Oncologic applications of dual-energy CT in the abdomen

Dual-energy computed tomographic (DECT) technology offers enhanced capabilities that may benefit oncologic imaging in the abdomen. By using two different energies, dual-energy CT allows material decomposition on the basis of energy-dependent attenuation profiles of specific materials. Although image acquisition with dual-energy CT is similar to that with single-energy CT, comprehensive postprocessing is able to generate not only images that are similar to single-energy CT (SECT) images, but a variety of other images, such as virtual unenhanced (VUE), virtual monochromatic (VMC), and material-specific iodine images. An increase in the conspicuity of iodine on low-energy VMC images and material-specific iodine images may aid detection and characterization of tumors. Use of VMC images of a desired energy level (40-140 keV) improves lesion-to-background contrast and the quality of vascular imaging for preoperative planning. Material-specific iodine images enable differentiation of hypoattenuating tumors from hypo- or hyperattenuating cysts and facilitate detection of isoattenuating tumors, such as pancreatic masses and peritoneal disease, thereby defining tumor targets for imaging-guided therapy. Moreover, quantitative iodine mapping may serve as a surrogate biomarker for monitoring effects of the treatment. Dual-energy CT is an innovative imaging technique that enhances the capabilities of CT in evaluating oncology patients.

Oncological applications of dual-energy computed tomography imaging

Dual-energy computed tomography (DECT) imaging is a promising method used in oncology for accurate detection/diagnosis of malignant and benign lesions. Use of dual-energy spectral, weighted average, color-coded map, and virtual unenhanced images provides increased visual detection and easy lesion delineation. Lesion detectability, sensitivity, and conspicuity are significantly improved using DECT. Material characterization and decomposition are promising using DECT. Both anatomical and functional information related to oncology can be provided by DECT using single contrast-enhanced CT.

Low enhancing papillary renal cell carcinoma diagnosed by using dual energy computerized tomography: a case report and review of literature

BACKGROUND

Papillary renal cell carcinoma (pRCC) is a mixed group of tumors that constitutes about 15-20% of all renal cortical cancers. Strong enhancement on computerized tomography (CT) is a feature of clear cell, but not of pRCC making the differentiation of papillary tumors from benign cysts a diagnostic problem in some cases.

CASE PRESENTATION

We report here a case of a female patient with pRCC that was initially diagnosed as a benign renal cyst. The patient is a 66 year old Caucasian female who initially presented with an ultrasound showing a 2.6 cm hypo-echoic lesion within the inferior pole of her left kidney. This was followed by a contrast enhanced computerized tomography that suggested the hypo-echoic lesion to be a hyper-attenuating benign renal cyst. Follow-up CT scan 4 months later demonstrated an increase in the size of the lesion to 3.2 cm with equivocal enhancement. A dual energy computerized tomography (DECT) showed the lesion to be a solid mass suspicious for renal cell carcinoma. A robotic partial nephrectomy revealed a papillary renal cell carcinoma with negative margins.

CONCLUSION

In this case report, we reviewed the literature on variations in enhancement of renal tumors and the possible role of dual energy contract enhanced CT in differentiating papillary tumors with low enhancement from benign kidney cystic lesions.

Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization

PURPOSE

Dual-energy CT (DECT) is being increasingly used for its capability of material decomposition and energy-selective imaging. A generic problem of DECT, however, is that the decomposition process is unstable in the sense that the relative magnitude of decomposed signals is reduced due to signal cancellation while the image noise is accumulating from the two CT images of independent scans. Direct image decomposition, therefore, leads to severe degradation of signal-to-noise ratio on the resultant images. Existing noise suppression techniques are typically implemented in DECT with the procedures of reconstruction and decomposition performed independently, which do not explore the statistical properties of decomposed images during the reconstruction for noise reduction. In this work, the authors propose an iterative approach that combines the reconstruction and the signal decomposition procedures to minimize the DECT image noise without noticeable loss of resolution.

METHODS

The proposed algorithm is formulated as an optimization problem, which balances the data fidelity and total variation of decomposed images in one framework, and the decomposition step is carried out iteratively together with reconstruction. The noise in the CT images from the proposed algorithm becomes well correlated even though the noise of the raw projections is independent on the two CT scans. Due to this feature, the proposed algorithm avoids noise accumulation during the decomposition process. The authors evaluate the method performance on noise suppression and spatial resolution using phantom studies and compare the algorithm with conventional denoising approaches as well as combined iterative reconstruction methods with different forms of regularization.

