Value of virtual non-contrast images to identify uncomplicated cystic renal lesions: photon-counting detector CT vs. dual-energy integrating detector CT

PURPOSE

To investigate the value of photon-counting detector CT (PCD-CT) derived virtual non-contrast (VNC) reconstructions to identify renal cysts in comparison with conventional dual-energy integrating detector (DE EID) CT-derived VNC reconstructions.

MATERIAL AND METHODS

We prospectively enrolled consecutive patients with simple renal cysts (Bosniak classification-Version 2019, density ≤ 20 HU and/or enhancement ≤ 20 HU) who underwent multiphase (non-contrast, arterial, portal venous phase) PCD-CT and for whom non-contrast and portal venous phase DE EID-CT was available. Subsequently, VNC reconstructions were calculated for all contrast phases and density as well as contrast enhancement within the cysts were measured and compared. MRI and/or ultrasound served as reference standards for lesion classification.

RESULTS

19 patients (1 cyst per patient; age 69.5 ± 10.7 years; 17 [89.5%] male) were included. Density measurements on PCD-CT non-contrast and VNC reconstructions (arterial and portal venous phase) revealed no significant effect on HU values (p = 0.301). In contrast, a significant difference between non-contrast vs. VNC images was found for DE EID-CT (p = 0.02). For PCD-CT, enhancement for VNC reconstructions was < 20 HU for all evaluated cysts. DE EID-CT measurements revealed an enhancement of > 20 HU in five lesions (26.3%) using the VNC reconstructions, which was not seen with the non-contrast images.

CONCLUSION

PCD-CT-derived VNC images allow for reliable and accurate characterization of simple cystic renal lesions similar to non-contrast scans whereas VNC images calculated from DE EID-CT resulted in substantial false characterization. Thus, PCD-CT-derived VNC images may substitute for non-contrast images and reduce radiation dose and follow-up imaging.

Photon-Counting Detector CT for Kidney Stone Detection in Excretory Phase CT-Comparison Between Virtual Non-contrast and Virtual Non-iodine Reconstructions in a 3D Printed Kidney Phantom

RATIONALE AND OBJECTIVES

To evaluate and compare the effectiveness of contrast media subtraction and kidney stone detection between a virtual non-iodine reconstruction algorithm (VNI; PureCalcium) and a virtual non-contrast (VNC) algorithm in excretory phase photon-counting detector computed tomography (PCD-CT), using a 3D printed kidney phantom under various tube voltages and radiation doses.

MATERIALS AND METHODS

A 3D-printed kidney phantom, holding Calcium Oxalate (CaOx) and uric acid stones within contrast-enhanced calyces, was created. The calyx density mirrored the average density observed in 200 excretory phase patients (916 HU at 110 kV). Imaging was conducted on a clinical dual-source PCD-CT at 120 kV and 140 kV, with radiation doses set at 5, 10, and 15 mGy. VNI and VNC algorithms were applied. Two blinded readers evaluated the image quality, along with the degree of contrast media and kidney stone subtraction, using visual scales. Krippendorff's alpha was calculated to determine inter-reader agreement, and the Chi-squared test was employed for comparing ordinal data.

RESULTS

Reader 2 rated overall image quality higher for VNI than VNC (4.90 vs. 4.00; P < .05), while Reader 1 found no significant difference (4.96 vs. 5.00; P > .05). Substantial agreement was observed between readers for contrast media subtraction in both VNC and VNI (Krippendorff's alpha range: 0.628-0.748). Incomplete contrast media subtraction occurred more frequently with VNI for both readers (Reader 1: 29% vs. 15%; P < .05; Reader 2: 24% vs. 20%; P > .05). Uric acid and smaller stones (<5 mm) were more likely to be subtracted than CaOx and larger stones in both VNC and VNI. Overall, a higher rate of stone subtraction was noted with VNI compared to VNC (Reader 1: 22% vs. 16%; Reader 2: 25% vs. 10%; P < .05). Neither radiation dose nor tube voltage significantly influenced stone subtraction (P > .05).

