Use of dual-energy CT for renal mass assessment

Quantitative Measurement on Contrast-Enhanced CT Distinguishes Small Clear Cell Renal Cell Carcinoma From Benign Renal Tumors: A Multicenter Study

RATIONALE AND OBJECTIVES

To evaluate the potential of quantitative measurements on contrast-enhanced CT (CECT) in differentiating small (≤4 cm) clear cell renal cell carcinoma (ccRCC) from benign renal tumors, including fat-poor angiomyolipoma (fpAML) and renal oncocytoma (RO).

MATERIALS AND METHODS

244 patients with pathologically confirmed ccRCC (n = 184) and benign renal tumors (fpAML, n = 50; RO, n = 10) were randomly assigned into training cohort (n = 193) and test cohort 1 (n = 51), while external test cohort 2 (n = 50) was from another hospital. Quantitative parameters were obtained from CECT (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP; excretory phase, EP) by measuring attenuation of renal mass and cortex and subsequently calculated. Univariable and multivariable logistic regression analyses were performed to evaluate the association between these parameters and ccRCC. Finally, the constructed models were compared with radiologists' diagnoses.

RESULTS

In univariable analysis, UP-related parameters, particularly UPC-T (cortex minus tumor attenuation on UP), demonstrated AUC of 0.766 in training cohort, 0.901 in test cohort 1, 0.805 in test cohort 2. The heterogeneity-related parameter SD (standard deviation) showed AUC of 0.781, 0.834, and 0.875 respectively. In multivariable analysis, model 1 incorporating UPC-T, NPC-T (cortex minus tumor attenuation on NP), CMPT-UPT (tumor attenuation on CMP minus UP), and SD yielded AUC of 0.866, 0.923, and 0.949 respectively. When compared with radiologists, multivariate models demonstrated higher accuracy (0.800-0.860) and sensitivity (0.794-0.971) than radiologists' assessments (accuracy: 0.700-0.720, sensitivity: 0.588-0.706).

CONCLUSION

Quantitative measurements on CECT, particularly UP- and heterogeneity-related parameters, have potential to discriminate ccRCC and benign renal tumors (fpAML, RO).

Photon-counting CT urogram: optimal acquisition potential (kV) determination for virtual noncontrast creation

PURPOSE

To quantitatively and qualitatively compare the degree of iodine removal in the collecting system from PCCT urographic phase-derived virtual noncontrast (VNC) images obtained at 140 kV versus 120 kV.

METHODS

A retrospective PACS search identified adult patients (>18 years) who underwent a PCCT urogram for hematuria from 4/2022 to 4/2023 with available urographic phase-derived VNC images in PACS. Tube voltage (120 kV, 140 kV), body mass index, CTDIvol, dose length product (DLP), and size-specific dose estimate (SSDE) were recorded. Hounsfield Unit (HU) in both renal pelvises and the urinary bladder on urographic-derived VNC were recorded. Three radiologists qualitatively assessed the degree of iodine removal (renal pelvis, urinary bladder) and diagnostic confidence for urinary stone detection. Continuous variables were compared for 140 kV versus 120 kV with the Wilcoxon rank sum test. A p < .05 indicated statistical significance.

RESULTS

63 patients (34 male; median (Q1, Q3) age: 30 (26, 34) years; 140 kV/120 kV: 30 patients/33 patients) were included. BMI, CTDIvol, DLP, and SSDE were not different for 140 kV and 120 kV (all p > .05). Median (Q1, Q3) collecting system HU (renal pelvis and bladder) was 0.9 (- 3.6, 4.4) HU at 140 kV and 10.5 (3.6, 26.7) HU at 120 kV (p = .04). Diagnostic confidence for urinary calculi was 4.6 [1.1] at 140 kV and 4.1 [1.4] at 120 kV (p = .005). Diagnostic confidence was 5/5 (all readers) in 82.2% (74/90) at 140 kV and 59.6% (59/99) at 120 kV (p < .001).

CONCLUSION

PCCT urographic phase-derived VNC images obtained at 140 kV had better collecting system iodine removal than 120 kV with similar patient radiation exposure. With excellent PCCT urographic phase iodine removal at 140 kV, consideration can be made to utilize a single-phase CT urogram in young patients.