RESULTS

On the Catphan©600 phantom, the proposed method outperforms the existing denoising methods on preserving spatial resolution at the same level of noise suppression, i.e., a reduction of noise standard deviation by one order of magnitude. This improvement is mainly attributed to the high noise correlation in the CT images reconstructed by the proposed algorithm. Iterative reconstruction using different regularization, including quadratic orq-generalized Gaussian Markov random field regularization, achieves similar noise suppression from high noise correlation. However, the proposed TV regularization obtains a better edge preserving performance. Studies of electron density measurement also show that our method reduces the average estimation error from 9.5% to 7.1%. On the anthropomorphic head phantom, the proposed method suppresses the noise standard deviation of the decomposed images by a factor of ∼14 without blurring the fine structures in the sinus area.

CONCLUSIONS

The authors propose a practical method for DECT imaging reconstruction, which combines the image reconstruction and material decomposition into one optimization framework. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to decomposition accuracy, noise reduction, and spatial resolution.

Iterative image-domain decomposition for dual-energy CT

PURPOSE

Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its capability of material decomposition. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical values of DECT. Existing denoising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. In this work, the authors propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images.

METHODS

The proposed algorithm is formulated in the form of least-square estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. The regularization term enforces the image smoothness by calculating the square sum of neighboring pixel value differences. To retain the boundary sharpness of the decomposed images, the authors detect the edges in the CT images before decomposition. These edge pixels have small weights in the calculation of the regularization term. Distinct from the existing denoising algorithms applied on the images before or after decomposition, the method has an iterative process for noise suppression, with decomposition performed in each iteration. The authors implement the proposed algorithm using a standard conjugate gradient algorithm. The method performance is evaluated using an evaluation phantom (Catphan©600) and an anthropomorphic head phantom. The results are compared with those generated using direct matrix inversion with no noise suppression, a denoising method applied on the decomposed images, and an existing algorithm with similar formulation as the proposed method but with an edge-preserving regularization term.

RESULTS

On the Catphan phantom, the method maintains the same spatial resolution on the decomposed images as that of the CT images before decomposition (8 pairs/cm) while significantly reducing their noise standard deviation. Compared to that obtained by the direct matrix inversion, the noise standard deviation in the images decomposed by the proposed algorithm is reduced by over 98%. Without considering the noise correlation properties in the formulation, the denoising scheme degrades the spatial resolution to 6 pairs/cm for the same level of noise suppression. Compared to the edge-preserving algorithm, the method achieves better low-contrast detectability. A quantitative study is performed on the contrast-rod slice of Catphan phantom. The proposed method achieves lower electron density measurement error as compared to that by the direct matrix inversion, and significantly reduces the error variation by over 97%. On the head phantom, the method reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures.

CONCLUSIONS

The authors propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. By exploring the full variance-covariance properties of the decomposed images and utilizing the edge predetection, the proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability.

Interdependencies of acquisition, detection, and reconstruction techniques on the accuracy of iodine quantification in varying patient sizes employing dual-energy CT

PURPOSE

To assess the impact of patient habitus, acquisition parameters, detector efficiencies, and reconstruction techniques on the accuracy of iodine quantification using dual-source dual-energy CT (DECT).

MATERIALS AND METHODS

Two phantoms simulating small and large patients contained 20 iodine solutions mimicking vascular and parenchymal enhancement from saline isodensity to 400 HU and 30 iodine solutions simulating enhancement of the urinary collecting system from 400 to 2,000 HU. DECT acquisition (80/140 kVp and 100/140 kVp) was performed using two DECT systems equipped with standard and integrated electronics detector technologies. DECT raw datasets were reconstructed using filtered backprojection (FBP), and iterative reconstruction (SAFIRE I/V).

RESULTS

Accuracy for iodine quantification was significantly higher for the small compared to the large phantoms (9.2 % ± 7.5 vs. 24.3 % ± 26.1, P = 0.0001), the integrated compared to the conventional detectors (14.8 % ± 20.6 vs. 18.8 % ± 20.4, respectively; P = 0.006), and SAFIRE V compared to SAFIRE I and FBP reconstructions (15.2 % ± 18.1 vs. 16.1 % ± 17.6 and 18.9 % ± 20.4, respectively; P ≤ 0.003). A significant synergism was observed when the most effective detector and reconstruction techniques were combined with habitus-adapted dual-energy pairs.

