True Noncontrast Images Research Articles

Comparison of virtual and true non-contrast images from dual-layer spectral detector computed tomography (CT) in patients with colorectal cancer.

Colorectal cancer (CRC) is commonly assessed by computed tomography (CT), but the associated radiation exposure is a major concern. This study aimed to quantitatively and qualitatively compare the image quality of virtual non-contrast (VNC) images reconstructed from arterial and portal venous phases with that of true non-contrast (TNC) images in patients with CRC to assess the potential of TNC images to replace VNC images, thereby reducing the radiation dose. A total of 69 patients with postoperative pathologically confirmed CRC at the West China Hospital of Sichuan University between May 2022 and April 2023 were enrolled in this cross-sectional study. The CT protocol included the acquisition of TNC images, arterial and portal venous phase images; the VNC images were reconstructed from the two postcontrast phase images. Several parameters, including the CT attenuation value, absolute attenuation error, imaging noise [standard deviation (SD)], signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were measured in multiple abdominal structures for both the TNC and VNC images. Two blinded readers assessed the subjective image quality using a five-point scale. Interobserver agreement was evaluated using interclass correlation coefficients (ICCs). The paired t-test and Wilcoxon signed-rank test were used to compare the objective and subjective results between the TNC and VNC images. Individual measurements of radiation doses for the TNC scan and contrast scan protocols were recorded. A total of 2,070 regions of interest (ROIs) of the 69 patients were analyzed. Overall, the VNC images exhibited significantly lower attenuation values and SD values than the TNC images in all tissues, except for the abdominal aorta, portal vein, and spleen. The mean absolute attenuation errors between the VNC and TNC images were all less than 10 Hounsfield units (HU). The percentages of absolute attenuation errors less than 5 and 10 HU in the VNC images from the arterial phase (VNCa) were 78.99% and 97.97%, respectively, while those from the portal venous phase (VNCp) were 81.59% and 96.96%, respectively. The absolute attenuation errors between the TNC and VNCa images were smaller than those between the TNC and VNCp images for tumors [VNCaerror: 2.77, interquartile range (IQR) 1.77-4.22; VNCperror: 3.27, IQR 2.68-4.30; P=0.002]. The SNR values and CNR values in the VNC images were significantly higher than those in the TNC images for all tissues, except for the portal vein and spleen. The image quality was rated as excellent (represented by a score of 5) in the majority of the TNC and VNC images; however, the VNC images scored lower than the TNC images. Eliminating the TNC phase resulted in a reduction of approximately 37.99% in the effective dose (ED). The VNC images provided accurate CT attenuation, good image quality, and lower radiation doses than the TNC images in CRC, and the VNCa images showed minimal differences in the CT attenuation of the tumors.

The impact of deep learning image reconstruction of spectral CTU virtual non contrast images for patients with renal stones

PurposeTo compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU). MethodsA retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively. CT value, image quality and kidney stones quantification accuracy were measured and compared among groups. The subjective evaluation was independently assessed by two senior radiologists using the 5-point Likert scale for image quality and lesion visibility. ResultsDH images were superior to AR70 and DM images in objective image quality evaluation. There was no statistical difference in the liver and spleen (both P > 0.05), or within 6HU in renal and fat in CT value between VNC and TNC images. EP-VNC-DH had the lowest image noise, highest SNR, and CNR, and VNC-AR70 images had better noise and SNR performance than TNC-AR70 images (all p < 0.05). EP-VNC-DH had the highest subjective image quality, and CP-VNC-DH performed the best in lesion visibility. In stone CT value and volume measurements, there was no statistical difference between VNC and TNC (P > 0.05). ConclusionThe DLIR-reconstructed VNC images in CTU provide better image quality than the ASIR-V reconstructed TNC images and similar quantification accuracy for kidney stones for potential dose savings.The study highlights that deep learning image reconstruction (DLIR)-reconstructed virtual non-contrast (VNC) images in spectral CT Urography (CTU) offer improved image quality compared to traditional true non-contrast (TNC) images, while maintaining similar accuracy in kidney stone detection, suggesting potential dose savings in clinical practice.

Virtual non-contrast images in photon-counting computed tomography: impact of different contrast phases.