Deep-learned generation of renal dual-energy CT from a single-energy scan

AIM

To investigate the role of the deep-learning (DL) method in the generation of dual-energy computed tomography (DECT) images from single-energy images for precise diagnosis of kidney stone type.

MATERIALS AND METHODS

DECT of 23 patients was acquired, and the stone types were investigated based on the DECT software suggestions. The data were divided into two paired groups:120 kVp input and 80 kVp target and 120 kVp input and 135 kVp targets, p2p-UNet-GAN was exploited to generate the different energy images based on the common CT protocols.

RESULTS

The images generated of the generative adversarial network (GAN) network were evaluated based on the SSIM, PSNR, and MSE metrics, and the values were estimated as 0.85-0.95, 28-32, and 0.85-0.89 respectively. The attenuation ratio of test patient images were estimated and compared with real patient reports. The network achieved high accuracy in stone region localisation and resulted in accurate stone type predictions.

CONCLUSION

This study presents a useful method based on the DL technique to reduce patient radiation dose and facilitate the prediction of urinary stone types using single-energy CT imaging.

Expanding Role of Dual-Energy CT for Genitourinary Tract Assessment in the Emergency Department, From the AJR Special Series on Emergency Radiology

Among explored applications of dual-energy CT (DECT) in the abdomen and pelvis, the genitourinary (GU) tract represents an area where accumulated evidence has established the role of DECT to provide useful information that may change management. This review discusses established applications of DECT for GU tract assessment in the emergency department (ED) setting, including characterization of renal stones, evaluation of traumatic injuries and hemorrhage, and characterization of incidental renal and adrenal findings. Use of DECT for such applications can reduce the need for additional multiphase CT or MRI examinations and reduce follow-up imaging recommendations. Emerging applications are also highlighted, including use of low-energy virtual monoenergetic images (VMIs) to improve image quality and potentially reduce contrast media doses and use of high-energy VMIs to mitigate renal mass pseudoenhancement. Finally, implementation of DECT into busy ED radiology practices is presented, weighing the trade-off of additional image acquisition, processing time, and interpretation time against potential additional useful clinical information. Automatic generation of DECT-derived images with direct PACS transfer can facilitate radiologists' adoption of DECT in busy ED environments and minimize impact on interpretation times. Using the described approaches, radiologists can apply DECT technology to improve the quality and efficiency of care in the ED.

Insights into Renal Cell Carcinoma with Novel Imaging Approaches

This article presents a comprehensive overview of new imaging approaches and techniques for improving the assessment of renal masses and renal cell carcinoma. The Bosniak classification, version 2019, as well as the clear cell likelihood score, version 2.0, will be discussed as new imaging algorithms using established techniques. Additionally, newer modalities, such as contrast-enhanced ultrasound, dual energy computed tomography, and molecular imaging, will be discussed in conjunction with emerging radiomics and artificial intelligence techniques. Current diagnostic algorithms combined with newer approaches may be an effective way to overcome existing limitations in renal mass and RCC characterization.

Pseudoenhancement in Cystic Renal Lesions - Impact of Virtual Monoenergetic Images of Photon-Counting Detector CT on Lesion Classification

RATIONALE AND OBJECTIVES

To investigate the impact of virtual monoenergetic images (VMI) from photon-counting detector CT (PCD-CT) on the enhancement and classification of renal cysts.

MATERIALS AND METHODS

Adults with renal cysts (≥7 mm) who received a triphasic examination on a clinical PCD-CT (120 kVp; IQ level 68) between July 2021 and March 2022 were retrospectively identified. Only non-enhancing cysts (enhancement<10 HU between unenhanced and venous phase at 70 keV) were included. VMI from 40 to 190 keV with increments of 10 keV were reconstructed from the venous phase. Enhancement was measured to classify each lesion as non-enhancing (<10 HU), equivocally enhancing (10-19 HU), and definitely enhancing (≥20 HU). Classification changes as a function of VMI were assessed. Pearson correlation coefficient, the Kruskal-Wallis and the Chi-square test were used.