CONCLUSION

In a second-generation dual-source DECT system, the accuracy of iodine quantification can be substantially improved by an optimal choice and combination of acquisition parameters, detector, and reconstruction techniques.

Iodine quantification to distinguish clear cell from papillary renal cell carcinoma at dual-energy multidetector CT: a multireader diagnostic performance study

PURPOSE

To investigate whether dual-energy multidetector row computed tomographic (CT) imaging with iodine quantification is able to distinguish between clear cell and papillary renal cell carcinoma ( RCC renal cell carcinoma ) subtypes.

MATERIALS AND METHODS

In this retrospective, HIPAA-compliant, institutional review board-approved study, 88 patients (57 men, 31 women) with diagnosis of either clear cell or papillary RCC renal cell carcinoma at pathologic analysis, who underwent contrast material-enhanced dual-energy nephrographic phase study between December 2007 and June 2013, were included. Five readers, blinded to pathologic diagnosis, independently evaluated all cases by determining the lesion iodine concentration on color-coded iodine maps. The receiving operating characteristic curve analysis was adopted to estimate the optimal threshold for discriminating between clear cell and papillary RCC renal cell carcinoma , and results were validated by using a leave-one-out cross-validation. Interobserver agreement was assessed by using an intraclass correlation coefficient. The correlation between tumor iodine concentration and tumor grade was investigated.

RESULTS

A tumor iodine concentration of 0.9 mg/mL represented the optimal threshold to discriminate between clear cell and papillary RCC renal cell carcinoma , and it yielded the following: sensitivity, 98.2% (987 of 1005 [95% confidence interval: 97.7%, 98.7%]); specificity, 86.3% (272 of 315 [95% confidence interval: 85.0%, 87.7%]); positive predictive value, 95.8% (987 of 1030 [95% confidence interval: 95.0%, 96.6%]); negative predictive value, 93.7% (272 of 290 [95% confidence interval: 92.8%, 94.7%]); overall accuracy of 95.3% (1259 of 1320 [95% confidence interval: 94.6%, 96.2%]), with an area under the curve of 0.923 (95% confidence interval: 0.913, 0.933). An excellent agreement was found among the five readers in measured tumor iodine concentration (intraclass correlation coefficient, 0.9990 [95% confidence interval: 0. 9987, 0.9993). A significant correlation was found between tumor iodine concentration and tumor grade for both clear cell (τ = 0.85; P < .001) and papillary RCC renal cell carcinoma (τ = 0.53; P < .001).

CONCLUSION

Dual-energy multidetector CT with iodine quantification can be used to distinguish between clear cell and papillary RCC renal cell carcinoma , and it provides insights regarding the tumor grade.

State of the art: dual-energy CT of the abdomen

Recent technologic advances in computed tomography (CT)--enabling the nearly simultaneous acquisition of clinical images using two different x-ray energy spectra--have sparked renewed interest in dual-energy CT. By interrogating the unique characteristics of different materials at different x-ray energies, dual-energy CT can be used to provide quantitative information about tissue composition, overcoming the limitations of attenuation-based conventional single-energy CT imaging. In the past few years, intensive research efforts have been devoted to exploiting the unique and powerful opportunities of dual-energy CT for a variety of clinical applications. This has led to CT protocol modifications for radiation dose reduction, improved diagnostic performance for detection and characterization of diseases, as well as image quality optimization. In this review, the authors discuss the basic principles, instrumentation and design, examples of current clinical applications in the abdomen and pelvis, and future opportunities of dual-energy CT.

Accuracy of contrast-enhanced dual-energy MDCT for the assessment of iodine uptake in renal lesions

OBJECTIVE

The objective of our study was to assess the accuracy of iodine-related attenuation and iodine quantification as imaging biomarkers of iodine uptake in renal lesions on a single-phase nephrographic image with dual-energy MDCT.

MATERIALS AND METHODS

Fifty-nine patients (41 men, 18 women; age range, 28-84 years) with 80 renal lesions underwent contrast-enhanced dual-energy CT during the nephrographic phase of enhancement. Renal lesions were characterized as enhancing or nonenhancing on color-coded iodine overlay maps using iodine-related attenuation (in Hounsfield units) and iodine quantification (in milligrams per milliliter). For iodine-related attenuation the iodine uptake thresholds of 15 and 20 HU were tested; a threshold of 0.5 mg/mL was used for iodine quantification. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of iodine-related attenuation and iodine quantification were calculated from chi-square tests of contingency with histopathology or imaging follow-up as the reference standard. The 95% CIs were calculated from binomial expression. Differences in sensitivity and specificity were assessed by means of McNemar analysis.