CONCLUSION

VNC demonstrated greater accuracy than VNI for contrast media subtraction and kidney stone visibility. Radiation dose and tube voltage had no significant impact. Nonetheless, both algorithms still exhibited frequent incomplete contrast media subtraction and partial kidney stone subtraction.

CT radiomic features reproducibility of virtual non-contrast series derived from photon-counting CCTA datasets using a novel calcium-preserving reconstruction algorithm compared with standard non-contrast series: focusing on epicardial adipose tissue

PURPOSE

We aimed to evaluate the reproducibility of computed tomography (CT) radiomic features (RFs) about Epicardial Adipose Tissue (EAT). The features derived from coronary photon-counting computed tomography (PCCT) angiography datasets using the PureCalcium (VNCPC) and conventional virtual non-contrast (VNCConv) algorithm were compared with true non-contrast (TNC) series.

METHODS

RFs of EAT from 52 patients who underwent PCCT were quantified using VNCPC, VNCConv, and TNC series. The agreement of EAT volume (EATV) and EAT density (EATD) was evaluated using Pearson's correlation coefficient and Bland-Altman analysis. A total of 1530 RFs were included. They are divided into 17 feature categories, each containing 90 RFs. The intraclass correlation coefficients (ICCs) and concordance correlation coefficients (CCCs) were calculated to assess the reproducibility of RFs. The cutoff value considered indicative of reproducible features was > 0.75.

RESULTS

the VNCPC and VNCConv tended to underestimate EATVs and overestimate EATDs. Both EATV and EATD of VNCPC series showed higher correlation and agreement with TNC than VNCConv series. All types of RFs from VNCPC series showed greater reproducibility than VNCConv series. Across all image filters, the Square filter exhibited the highest level of reproducibility (ICC = 67/90, 74.4%; CCC = 67/90, 74.4%). GLDM_GrayLevelNonUniformity feature had the highest reproducibility in the original image (ICC = 0.957, CCC = 0.958), exhibiting a high degree of reproducibility across all image filters.

CONCLUSION

The accuracy evaluation of EATV and EATD and the reproducibility of RFs from VNCPC series make it an excellent substitute for TNC series exceeding VNCConv series.

Virtual non-contrast series of photon-counting detector computed tomography angiography for aortic valve calcium scoring

The aim of our study was to evaluate two different virtual non-contrast (VNC) algorithms applied to photon counting detector (PCD)-CT data in terms of noise, effectiveness of contrast media subtraction and aortic valve calcium (AVC) scoring compared to reference true non-contrast (TNC)-based results. Consecutive patients underwent TAVR planning examination comprising a TNC scan, followed by a CTA of the heart. VNC series were reconstructed using a conventional (VNCconv) and a calcium-preserving (VNCpc) algorithm. Noise was analyzed by means of the standard deviation of CT-values within the left ventricle. To assess the effectiveness of contrast media removal, heart volumes were segmented and the proportion of their histograms > 130HU was taken. AVC was measured by Agatston and volume score. 41 patients were included. Comparable noise levels to TNC were achieved with all VNC reconstructions. Contrast media was effectively virtually removed (proportions > 130HU from 81% to < 1%). Median calcium scores derived from VNCconv underestimated TNC-based scores (up to 74%). Results with smallest absolute difference to TNC were obtained with VNCpc reconstructions (0.4 mm, Br36, QIR 4), but with persistent significant underestimation (median 29%). Both VNC algorithms showed near-perfect (r²>0.9) correlation with TNC. Thin-slice VNC reconstructions provide equivalent noise levels to standard thick-slice TNC series and effective virtual removal of iodinated contrast. AVC scoring was feasible on both VNC series, showing near-perfect correlation, but with significant underestimation. VNCpc with 0.4 mm slices and Br36 kernel at QIR 4 gave the most comparable results and, with further advances, could be a promising replacement for additional TNC.

