To evaluate the image quality and diagnostic performance for pancreatic lesion between true non-contrast (TNC) and virtual non-contrast (VNC) images obtained from the dual-energy computed tomography (DECT).
One hundred six patients with pancreatic mass underwent contrast-enhanced DECT examinations were retrospectively included in this study. VNC images of the abdomen were generated from late arterial (aVNC) and portal (pVNC) phases. For quantitative analysis, the attenuation differences and reproducibility of abdominal organs were compared between TNC and aVNC/pVNC measurements. Qualitatively image quality was assessed by two radiologists using a five-point scale, and they independently compared the detection accuracy of pancreatic lesions between TNC and aVNC/pVNC images. The volume CT dose index (CTDIvol) and size-specific dose estimates (SSDE) were recorded to evaluate the potential dose reduction when using VNC reconstruction to replace the unenhanced phase.
A total of 78.38% (765/976) of the attenuation measurement pairs were reproducible between TNC and aVNC images, and 71.0% (693/976) between TNC and pVNC images. In triphasic examinations, a total of 108 pancreatic lesions were found in 106 patients, and no significant difference in detection accuracy was found between TNC and VNC images (p = 0.587-0.957). Qualitatively, image quality was rated diagnostic (score ≥ 3) in all the VNC images. Calculated CTDIvol and SSDE reduction of about 34% could be achieved by omitting the non-contrast phase.
VNC images of DECT provide diagnostic image quality and accurate pancreatic lesions detection, which are a promising alternative to unenhanced phase with a substantial reduction of radiation exposure in clinical routine.

OBJECTIVE: The risk factors associated with iodine contrast extravasation immediately after endovascular thrombectomy (EVT) and subsequent hemorrhagic transformation within 24 hours remain unclear.
METHODS: Mixed images, iodine overlay maps, and virtual non-contrast images were reconstructed from 106 consecutive acute ischemic stroke patients who underwent dual energy computed tomography immediately and 24 hours after EVT. Multivariate analyses of clinical and radiological data were performed to explore independent predictors of iodine contrast extravasation and hemorrhagic transformation.
RESULTS: Sixty-eight (64.2%) patients exhibited pure iodine contrast extravasation after EVT; 30.9% developed hemorrhagic transformation within 24 hours after EVT. The number of stent retriever passes was independently associated with both iodine contrast extravasation (odds ratio 1.608; 95% confidence interval (CI) 1.047-2.469) and subsequent hemorrhagic transformation (odds ratio 1.477; 95% CI 1.003-2.175). Patients with more than two stent retriever passes were more likely to exhibit iodine contrast extravasation (sensitivity = 68.2%, specificity = 81.5%), while those with more than three stent retriever passes more often exhibited hemorrhage after iodine contrast extravasation (sensitivity = 64.6%, specificity = 87.2%).
CONCLUSIONS: The number of stent retriever passes was an independent predictor for both iodine contrast extravasation and subsequent hemorrhagic transformation.

Gout is a common form of inflammatory arthritis, and the majority of gout patients experience recurrent acute attacks, joint damage, and other complications. Carpal tunnel syndrome caused by gouty tophi indicates the severity of untreated gouty tophi. In this article, we report a 54-year- old male chronic gout patient with serious carpal tunnel syndrome secondary to monosodium urate crystal deposit. The patient was admitted to our department with palmar numbness and disability for two years. He had a history of gout for 30 years but received no treatment. Multiple monosodium urate crystals were observed to be deposited in and around carpal tunnel on the three-dimensional reconstruction using dual energy computed tomography, which confirmed the diagnosis. The needle biopsy and electrophysiological test also supported the diagnosis. Our case indicates that dual energy computed tomography is a useful method to diagnose carpal tunnel syndrome associated with gout and that we should keep in mind the possibility of gouty tophi as a possible cause of carpal tunnel syndrome.

Dual energy computed tomography (DECT) can improve the capability of differentiating different materials compared with conventional CT. However, due to non-negligible radiation exposure to patients, dose reduction has recently become a critical concern in CT imaging field. In this work, to reduce noise at the same time maintain DECT images quality, we present an iterative reconstruction algorithm for low-dose DECT images where in the objective function of the algorithm consists of a data-fidelity term and a regularization term. The former term is based on alpha-divergence to describe the statistical distribution of the DE sinogram data. And the latter term is based on the redundant information to reflect the prior information of the desired DECT images. For simplicity, the presented algorithm is termed as \"AlphaD-aviNLM\". To minimize the associative objective function, a modified proximal forward-backward splitting algorithm is proposed. Digital phantom, physical phantom, and patient data were utilized to validate and evaluate the presented AlphaD-aviNLM algorithm. The experimental results characterize the performance of the presented AlphaD-aviNLM algorithm. Speficically, in the digital phantom study, the presented AlphaD-aviNLM algorithm performs better than the PWLS-TV, PWLS-aviNLM, and AlphaD-TV with more than 49%, 34%, and 40% gains for the RMSE metric, 1.3%, 0.4%, and 0.7% gains for the FSIM metric and 13%, 8%, and 11% gains for the PSNR metric. In the physical phantom study, the presented AlphaD-aviNLM algorithm performs better than the PWLS-TV, PWLS-aviNLM, and AlphaD-TV with more than 0.55%, 0.07%, and 0.16% gains for the FSIM metric.