RESULTS

A total of 86 patients (mean age, 74 ± 9 years; 74 male) with 160 non-enhancing renal cysts (17.6 ± 10 mm) were included. CT attenuation of the cysts increased from higher to lower VMI levels with a mean attenuation of 4 ± 11 HU at 190 keV to 36 ± 17 HU at 40 keV. Mean attenuation of the renal parenchyma was 43 ± 4 HU at 190 keV and 414 ± 71 HU at 40 keV. No cyst exhibited enhancement from 70 keV to 190 keV. At 40, 50, and 60 keV, 35% (56/160), 29% (47/160) and 9% (15/160) of cysts showed equivocal and 46% (74/160), 10% (16/160), and 0% (0/160) definite enhancement, respectively. There was no significant influence of size (P=.13), cyst location (P=.9) and BMI (P=.19) on enhancement classification.

CONCLUSION

VMI has a relevant impact on enhancement and classification of renal cysts with misclassification in a large number of cases at energy levels below 70 keV.

Virtual monochromatic spectral attenuation curve analysis for evaluation of incidentally detected small renal lesions using rapid kilovoltage-switching dual-energy computed tomography

PURPOSE

To determine whether the spectral attenuation curve on a rapid kilovoltage-switching dual-energy computed tomography (DECT) scan can distinguish enhancing from nonenhancing incidental small (1-4 cm) renal lesions compared with conventional single-energy attenuation changes.

METHODS

This retrospective study enrolled 46 patients with 78 renal lesions (24 enhancing; 54 nonenhancing) who underwent DECT with DE mode performed during the portovenous or nephrographic phase. Final diagnosis of enhancing and nonenhancing masses was confirmed by pathology or imaging following the established criteria. Virtual monochromatic images (VMI) were reconstructed, and the slopes between the VMI dataset at 40-70 keV (Slope HU_40-70), 40-100 keV (Slope HU_40-100), and 40-140 keV (Slope HU_40-140) were measured. Visual assessment of the curve pattern was recorded. Diagnostic accuracies were calculated with a cross-validated Mann-Whitney U test, and correlations of quantitative spectral parameters and intraclass correlation coefficient (ICC) were calculated using Spearman's rho correlation.

RESULTS

All quantitative and qualitative spectral analysis parameters significantly differentiated the enhancing and nonenhancing lesions (P < 0.001). The optimal slope thresholds calculated by cross-validation for Slope HU_40-70, Slope HU_40-100, and Slope HU_40-140 were 3.0, 1.8 and 1.2, respectively for reader 1 and 3.0, 1.9 and 1.15, respectively for reader 2. Using a slope threshold at all datasets yielded a high diagnostic accuracy of 96 for reader 1 and 95 for reader 2. Using a ∆HU threshold of 20 HU yielded an accuracy of 100. Visual analysis of the curve pattern also yielded high accuracy of 94.

CONCLUSIONS

The spectral attenuation curve on rapid kilovoltage-switching DECT gives excellent diagnostic accuracy differentiating between incidental enhancing and nonenhancing renal lesions. This benefit of DECT will be most helpful when the true unenhanced phase is not performed.

Detecting thin adhesive coatings in wood fiber materials with laboratory-based dual-energy computed tomography (DECT)

The distribution and good spreading of adhesive resins is critical for the wood-based panels industry. Full 3D non-destructive characterization is necessary, but methods are limited due to the chemical similarities between the resins and the wood fibers. For X-ray microtomography (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu $$\end{document}μCT), the doping of the resin with a highly attenuating contrast agent is necessary to visualize the resin distribution. However, the attenuation signal remains hard to segment clearly due to partial volume effects in the image, and phase mixing in the material. To help in the identification of the doped resin, dual-energy X-ray CT (DECT) is used to exploit the contrast agent’s K-edge, based on simulations which take into account the polychromatic properties of the X-ray tube and detector response. The contrast agent’s identification with DECT is validated with elemental mapping using scanning electron microscopy combined with energy-dispersive spectroscopy (SEM-EDX) on the surface of a wood-based panel sample, using data fusion between DECT and SEM-EDX. Overall, DECT results here in the first 3D identification of doped resin inside wood fiberboards, guiding the industry’s efforts in further improving the durability of wood-based panels.