Detection of moderate hepatic steatosis on contrast-enhanced dual-source dual-energy CT: Role and accuracy of virtual non-contrast CT

PURPOSE

To investigate diagnostic accuracy of virtual non contrast (VNC) images, based on dual-source dual-energy CT (dsDECT), for detection of at least moderate steatosis and to define a threshold value to make this diagnosis on VNC.

METHODS

This single-institution retrospective study included patients who had multi-phasic protocol dsDECT. Regions of interests were placed in different segments of the liver and spleen on true non-contrast (TNC), VNC, and portal-venous phase (PVP) images. At least moderate steatosis was defined as liver attenuation (LHU) < 40 HU on TNC. Diagnostic performance of VNC to detect steatosis was determined and the new threshold was tested in a validation cohort.

RESULTS

236 patients were included in training cohort. Mean liver attenuation values were 51.3 ± 10.8 HU and 58.1 ± 11.5 HU for TNC and VNC (p < 0.001), with a mean difference (VNC - TNC) of 6.8 ± 6.9 HU. Correlation between TNC and VNC was strong (r = 0.81, p < 0.001). The AUCs of LHU on VNC for detection of hepatic steatosis were 0.92 (95 % Cl: 0.86-0.98), 0.92 (95 % Cl: 0.87-0.97), 0.92 (95 % Cl: 0.86-0.99), 0.91 (95 % Cl: 0.84-0.97), and 0.87 (95 % Cl: 0.80-0.95) for entire liver, left lateral, left medial, right anterior, and right posterior segments, respectively. VNC had sensitivity/specificity of 100 % /42 % when using a threshold of 40 HU; they were 69 % and 95 %, respectively, when using optimized threshold of 46 HU. This threshold showed similar performance in validation cohort (n = 80).

CONCLUSIONS

Hepatic attenuation on VNC has promising performance for detection of at least moderate steatosis. Proposed threshold of 46 HU provides high specificity and moderate sensitivity to detect steatosis.

Virtual unenhanced dual-energy computed tomography for photon radiotherapy: The effect on dose distribution and cone-beam computed tomography based position verification

Background and Purpose

Virtual Unenhanced images (VUE) from contrast-enhanced dual-energy computed tomography (DECT) eliminate manual suppression of contrast-enhanced structures (CES) or pre-contrast scans. CT intensity decreases in high-density structures outside the CES following VUE algorithm application. This study assesses VUE's impact on the radiotherapy workflow of gynecological tumors, comparing dose distribution and cone-beam CT-based (CBCT) position verification to contrast-enhanced CT (CECT) images.

Materials and Methods

A total of 14 gynecological patients with contrast-enhanced CT simulation were included. Two CT images were reconstructed: CECT and VUE. Volumetric Modulated Arc Therapy (VMAT) plans generated on CECT were recalculated on VUE using both the CECT lookup table (LUT) and a dedicated VUE LUT. Gamma analysis assessed 3D dose distributions. CECT and VUE images were retrospectively registered to daily CBCT using Chamfer matching algorithm..

Results

Planning target volume (PTV) dose agreement with CECT was within 0.35% for D2%, Dmean, and D98%. Organs at risk (OARs) D2% agreed within 0.36%. A dedicated VUE LUT lead to smaller dose differences, achieving a 100% gamma pass rate for all subjects. VUE imaging showed similar translations and rotations to CECT, with significant but minor translation differences (<0.02 cm). VUE-based registration outperformed CECT. In 24% of CBCT-CECT registrations, inadequate registration was observed due to contrast-related issues, while corresponding VUE images achieved clinically acceptable registrations.

Conclusions

VUE imaging in the radiotherapy workflow is feasible, showing comparable dose distributions and improved CBCT registration results compared to CECT. VUE enables automated bone registration, limiting inter-observer variation in the Image-Guided Radiation Therapy (IGRT) process.