RESULTS

A significant difference in sensitivity and specificity was found between iodine-related attenuation with the thresholds of 15 HU (sensitivity, 91.4%; specificity, 93.3%; PPV, 91.4%; NPV, 93.3%) and 20 HU (sensitivity, 77.1%; specificity, 100%; PPV, 100%; NPV, 84.9%) (p = 0.008) and between iodine quantification (sensitivity, 100%; specificity, 97.7%; PPV, 97.2%; NPV, 100%) and iodine-related attenuation with a threshold of 20 HU (p = 0.004). No significant difference in sensitivity and specificity was found between iodine quantification and iodine-related attenuation with a threshold of 15 HU.

CONCLUSION

Contrast-enhanced dual-energy MDCT with iodine-related attenuation and iodine quantification allows accurate evaluation of iodine uptake in renal lesions on a single-phase nephrographic image.

Theoretical comparison of the iodine quantification accuracy of two spectral CT technologies

We compare the theoretical limits of iodine quantification for the photon counting multibin and dual energy technologies. Dual energy systems by necessity have to make prior assumptions in order to quantify iodine. We explicitly allow the multibin system to make the same assumptions and also allow them to be wrong. We isolate the effect of technology from imperfections and implementation issues by assuming both technologies to be ideal, i.e., without scattered radiation, unity detection efficiency and perfect energy response functions, and by applying the Cramér-Rao lower bound methodology to assess the quantification accuracy. When priors are wrong the maximum likelihood estimates will be biased and the mean square error of the quantification error is a more appropriate figure of merit. The evaluation assumes identical X-ray spectra for both methodologies and for that reason a sensitivity analysis is performed with regard to the assumed X-ray spectrum. We show that when iodine is quantified over regions of interest larger than 6 cm(2), multibin systems benefit by independent estimation of three basis functions. For smaller regions of interest multibin systems can increase quantification accuracy by making the same prior assumptions as dual energy systems.

Accuracy of dual-energy computed tomography for the measurement of iodine concentration using cardiac CT protocols: validation in a phantom model

PURPOSE

To assess the accuracy of dual-energy CT (DECT) for the quantification of iodine concentrations in a thoracic phantom across various cardiac DECT protocols and simulated patient sizes.

MATERIALS AND METHODS

Experiments were performed on first- and second-generation dual-source CT (DSCT) systems in DECT mode using various cardiac DECT protocols. An anthropomorphic thoracic phantom was equipped with tubular inserts containing known iodine concentrations (0-20 mg/mL) in the cardiac chamber and up to two fat-equivalent rings to simulate different patient sizes. DECT-derived iodine concentrations were measured using dedicated software and compared to true concentrations. General linear regression models were used to identify predictors of measurement accuracy

RESULTS

Correlation between measured and true iodine concentrations (n = 72) across CT systems and protocols was excellent (R = 0.994-0.997, P < 0.0001). Mean measurement errors were 3.0 ± 7.0% and -2.9 ± 3.8% for first- and second-generation DSCT, respectively. This error increased with simulated patient size. The second-generation DSCT showed the most stable measurements across a wide range of iodine concentrations and simulated patient sizes.

CONCLUSION

Overall, DECT provides accurate measurements of iodine concentrations across cardiac CT protocols, strengthening the case for DECT-derived blood volume estimates as a surrogate of myocardial blood supply.

KEY POINTS

• Dual-energy CT provides new opportunities for quantitative assessment in cardiac imaging. • DECT can quantify myocardial iodine as a surrogate for myocardial perfusion. • DECT measurements of iodine concentrations are overall very accurate. • The accuracy of such measurements decreases as patient size increases.

Comparison of dual-energy CT-derived iodine content and iodine overlay of normal, inflammatory and metastatic squamous cell carcinoma cervical lymph nodes

OBJECTIVES

To evaluate whether dual-energy computed tomography (DECT)-derived iodine content and iodine overlay could differentiate between normal, inflammatory and metastatic squamous cell carcinoma (SCC) cervical lymph nodes.