Photon-counting computed tomography (PCCT) enables new ways of image reconstruction, e.g. material decomposition and creation of virtual non-contrast (VNC) series with higher resolution and lower radiation dose than standard computed tomography (CT). Clinical experiences of this are limited. To compare true non-contrast (TNC) series with VNC series derived from non-enhanced (VNCu), arterial phase (VNCa) and portal venous phase (VNCv) in clinically approved PCCT. A total of 45 clinical, tri-phasic abdominal CT scans from the PCCT Naetom Alpha, between February 2022 and November 2022, were retrospectively assessed. Placing a region of interest in six different locations in each VNC series - right liver parenchyma, left liver parenchyma, spleen, aorta, erector spinae muscle, and in the subcutaneous fat - absolute Hounsfield values (HU) and standard deviations (SD) were collected. Differences in HU (ΔHU) were compared and statistically analyzed. Statistically significant differences between VNC and TNC were seen in all measurements, with the largest difference in the subcutaneous fat and the smallest difference in the erector spinae muscle. Only small differences were seen between VNCa and VNCv, where the largest differences were seen in the left and right liver lobes. VNC images from the first-generation clinically approved PCCT showed a significant difference between VNC and TNC images. The differences vary with the type of tissue. Only small differences were seen depending from which contrast phase the VNC was derived.

Automated hepatic steatosis assessment on dual-energy CT-derived virtual non-contrast images through fully-automated 3D organ segmentation

PurposeTo evaluate the efficacy of volumetric CT attenuation-based parameters obtained through automated 3D organ segmentation on virtual non-contrast (VNC) images from dual-energy CT (DECT) for assessing hepatic steatosis.Materials and methodsThis retrospective study included living liver donor candidates having liver DECT and MRI-determined proton density fat fraction (PDFF) assessments. Employing a 3D deep learning algorithm, the liver and spleen were automatically segmented from VNC images (derived from contrast-enhanced DECT scans) and true non-contrast (TNC) images, respectively. Mean volumetric CT attenuation values of each segmented liver (L) and spleen (S) were measured, allowing for liver attenuation index (LAI) calculation, defined as L minus S. Agreements of VNC and TNC parameters for hepatic steatosis, i.e., L and LAI, were assessed using intraclass correlation coefficients (ICC). Correlations between VNC parameters and MRI-PDFF values were assessed using the Pearson’s correlation coefficient. Their performance to identify MRI-PDFF ≥ 5% and ≥ 10% was evaluated using receiver operating characteristic (ROC) curve analysis.ResultsOf 252 participants, 56 (22.2%) and 16 (6.3%) had hepatic steatosis with MRI-PDFF ≥ 5% and ≥ 10%, respectively. LVNC and LAIVNC showed excellent agreement with LTNC and LAITNC (ICC = 0.957 and 0.968) and significant correlations with MRI-PDFF values (r = − 0.585 and − 0.588, Ps < 0.001). LVNC and LAIVNC exhibited areas under the ROC curve of 0.795 and 0.806 for MRI-PDFF ≥ 5%; and 0.916 and 0.932, for MRI-PDFF ≥ 10%, respectively.ConclusionVolumetric CT attenuation-based parameters from VNC images generated by DECT, via automated 3D segmentation of the liver and spleen, have potential for opportunistic hepatic steatosis screening, as an alternative to TNC images.

Virtual Non-Contrast versus True Native in Photon-Counting CT: Stability of Density of Upper Abdominal Organs and Vessels.

The clinical use of photon-counting CT (PCCT) allows for the generation of virtual non-contrast (VNC) series from contrast-enhanced images. In routine clinical practice, specific issues such as ruling out acute bleeding require non-contrast images. The aim of this study is to evaluate the use of PCCT-derived VNC reconstructions in abdominal imaging. PCCT scans of 17 patients including early arterial, portal venous and native sequences were enrolled. VNC reconstructions have been calculated. In every sequence and VNC reconstruction, 10 ROIs were measured (portal vein, descending aorta, inferior vena cava, liver parenchyma, spleen parenchyma, erector spinae muscle, subcutaneous adipose tissue, first lumbar vertebral body, air, and psoas muscle) and density values were compared. The VNC reconstructions show significant changes in density compared to the contrast-enhanced images. However, there were no significant differences present between the true non-contrast (TNC) and any VNC reconstructions in the observed organs and vessels. Significant differences (p < 0.05) between the measured mean density values in the TNC versus VNC reconstructions were found in fat and bone tissue. The PCCT-derived VNC reconstructions seemed to be comparable to the TNC images, despite some deviations shown in the adipose tissue and bone structures. However, the further benefits in terms of specific clinical issues need to be evaluated.