Diagnostic accuracy of virtual non-contrast CT for aortic valve stenosis severity evaluation

BACKGROUND

Computed tomography aortic valve calcium (AVC) score has accepted value for diagnosing and predicting outcomes in aortic stenosis (AS). Multi-energy CT (MECT) allows virtual non-contrast (VNC) reconstructions from contrast scans. We aim to compare the VNC-AVC score to the true non-contrast (TNC)-AVC score for assessing AS severity.

METHODS

We prospectively included patients undergoing a MECT for transcatheter aortic valve replacement (TAVR) planning. TNC-AVC was acquired before contrast, and VNC-AVC was derived from a retrospectively gated contrast-enhanced scan. The Agatston scoring method was used for quantification, and linear regression analysis to derive adjusted-VNC values.

RESULTS

Among 109 patients (55% female) included, 43% had concordant severe and 14% concordant moderate AS. TNC scan median dose-length product was 116 ​mGy∗cm. The median TNC-AVC was 2,107 AU (1,093-3,372), while VNC-AVC was 1,835 AU (1293-2,972) after applying the coefficient (1.46) and constant (743) terms. A strong correlation was demonstrated between methods (r ​= ​0.93; p ​< ​0.001). Using accepted thresholds (>1,300 AU for women and >2,000 AU for men), 65% (n ​= ​71) of patients had severe AS by TNC-AVC and 67% (n ​= ​73) by adjusted-VNC-AVC. After estimating thresholds for adjusted-VNC (>1,564 AU for women and >2,375 AU for men), 56% (n ​= ​61) had severe AS, demonstrating substantial agreement with TNC-AVC (κ ​= ​0.77).

CONCLUSIONS

MECT-derived VNC-AVC showed a strong correlation with TNC-AVC. After adjustment, VNC-AVC demonstrated substantial agreement with TNC-AVC, potentially eliminating the requirement for an additional scan and enabling reductions in both radiation exposure and acquisition time.

Dedicated virtual non-contrast images adapted for liver tissue in clinical photon counting CT improve virtual non-contrast imaging in various organs beyond the liver

PURPOSE

Purpose of this study is to re-evaluate the accuracy and diagnostic reliability of virtual non-contrast (VNC) images acquired with the photon-counting computed tomography (PCCT) after an update of the CT scanner software.

METHODS

Fifty-four patients were retrospectively enrolled. VNC images were reconstructed from true non-contrast (TNC) images (VNCn) and contrast-enhanced images in portal venous contrast phase (VNCv). Additionally, a liver-specific VNC (VNCl) was assessed. Quantitative image properties of VNC and TNC images were compared and consistency between VNC images was evaluated. Regions of interest were drawn in the liver, spleen, renal cortex, aorta, muscle and subcutaneous fat.

RESULTS

Attenuation values on all VNC images differed significantly from TNC images in the liver, renal cortex, aorta and fat. A mean offset of <10HU between TNC and all VNC images was found in the liver, spleen and muscle. The comparison of TNC and VNCl images revealed an offset < 10HU in fat. Differences ≤ 10HU between TNC and VNCv and between TNC and VNCl were found in 68%, respectively in 75%. Differences ≤ 15HU were found in 79%, respectively in 92% of all measurements. Differences ≤ 10HU between TNC and VNCn were found in 79% and differences ≤ 15HU in 85%.

CONCLUSION

Although there are statistically significant differences between HU values measured on TNC and VNC images in certain tissues, the minor offsets measured in liver and spleen suggest a good clinical applicability of VNCv and VNCl images. The significantly lower offset in subcutaneous fat on VNCl images suggests a superiority for measurements in adipose tissues.

Calcium scoring on coronary computed angiography tomography with photon-counting detector technology: Predictors of performance

INTRODUCTION

Obtaining accurate coronary artery calcium (CAC) score measurements from CCTA datasets with virtual non-iodine (VNI) algorithms would reduce acquisition time and radiation dose. We aimed to assess the agreement of VNI-derived and conventional true non-contrast (TNC)-based CAC scores and to identify the predictors of accuracy.