METHODS

This study was approved by the institutional review board. Sixteen patients with normal lymph nodes, 20 patients with enlarged nodes draining deep cervical inflammations and 23 patients with pathologically proved metastatic SCC nodes who underwent contrast enhanced DECT were retrospectively identified. Iodine content and overlay of 36 normal, 43 inflammatory and 52 metastatic lymph nodes were calculated using circular regions of interest and compared among the three groups. A receiver operating characteristic (ROC) curve was used to determine the sensitivity and specificity of iodine content and overlay for diagnosis of metastatic nodes.

RESULTS

Iodine content (mg/ml) was significantly lower for metastatic lymph nodes (2.34 ± 0.45) than for normal (2.86 ± 0.37) and inflammatory (3.53 ± 0.56) lymph nodes, P < 0.0001. Iodine overlay (HU) was also significantly lower for metastatic lymph nodes (47 ± 11.6) than normal (57.4 ± 8.2) and inflammatory nodes (69.3 ± 11.5), P < 0.0001. The areas under the ROC curve for iodine content and iodine overlay were 0.923 and 0.896.

CONCLUSIONS

DECT-derived iodine content and overlay differ significantly among normal, inflammatory and metastatic SCC cervical lymph nodes.

KEY POINTS

• Derived iodine content can be calculated from contrast-enhanced dual-energy CT. • Derived iodine content and iodine overlay could help characterise cervical lymph nodes. • Iodine parameters were significantly lower in metastatic lymph nodes than normal/inflammatory lymph nodes. • Iodine content appears more sensitive than iodine overlay for lymph node characterisation.

Iodine quantification with dual-energy CT: phantom study and preliminary experience with VX2 residual tumour in rabbits after radiofrequency ablation

OBJECTIVE

The purpose of our study was to validate iodine quantification in a phantom study with dual-source dual-energy CT (DECT) and to apply this technique to differentiate benign periablational reactive tissue from residual tumour in VX2 carcinoma in rabbits after radiofrequency ablation (RFA).

METHODS

We applied iodine quantification with DECT in a phantom and in VX2 carcinoma in rabbits after incomplete RFA to differentiate benign periablational reactive tissue from residual tumour and evaluated its efficacy in demonstrating response to therapeutic RFA. A series of tubes containing solutions of varying iodine concentration were scanned with DECT. The iodine concentration was calculated and compared with known true iodine concentration. Triple-phase contrast-enhanced DECT data on 24 rabbits with VX2 carcinoma were then assessed at Day 3 (n=6), 1 week (n=6), 2 weeks (n=6) and 3 weeks (n=6) after incomplete RFA independently by 2 readers. Dual-energy postprocessing was used to produce iodine-only images. Regions of interest were positioned on the iodine image over the lesion and, as a reference, over the aorta, to record iodine concentration in the lesion and in the aorta. The pathological specimens were sectioned in the same plane as DECT imaging, and the lesion iodine concentration and lesion-to-aorta iodine ratio of residual tumour and benign periablational reactive tissue were assessed.

RESULTS

There was excellent correlation between calculated and true iodine concentration (r=0.999, p<0.0001) in the phantom study. The lesion iodine concentration and lesion-to-aorta iodine ratio in residual tumour were significantly higher than in benign periablational reactive tissue in the 2-week group during the arterial phase (AP) (p<0.01) and in the 3-week group during both the AP (p<0.05) and the portal venous phase (p<0.05). There was no significant difference between them with respect to the lesion iodine concentration or lesion-to-aorta iodine ratio in the 3-day and 1-week groups.

CONCLUSION

Iodine quantification with DECT is accurate in a phantom study and can be used to differentiate benign periablational reactive tissue from residual tumour in VX2 carcinoma in rabbits after RFA.

ADVANCES IN KNOWLEDGE

Iodine quantification with DECT may help in differentiating benign periablational reactive tissue from residual tumour in VX2 carcinoma in rabbits after RFA.

Monitoring targeted therapy using dual-energy CT: semi-automatic RECIST plus supplementary functional information by quantifying iodine uptake of melanoma metastases

AIM

Supplementary functional information can contribute to assess response in targeted therapies. The aim of this study was to evaluate semi-automatic RECIST plus iodine uptake (IU) determination in melanoma metastases under BRAF inhibitor (vemurafenib) therapy using dual-energy computed tomography (DECT).

