0.001). There was no significant difference in iodine density values (median: 1.7 mg/ml versus 1 mg/ml, P = 0.09). However, the fat fraction value was significantly lower in the abnormal thymus (score 4) than in the predominantly soft-tissue attenuation thymuses (score 3) (median: 12.8% versus 38.7%, P <0.001). ROC curve analysis showed that fat fraction had an AUC of 0.96 (P <0.001), with a cutoff of <39.2% fat fraction yielding 100% sensitivity and 85% specificity. ConclusionDECT fat fraction measurements of the thymus may provide additional value in distinguishing anterior mediastinal lesions from benign thymus. Use of DECT may reduce the need for subsequent imaging evaluation.

Diagnostic Performance of Dual-Energy CT for Detecting Painful Hip Prosthesis Loosening.

Background Revisions of hip prostheses are increasing, and conventional radiography (CR) is a primary tool for managing -complications. However, dual-energy CT (DECT) with virtual monoenergetic imaging is capable of reducing periprosthetic metal artifacts compared with standard CT. Purpose To compare the diagnostic performance of DECT and CR in detecting hip prosthesis loosening, using surgery as a reference for diagnosis. Materials and Methods This retrospective single-center study conducted between January 2018 and October 2020 included -consecutive patients with unilateral painful hip prostheses. Two independent readers (with 15 years and 4 years of experience) who were blinded to clinical findings evaluated CR and DECT images. At imaging, diagnosis of loosening prosthesis was made for -periprosthetic radiolucency greater than or equal to 2 mm wide or the presence of two or more secondary findings, including -periprosthetic osteolysis, angulation of the implant, fracture, or abnormal periosteal reaction. For each reader and for each imaging parameter, -sensitivity and specificity were calculated. The diagnostic performance of each imaging tool was compared by using the McNemar test. Interobserver agreements were calculated with Cohen κ statistics. Statistical software was used. Results Overall, 178 patients (mean age ± standard deviation, 74 years ± 20; 96 men) were included (121 undergoing surgery, 57 follow-up). Overall, 87 of 178 patients (49%) were diagnosed with a loosened prosthesis. DECT had higher sensitivity and specificity than CR for both reader 1 (94% [82 of 87 examinations; 95% CI: 87, 98] and 93% [85 of 91 examinations; 95% CI: 86, 97] vs 84% [73 of 87 examinations; 95% CI: 74, 91] and 91% [83 of 91 examinations; 95% CI: 83, 96], respectively; P < .001) and reader 2 (92% [80 of 87 examinations; 95% CI: 84, 97] and 95% [86 of 91 examinations; 95% CI: 88, 98] vs 80% [70 of 87 -examinations; 95% CI: 71, 88] and 91% [83 of 91 examinations; 95% CI: 83, 96], respectively; P = .001), with better interobserver agreement (κ, 0.88 [95% CI: 0.81, 0.95] vs 0.78 [95% CI: 0.69, 0.88]). Conclusion Dual-energy CT showed better diagnostic performance than conventional radiography in diagnosing hip prosthesis loosening. See also the editorial by Lutz in this issue. © RSNA, 2021.

Quantification of liver and muscular fat using contrast-enhanced Dual Source Dual Energy Computed Tomography compared to an established multi-echo Dixon MRI sequence

PurposeTo investigate the feasibility of liver fat quantification in contrast-enhanced dual source dual energy computed tomography (DECT) using multi-echo Dixon magnetic resonance imaging (MRI) as reference standard. MethodPatients who underwent MRI of the liver including a multi-echo Dixon sequence for estimation of proton density fat fraction in 2017 as well as contrast-enhanced DECT imaging of the abdomen were included in this retrospective, monocentric IRB approved study. Furthermore, patients with a hepatic fat amount >5% who were examined in 2018 with MRI and DECT were included. The final study group consisted of 81 patients with 90 pairs of examinations. Analysis of parameter maps was performed manually using congruent regions of interest which were placed in the liver parenchyma, in the erector spinae muscles, and psoas major muscles. ResultsMean patient age was 61 ± 13 years. Median time between MRI and DECT was 48 days. MRI liver fat quantification resulted in a median of 3.8% (IQR: 2.2–8.2%) compared to 1.8% (IQR: 0–6.3%) in DECT (p < 0.001), with a Spearman correlation of 0.73. Bland-Altman analysis resulted in a systematic underestimation of liver fat in DECT, with a mean difference of −1.7%. Fat quantification in the erector spinae muscles (p = 0.257) and the psoas major muscles (p = 0.208) was not significantly different in DECT compared to MRI. ConclusionsLiver and muscular fat quantification in portal-venous phase DECT is feasible with good to excellent correlation compared to a multi-echo Dixon MRI sequence analysis. While there is an underestimation of the liver fat content in DECT, there are no significant differences between DECT and MRI fat quantification of the erector spinae and psoas major muscles.

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.