X-ray dual spectral CT (DSCT) scans the measured object with two different x-ray spectra, and the acquired rawdata can be used to perform the material decomposition of the object. Direct calibration methods allow a faster material decomposition for DSCT and can be separated in two groups: image-based and rawdata-based. The image-based method is an approximative method, and beam hardening artifacts remain in the resulting material-selective images. The rawdata-based method generally obtains better image quality than the image-based method, but this method requires geometrically consistent rawdata. However, today\'s clinical dual energy CT scanners usually measure different rays for different energy spectra and acquire geometrically inconsistent rawdata sets, and thus cannot meet the requirement. This paper proposes a practical material decomposition method to perform rawdata-based material decomposition in the case of inconsistent measurement. This method first yields the desired consistent rawdata sets from the measured inconsistent rawdata sets, and then employs rawdata-based technique to perform material decomposition and reconstruct material-selective images. The proposed method was evaluated by use of simulated FORBILD thorax phantom rawdata and dental CT rawdata, and simulation results indicate that this method can produce highly quantitative DSCT images in the case of inconsistent DSCT measurements.

OBJECTIVE: To evaluate the capacity of dual-energy computed tomography (DECT) virtual non-calcium (VNCa) images in detecting post-traumatic bone marrow lesions (BMLs) in the knee with a new grading system.
METHODS: DECT and magnetic resonance (MR) imaging were used to examine acute trauma of the knee in 32 patients. VNCa images were generated by dual-energy subtraction of calcium, and the lower end of the femur and upper end of the tibia each were divided into six regions for grading of bone marrow by two musculoskeletal radiologists using a four-grading system (Grade 4, very obvious lesions; Grade 3, relatively obvious lesions; Grade 2, slight or suspicious lesion on VNCa image and mild lesion on MR image; Grade 1, normal bone marrow). CT values were obtained in the BMLs. MR images were used as the reference standard. Grade 3-4 bone marrow was regarded as a positive result to evaluate the performance of VNCa images in detecting traumatic BMLs in the knee, and receiver operating characteristic (ROC) curve analysis of VNCa images for detection of knee BMLs was performed based on CT value of the bone marrow.
RESULTS: Bone marrow rating by the two radiologists showed very good consistency (κ=0.850 and 0.869 for VNCa and MR images, respectively). VNCa and MR images had good consistency (κ=0.799 for lower end of the femur; κ=0.659 for upper end of the tibia). When Grade 3-4 bone marrow was regarded as a positive result, the sensitivity, specificity, positive predictive value, and negative predictive value of VNCa images for detection of BMLs in the lower end of the femur were 73.5%, 98.6%, 94.7%, and 91.6%, respectively, and the values in the upper end of the tibia were 91.0%, 100.0%, 100.0%, and 95.4%, respectively. The CT values of bone marrow were (-52.5 ± 31.3) HU in positive area and (-91.2 ± 16.9) HU in negative area for the lower end of the femur, and those were (-51.3 ± 30.2) HU in positive area and (-104.7 ± 17.5) HU in negative area for the upper end of the tibia (all p values<0.0001). The areas under the ROC curve of VNCa images for detection of BMLs were 0.875 for the lower end of the femur and 0.939 for the upper end of the tibia.
CONCLUSIONS: Good interrater reliability of this new grading system in detecting traumatic BMLs in the knee by VNCa images of DECT can be obtained with good diagnostic predictive values.

Today\'s clinical dual energy computed tomography (DECT) scanners generally measure different rays for different energy spectra and acquire spatial mismatched raw data sets. The deficits in clinical DECT technologies suggest that mainly image based material decomposition methods are in use nowadays. However, the image based material decomposition is an approximate technique, and beam hardening artifacts remain in decomposition results. A recently developed image based iterative method for material decomposition from inconsistent rays (MDIR) can achieve much better image quality than the conventional image based methods. Inspired by the MDIR method, this paper proposes an iterative method to indirectly perform raw data based DECT even with completely mismatched raw data sets. The iterative process is initialized by density images that were obtained from an image based material decomposition. Then the density images are iteratively corrected by comparing the estimated polychromatic projections and the measured polychromatic projections. Only three iterations of the method are sufficient to greatly improve the qualitative and quantitative information in material density images. Compared with the MDIR method, the proposed method needs not to perform additional water precorrection. The advantages of the method are verified with numerical experiments from inconsistent noise free and noisy raw data.