METHODS

Nine patients with stage IV melanoma treated with a BRAF inhibitor were included. Contrast-enhanced DECT was performed before and twice after treatment onset. Changes in tumor size were assessed according to RECIST. Quantification of IU (absolute value for total IU (mg) and volume-normalized IU (mg/ml)) was based on semi-automatic tumor volume segmentation. The decrease compared with baseline was calculated.

RESULTS

The mean change of RECIST diameter sum per patient was -47% at the first follow-up (FU), -56% at the second FU (P < 0.01). The mean normalized IU per patient was -21% at the first FU (P < 0.2) and -45% at the second FU (P < 0.01). Total IU per patient, combining both normalized IU and volume, showed the most pronounced decrease: -89% at the first FU and -90% at the second FU (P < 0.01).

CONCLUSION

Semi-automatic RECIST plus IU quantification in DECT enables objective, easy and fast parameterization of tumor size and contrast medium uptake, thus providing 2 complementary pieces of information for response monitoring applicable in daily routine.

Single-phase dual-energy CT urography in the evaluation of haematuria

AIM

To assess the value of a single-phase dual-energy computed tomography (DECT) urography protocol with synchronous nephrographic-excretory phase enhancement and to calculate the potential dose reduction by omitting the unenhanced scan.

MATERIALS AND METHODS

Eighty-four patients referred for haematuria underwent CT urography using a protocol that included single-energy unenhanced and dual-energy contrast-enhanced with synchronous nephrographic-excretory phase scans. DECT-based images [virtual unenhanced (VUE), weighted average, and colour-coded iodine overlay] were reconstructed. Opacification degree by contrast media of the upper urinary tract, and image quality of virtual unenhanced images were independently evaluated using a four-point scale. The diagnostic accuracy in detecting urothelial tumours on DECT-based images was determined. The dose of a theoretical dual-phase single-energy protocol was obtained by multiplying the effective dose of the unenhanced single-energy acquisition by two. Radiation dose saving by omitting the unenhanced scan was calculated.

RESULTS

The degree of opacification was scored as optimal or good in 86.9% of cases (k = 0.72); VUE image quality was excellent or good in 83.3% of cases (k = 0.82). Sensitivity, specificity, positive predictive value, and negative predictive value for urothelial tumours detection were 85.7, 98.6, 92.3, and 97.1%. Omission of the unenhanced scan led to a mean dose reduction of 42.7 ± 5%.

CONCLUSION

Single-phase DECT urography with synchronous nephrographic-excretory phase enhancement represents an accurate "all-in-one'' approach with a radiation dose saving up to 45% compared with a standard dual-phase protocol.

Dual-energy computed tomography (DECT) in renal masses: nonlinear versus linear blending

RATIONAL AND OBJECTIVES

To investigate whether a nonlinear-blending algorithm improves tumor conspicuity and image quality in the evaluation of renal masses at dual-energy computed tomography (DECT) during nephrographic phase of enhancement.

MATERIALS AND METHODS

The Institutional Review Board approved this retrospective study from archival material from patients consenting to the use of medical records for research purposes. A retrospective review of contrast-enhanced abdominal DECT scans in 45 patients (mean age, 59.5 years; range, 24-84 years) was performed. DECT data were reconstructed using nonlinear and linear blending. A region of interest was located within tumors and adjacent normal parenchyma; attenuation differences and contrast-to-noise ratios (CNRs) were calculated for renal masses on nonlinear- and linear-blended images. The two datasets were subjectively compared in terms of tumor detection and image quality. An exact Wilcoxon's matched pairs signed rank and marginal homogeneity tests were used to test whether differences in attenuation, CNR, and subjective assessment were greater using nonlinear blending.

RESULTS

The mean difference in attenuation for renal masses and adjacent portion of renal parenchyma was 138.4 Hounsfield units ± 28.9 SD using nonlinear blending, and 121.6 HU ± 18.0 SD using linear blending (P < .001). Mean CNR was 12.6 ± 2.5 SD using nonlinear blending, and 9.6 ± 2.2 SD using 0.3 linear-blended (P < .001). No significant difference in tumor detection was observed between the two algorithms. Image quality was significantly better (P < .001) using nonlinear blending.

CONCLUSION

Compared with standard linear blending, nonlinear-blending algorithm improves tumor conspicuity and image quality in renal masses at DECT evaluation during nephrographic phase of enhancement.