Dual-energy Computed Tomography in Coronary Artery Calcium Scoring

Computed tomographic (CT) coronary artery angiography with calcium scoring is crucial in suspecting coronary artery disease (CAD). The routine protocol for CAD radiological investigation involves a precontrast series for calcium scoring and a postcontrast series for investigating structural changes in coronary arteries. This review intends to investigate the feasibility of virtual noncontrast images to replace the true noncontrast (TNC) images in coronary artery calcium scoring to decrease the radiation dose. The current review included articles found in the PubMed database. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for the further evaluation of the articles for inclusion criteria, 25 articles were finally shortlisted for the final review. It is found that the calcium score of coronary arteries estimated by virtual unenhanced images generated by dual-energy CT (DECT) is comparable with that of TNC images. The virtual unehncanced images has the potential to eliminate the need for the TNC series acquisition, thereby reducing the radiation dose. Especially, photon-counting detector-based DECT has excellent potential in the estimation of calcium scores and the reduction in radiation dose. In addition, contrast dose also can be significantly reduced, which is crucial in patients with renal insufficiency. In conclusion, using DECT for CAD investigations significantly reduces both radiation and contrast doses compared to conventional CT scans. This reduction is particularly crucial for patients who require repeated follow-up scans, as it enhances their safety and minimizes potential risks.

Comparison of Virtual Non-Contrast and True Non-Contrast CT Images Obtained by Dual-Layer Spectral CT in COPD Patients.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of the 300 million CT scans per year are contrast-enhanced, no proper emphysema quantification is available in a one-stop-shop approach for patients with known or newly diagnosed COPD. Since the introduction of spectral imaging (e.g., dual-energy CT scanners), it has been possible to create virtual non-contrast-enhanced images (VNC) from contrast-enhanced images, making it theoretically possible to offer proper COPD imaging despite contrast enhancing. This study is aimed towards investigating whether these VNC images are comparable to true non-contrast-enhanced images (TNC), thereby reducing the radiation exposure of patients and usage of resources in hospitals. In total, 100 COPD patients with two scans, one with (VNC) and one without contrast media (TNC), within 8 weeks or less obtained by a spectral CT using dual-layer technology, were included in this retrospective study. TNC and VNC were compared according to their voxel-density histograms. While the comparison showed significant differences in the low attenuated volumes (LAVs) of TNC and VNC regarding the emphysema threshold of -950 Houndsfield Units (HU), the 15th and 10th percentiles of the LAVs used as a proxy for pre-emphysema were comparable. Upon further investigation, the threshold-based LAVs (-950 HU) of TNC and VNC were comparable in patients with a water equivalent diameter (DW) below 270 mm. The study concludes that VNC imaging may be a viable option for assessing emphysema progression in COPD patients, particularly those with a normal body mass index (BMI). Further, pre-emphysema was generally comparable between TNC and VNC. This approach could potentially reduce radiation exposure and hospital resources by making additional TNC scans obsolete.

Diagnostic performance of photon-counting detector CT for differentiation between adrenal adenomas and metastases