METHODS

CCTA datasets were acquired with either 120 or 140 ​kVp. CAC scores and volumes were calculated from TNC and VNI images in 197 consecutive patients undergoing CCTA. CAC density score, mean volume/lesion, aortic Hounsfield units and standard deviations were then measured. Finally, percentage deviation (VNI - TNC/TNC∗100) of CTA-derived CAC scores from non-enhanced scans was calculated for each patient. Predictors (including anthropometric and acquisition parameters, as well as CAC characteristics) of the degree of discrepancy were evaluated using linear regression analysis.

RESULTS

While the agreement between TNC and VNI was substantial (mean bias, 6.6; limits of agreement, 178.5/145.3), a non-negligible proportion of patients (36/197, 18.3%) were falsely reclassified as CAC score ​= ​0 on VNI. The use of higher tube voltage significantly decreased the percentage deviation relative to TNC-based values (β ​= ​-0.21 [95%CI: 0.38 to -0.03], p ​= ​0.020) and a higher CAC density score also proved to be an independent predictor of a smaller difference (β ​= ​-0.22 [95%CI: 0.37 to -0.07], p ​= ​0.006).

CONCLUSION

The performance of VNI-based calcium scoring may be improved by increased tube voltage protocols, while the accuracy may be compromised for calcified lesions of lower density. The implementation of VNI in clinical routine, however, needs to be preceded by a solution for detecting smaller lesions as well.

Effect of iodine concentration and body size on iodine subtraction in virtual non-contrast imaging: A phantom study

INTRODUCTION

Dual-energy computed tomography (DECT) can generate virtual non-contrast (VNC) images. Herein, we sought to improve the accuracy of VNC images by identifying the optimal slope of contrast media (SCM) for VNC-image generation based on the iodine concentration and subject's body size.

METHODS

We used DECT to scan a multi-energy phantom including four iodine concentration rods (15, 10, 5, and 2 mg/mL), and 240 VNC images (eight SCM ranging from 0.49 to 0.56 × three body sizes × ten scans) that were generated by three-material decomposition. The CT number of each iodine and solid water rod part was measured in each VNC image. The difference in the CT number between the iodine and the solid water rod part was calculated and compared using paired t-test or repeated measures ANOVA.

RESULTS

The SCM that achieved an absolute value of the difference in CT number of <5.0 Hounsfield units (HU) for all body sizes simultaneously was greater at lower iodine concentration (SCM of 0.5, 0.51, and 0.53 at 10, 5, and 2 mg/mL iodine, respectively). At an iodine concentration of 15 mg/mL, no SCM achieved an absolute difference of <5.0 HU in CT number for all body sizes simultaneously. At all iodine concentrations, the SCM achieving the minimal difference in the CT number increased with the increase in body size.

CONCLUSION

By adjusting the SCM according to the iodine concentration and body size, it is possible to generate VNC images with an accuracy of <5.0 HU.

IMPLICATIONS FOR PRACTICE

Improving the accuracy of VNC images minimizing incomplete iodine subtraction would make it possible to replace true non-contrast (TNC) images with VNC images and reduce the radiation dose.

Added value of iodine-specific imaging and virtual non-contrast imaging for gastrointestinal assessment using dual-energy computed tomography

Dual-energy computed tomography (DECT) has become increasingly available and can be readily incorporated into clinical practice. Although DECT can provide a wide variety of spectral imaging reconstructions, most clinically valuable information is available from a limited number of standard image reconstructions including virtual non-contrast and iodine overlay. The combination of these standard reconstructions can be used for specific diagnostic tasks that provide added value over traditional CT protocols. In this pictorial essay, the added value of these standard reconstructed images will be demonstrated by case examples for diseases specifically related to the gastrointestinal system.

ASPECTS estimation using dual-energy CTA-derived virtual non-contrast in large vessel occlusion acute ischemic stroke: a dose reduction opportunity for patients undergoing repeat CT?