Quantitative dual-energy CT techniques in the abdomen

Advances in dual-energy CT (DECT) technology and spectral techniques are catalyzing the widespread implementation of this technology across multiple radiology subspecialties. The inclusion of energy- and material-specific datasets has ushered overall improvements in CT image contrast and noise as well as artifacts reduction, leading to considerable progress in radiologists' ability to detect and characterize pathologies in the abdomen. The scope of this article is to provide an overview of various quantitative clinical DECT applications in the abdomen and pelvis. Several of the reviewed applications have not reached mainstream clinical use and are considered investigational. Nonetheless awareness of such applications is critical to having a fully comprehensive knowledge base to DECT and fostering future clinical implementation.

Virtual Non-contrast Imaging in The Abdomen and The Pelvis: An Overview

Virtual non-contrast (VNC) imaging is a post-processing technique generated from contrast-enhanced scans using dual-energy computed tomography (DECT). It is generated by removing iodine from imaging acquired at multiple energies. Myriad clinical studies have shown its ability to diagnose the various abdominal and pelvic pathologies discussed in the article. VNC is also a problem-solving tool for characterizing incidentally detected lesions ("incidentalomas"), often decreasing the need for additional follow-up imaging. It also obviates the multiphase image acquisitions to evaluate hematuria, hepatic steatosis, aortic endoleaks, and gastrointestinal bleeding by generating image datasets from different tissue attenuation values. The scope of this article is to provide an overview of various applications of VNC imaging obtained by DECT in the abdomen and pelvis.

Renal Lesion Characterization by Dual-Layer Dual-Energy CT: Comparison of Virtual and True Unenhanced Images

Background: Prior studies have provided mixed results for the ability to replace true unenhanced (TUE) images with virtual unenhanced (VUE) images when characterizing renal lesions by dual-energy CT. Detector-based dual-layer dual-energy (dlDECT) systems may optimize performance of VUE images for this purpose. Objective: To compare dual-phase dlDECT examinations evaluated using VUE and TUE images in differentiating cystic and solid renal masses. Methods: This retrospective study included 110 patients (mean age, 64.3±11.8 years; 46 women, 64 men) who underwent renal-mass protocol dlDECT between July 2018 and February 2022. TUE, VUE, and nephrographic-phase image sets were reconstructed. Lesions were diagnosed as solid masses by histopathology or MRI. Lesions were diagnosed as cysts by composite criteria reflecting findings from MRI, ultrasound, and the TUE and nephrographic-phase images of the dlDECT examinations. One radiologist measured lesions' attenuation on all dlDECT image sets. Lesion characterization was compared between use of VUE and TUE images, including when considering enhancement ≥20 HU to indicate presence of a solid mass. Results: The analysis included 219 lesions [33 solid masses; 186 cysts (132 simple, 20 septated, 34 hyperattenuating]. TUE and VUE attenuation were significantly different for solid masses (33.4 ± 7.1 HU vs 35.4 ± 8.6 HU, p=.002), simple cysts (10.8±5.6 vs 7.1±8.1 HU, p<.001), and hyperattenuating cysts (56.3±21.0 HU vs 47.6±16.3 HU, p<.001), but not septated cysts (13.6±8.1 HU vs 14.0±6.8 HU, p=.79). Frequency of enhancement ≥20 HU when using TUE and VUE images was 90.9% and 90.9% in solid masses, 0.0% and 9.1% in simple cysts, 15.0% and 10.0% in septated cysts, and 11.8% and 38.2% in hyperattenuating cysts. All solid lesions were concordant in terms of enhancement ≥20 HU when using TUE and VUE images. Twelve simple cysts and nine hyperattenuating cysts showed enhancement ≥20 HU when using VUE but not TUE images. Conclusion: Use of VUE images reliably detected enhancement in solid masses. However, VUE images underestimated attenuation of simple and hyperattenuating cysts, leading to false-positive findings of enhancement by such lesions. Clinical impact: The findings do not support replacement of TUE acquisitions with VUE images when characterizing renal lesions by dlDECT.