PURPOSE

Recent studies have shown the feasibility of dual-energy CT (DECT) virtual non-contrast (VNC) for determining infarct extent. In this study, patients presenting with large-vessel occlusion (LVO) acute ischemic stroke (AIS), we assess whether ASPECTS on DECTA-VNC differs from non-contrast CT (NCCT).

METHODS

After IRB approval, LVO-AIS patients undergoing NCCT and DECTA between October 2016 and September 2018 were retrospectively reviewed. DECTA-VNC images were derived using Syngo.via (Siemens, Erlangen, Germany). ASPECTS was scored by two blinded neuroradiologists. Square-weighted kappa statistic, diagnostic performance, Wilcoxon signed-rank tests between groups, and CT doses were calculated.

RESULTS

Fifty-one patients met inclusion criteria, with median age of 76 (IQR 67-82); 26/51 (51%) were female. Median time between last-known-well and CT was 120 min (IQR 60-252). DECTA-VNC ASPECTS score differed by ≤ 1 from consensus NCCT in 49/51 (96%) patients for reader 1 and in 46/51 (90%) for reader 2. ASPECTS on DECTA-SI and consensus NCCT differed by ≤ 1 in 45/51 (88%) for both readers. On a per ASPECTS-region basis, DECTA-VNC had 87% sensitivity, 95% specificity, 0.82% PPV, and 0.96% NPV. ASPECTS inter-rater agreement was highest for DECTA-VNC (κ = 0.71), DECTA-SI (κ = 0.48), and NCCT (κ = 0.40). NCCT median CTDIvol was 63.7 mGy (IQR 60.7-67.2); DLP was 1060.0 mGy·cm (IQR 981.0-1151.5). DECTA-VNC dose was lower: median CTDIvol was 20.9 mGy (IQR 19.8-22.2); DLP was 804.1 (IQR 691.6-869.4), p < 0.0001.

CONCLUSION

DECTA-derived VNC yielded similar ASPECTS scores as NCCT and is therefore non-inferior in early ischemia-related low attenuation edema/infarct detection in acute LVO-AIS patients. Further evaluation of the role of DECTA-VNC in AIS imaging is warranted.

Virtual non-contrast enhanced magnetic resonance imaging (VNC-MRI)

PURPOSE

Application of contrast agents (CA) is widely used in various clinical fields like oncology. Similar to approaches used in computed tomography, virtual non-contrast enhanced (VNC) images can be generated with the goal to supersede true non-contrast enhanced (TNC) images.

METHODS

In MRI a T1-mapping sequence with variable flip angle (VFA) was used to acquire two images with different image contrast at the same time. To generate VNC images postprocessing based on this technique, an image-space based material decomposition algorithm was used. The inverse of a sensitivity matrix, consisting of intensity values for both VFA images and every material respectively, was used to determine the three material fractions and to calculate the final VNC images. The technique was tested on a 3 T scanner using a phantom and two in-vivo scans of patients with glioma and glioblastoma respectively. In all these cases the required six values were manually derived from the respective material or the background from both VFA images.

RESULTS

Postprocessing results of the phantom show that the chosen materials can be separated and visualized individually and unwanted materials can be suppressed. In the VNC images of in-vivo scans the signal of the CA is removed successfully.

CONCLUSION

It was shown that VNC images that match the visual impression of the TNC images can be generated, resulting in possibly reduced scan times and avoided mismatches due to movement of the patient.

Spectral detector CT-derived virtual non-contrast images: comparison of attenuation values with unenhanced CT

PURPOSE

To assess virtual non-contrast (VNC) images obtained on a detection-based spectral detector CT scanner and determine how attenuation on VNC images derived from various phases of enhanced CT compare to those obtained from true unenhanced images.

METHODS

In this HIPAA compliant, IRB approved prospective multi-institutional study, 46 patients underwent pre- and post-contrast imaging on a prototype dual-layer spectral detector CT between October 2013 and November 2015, yielding 84 unenhanced and VNC pairs (25 arterial, 39 portal venous/nephrographic, 20 urographic). Mean attenuation was measured by one of three readers in the liver, spleen, kidneys, psoas muscle, abdominal aorta, and subcutaneous fat. Equivalence testing was used to determine if the mean difference between unenhanced and VNC attenuation was less than 5, 10, or 15 HU. VNC image quality was assessed on a 5 point scale.

RESULTS

Mean difference between unenhanced and VNC attenuation was <15 HU in 92.6%, <10 HU in 75.2%, and <5 HU in 44.4% of all measurements. Unenhanced and VNC attenuation were equivalent in all tissues except fat using a threshold of <10 HU difference (p < 0.05). No significant variation was seen between phases. In fat, VNC overestimated the HU relative to unenhanced images. VNC image quality was rated as excellent or good in 84% of arterial phase and 85% of nephrographic phase cases, but only 40% of urographic phase.

CONCLUSION

VNC images derived from novel dual layer spectral detector CT demonstrate attenuation values similar to unenhanced images in all tissues evaluated except for subcutaneous fat. Further study is needed to determine if attenuation thresholds currently used clinically for common pathology should be adjusted, particularly for lesions containing fat.

Dual-Energy CT: What the Neuroradiologist Should Know

Because of the different attenuations of tissues at different energy levels, dual-energy CT offers tissue differentiation and characterization, reduction of artifacts, and remodeling of contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR), hereby creating new opportunities and insights in CT imaging. The applications for dual-energy imaging in neuroradiology are various and still expanding. Automated bone removal is used in CT angiography and CT venography of the intracranial vessels. Monoenergetic reconstructions can be used in patients with or without metal implants in the brain and spine to reduce artifacts, improve CNR and SNR, or to improve iodine conspicuity. Differentiation of iodine and hemorrhage is used in high-density lesions, after intra-arterial recanalization in stroke patients or after administration of contrast media. Detection of underlying (vascular and non-vascular) pathology and spot sign can be used in patients presenting with (acute) intracranial hemorrhage.

Dual-energy CT in the assessment of mediastinal lymph nodes: comparative study of virtual non-contrast and true non-contrast images

Objective

To evaluate the reliability of virtual non-contrast (VNC) images reconstructed from contrast-enhanced, dual-energy scans compared with true non-contrast (TNC) images in the assessment of high CT attenuation or calcification of mediastinal lymph nodes.

Materials and Methods

A total of 112 mediastinal nodes from 45 patients who underwent non-contrast and dual-energy contrast-enhanced scans were analyzed. Node attenuation in TNC and VNC images was compared both objectively, using computed tomography (CT) attenuation, and subjectively, via visual scoring (0, attenuation ≤ the aorta; 1, > the aorta; 2, calcification). The relationship among attenuation difference between TNC and VNC images, CT attenuation in TNC images, and net contrast enhancement (NCE) was analyzed.

Results

CT attenuation in TNC and VNC images showed moderate agreement (intraclass correlation coefficient, 0.612). The mean absolute difference was 7.8 ± 7.6 Hounsfield unit (HU) (range, 0-36 HU), and the absolute difference was equal to or less than 10 HU in 65.2% of cases (73/112). Visual scores in TNC and VNC images showed fair agreement (κ value, 0.335). Five of 16 nodes (31.3%) which showed score 1 (n = 15) or 2 (n = 1) in TNC images demonstrated score 1 in VNC images. The TNC-VNC attenuation difference showed a moderate positive correlation with CT attenuation in TNC images (partial correlation coefficient [PCC] adjusted by NCE: 0.455) and a weak negative correlation with NCE (PCC adjusted by CT attenuation in TNC: -0.245).

Conclusion

VNC images may be useful in the evaluation of mediastinal lymph nodes by providing additional information of high CT attenuation of nodes, although it is underestimated compared with TNC images.