ObjectivesAim of this study was to assess the value of virtual non-contrast (VNC) reconstructions in differentiating between adrenal adenomas and metastases on a photon-counting detector CT (PCD-CT).Material and methodsPatients with adrenal masses and contrast-enhanced CT scans in portal venous phase were included. Image reconstructions were performed, including conventional VNC (VNCConv) and PureCalcium VNC (VNCPC), as well as virtual monochromatic images (VMI, 40–90 keV) and iodine maps. We analyzed images using semi-automatic segmentation of adrenal lesions and extracted quantitative data. Logistic regression models, non-parametric tests, Bland–Altman plots, and a random forest classifier were used for statistical analyses.ResultsThe final study cohort consisted of 90 patients (36 female, mean age 67.8 years [range 39–87]) with adrenal lesions (45 adenomas, 45 metastases). Compared to metastases, adrenal adenomas showed significantly lower CT-values in VNCConv and VNCPC (p = 0.007). Mean difference between VNC and true non-contrast (TNC) was 17.67 for VNCConv and 14.85 for VNCPC. Random forest classifier and logistic regression models both identified VNCConv and VNCPC as the best discriminators. When using 26 HU as the threshold in VNCConv reconstructions, adenomas could be discriminated from metastases with a sensitivity of 86.7% and a specificity of 75.6%.ConclusionVNC algorithms overestimate CT values compared to TNC in the assessment of adrenal lesions. However, they allow a reliable discrimination between adrenal adenomas and metastases and could be used in clinical routine in near future with an increased threshold (e.g., 26 HU). Further (multi-center) studies with larger patient cohorts and standardized protocols are required.Clinical relevance statementVNC reconstructions overestimate CT values compared to TNC. Using a different threshold (e.g., 26 HU compared to the established 10 HU), VNC has a high diagnostic accuracy for the discrimination between adrenal adenomas and metastases.Key Points• Virtual non-contrast reconstructions may be promising tools to differentiate adrenal lesions and might save further diagnostic tests.• The conventional and a new calcium-preserving virtual non-contrast algorithm tend to systematically overestimate CT-values compared to true non-contrast images.• Therefore, increasing the established threshold for true non-contrast images (e.g., 10HU) may help to differentiate between adrenal adenomas and metastases on contrast-enhanced CT.

Coronary Artery Calcium Scoring Using Virtual Versus True Noncontrast Images From Photon-Counting Coronary CT Angiography.

Background Coronary artery calcium scoring (CACS) for coronary artery disease requires true noncontrast (TNC) CT alongside contrast-enhanced coronary CT angiography (CCTA). Photon-counting CT provides an algorithm (PureCalcium) for reconstructing virtual noncontrast images from CCTA specifically for CACS. Purpose To assess CACS differences based on PureCalcium images derived from contrast-enhanced photon-counting CCTA compared with TNC images and evaluate the impact of these differences on the clinically relevant classification of patients into plaque burden groups. Materials and Methods Photon-counting CCTA images acquired between August 2022 and May 2023 were retrospectively identified. Agatston scores were derived from both TNC and PureCalcium images and tested for differences with use of the Wilcoxon signed-rank test. The agreement was assessed with use of equivalence tests, Bland-Altman analysis, and intraclass correlation coefficient. Plaque burden groups were established based on Agatston scores, and agreement was evaluated using weighted Cohen kappa. The dose-length product was analyzed. Results Among 170 patients (mean age, 63 years ± 13 [SD]; 92 male), 111 had Agatston scores higher than 0. Median Agatston scores did not differ between TNC and PureCalcium images (4.8 [IQR, 0-84.4; range, 0.0-2151.8] vs 2.7 [IQR, 0-90.7; range, 0.0-2377.1]; P = .99), with strong correlation (intraclass correlation coefficient, 0.98 [95% CI: 0.97, 0.99]). The equivalence test was inconclusive, with a 95% CI of 0.90, 1.19. Bland-Altman analysis showed wide repeatability limits, indicating low agreement between the two scores. With use of the PureCalcium algorithm, 125 of 170 patients (74%) were correctly classified into plaque burden groups (excellent agreement, κ = 0.88). Patients without plaque burden were misclassified at higher than normal rates (P < .001). TNC image acquisition contributed a mean of 19.7% ± 8.8 of the radiation dose of the entire examination. Conclusion PureCalcium images show potential to replace TNC images for measuring Agatston scores, thereby reducing radiation dose in CCTA. There was strong correlation in calcium scores between TNC and PureCalcium, but limited agreement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Sakuma in this issue.

Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images

A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in abdominal CT, applicable to both non-contrast and post-contrast images. The algorithm was trained using dual-energy CT (DECT)-obtained portal venous phase (PVP) and spatiotemporally-matched virtual non-contrast images, and tested using a single-energy (SE) CT dataset comprising PVP and true non-contrast (TNC) images. The algorithm showed robust accuracy in segmenting the liver, spleen, right kidney (RK), and left kidney (LK), with mean dice similarity coefficients (DSCs) exceeding 0.94 for each organ, regardless of contrast enhancement. However, pancreas segmentation demonstrated slightly lower performance with mean DSCs of around 0.8. In organ volume estimation, the algorithm demonstrated excellent agreement with ground-truth measurements for the liver, spleen, RK, and LK (intraclass correlation coefficients [ICCs] > 0.95); while the pancreas showed good agreements (ICC = 0.792 in SE-PVP, 0.840 in TNC). Accurate volume estimation within a 10% deviation from ground-truth was achieved in over 90% of cases involving the liver, spleen, RK, and LK. These findings indicate the efficacy of our 3D nnU-Net-based algorithm, developed using DECT images, which provides precise segmentation of the liver, spleen, and RK and LK in both non-contrast and post-contrast CT images, enabling reliable organ volumetry, albeit with relatively reduced performance for the pancreas.

Experimental study on the effects of different exposure conditions and contrast agent concentrations on spectral computed tomography virtual non-contrast images.

The early diagnosis of thrombosis and fat embolism is important for subsequent treatment regimens. Spectral computed tomography (CT) virtual non-contrast (VNC) scanning can not only accurately diagnose thrombosis and medium fat embolism but can also reduce the radiation dose and scanning time. However, there is a relative paucity of studies on what contrast concentration and exposure conditions are best for the quality of VNC images. To address this issue, this study aimed to investigate the effects of different exposure conditions and contrast concentrations on the quality of VNC images of low-density substances in spectral CT. Four solution groups [i.e., groups A (15 mgI/mL), B (10 mgI/mL), C (5 mgI/mL), and D (the control group)] were matched with normal saline and contrast agent groups. Four groups of solution, duck blood clots, and fat were injected into four sections of the pig large intestine, respectively. CT scans with different exposure amounts were performed under the condition of 120 KV. Comparing the true non-contrast (TNC) image based on solution D group with the VNC images of the other three solution groups. The differences in the CT values, standard deviation (SD) values, and contrast noise ratio (CNR) values of the duck blood and fat under different iodine concentrations and exposures were compared. The image quality was evaluated using a three-point method and the Kappa consistency test was performed. The consistency of the tissue CT values in the TNC and VNC images was analyzed by drawing Bland-Altman scatter plots. The CT values of the duck blood in the VNC20mAs and VNCC groups were lower than those in the TNC groups (P<0.05). Under different exposures and contrast agent concentrations, the CT value of the fat in the VNC group was higher than that in the TNC group (P<0.05). The SD values of the duck blood and fat in three groups (i.e., groups A, B, and C) were lower than those in the TNC group (P<0.05). The CNR value of the duck blood in the VNC20mAs group was lower than that in the TNC group (Z=-2.10, P=0.04), and the CNR values of the duck blood and fat in the VNC group were higher than those in the TNC groups in the remaining different exposure and concentration groups (P<0.05). The CT values of the lesions in the two groups were consistent, and there were no statistically significant differences between the subjective scores of the TNC and VNC images (z=-1.34, P=0.18); the subjective evaluations of the two physicians had good consistency (K=0.80). Under the conditions of higher contrast agent concentrations and proper exposure conditions, the VNC images were better able to restore the CT values of the blood clots, reduce the SD values of the blood clots and fat. In addition, and improve the CNR values of the blood clots and fat. In addition, the quality of the two images was similar.

Virtual non-contrast images of detector-based spectral computed tomography in dogs: a promising alternative to true non-contrast images in veterinary medicine.

In veterinary medicine, abdominal computer tomographic (CT) examinations regularly require a minimum of two scans, with a native scan (true unenhanced, TUE) as a reference for the subsequent contrast-enhanced CT scan (CECT). Spectral detector CT (SDCT) offers the possibility to calculate virtual non-contrast (VNC) images from the post-contrast scan, but this has not yet been investigated in veterinary medicine. The purpose of this study was to assess the reliability of VNC images for abdominal organs in 44 dogs without abdominal pathologies by evaluating their quantitative and qualitative parameters compared to TUE images. We hypothesized that the subtraction of iodine is sufficient in the VNC series compared to the TUE series and that the image quality of the SDCT series is superior to conventional CT images. Corresponding attenuation values in the VNC and TUE series regarding the regions of interest (ROI) in different parenchymal organs and major vessels of the abdominal cavity were assessed by means of a two one-sided t-test (TOST) and Bland-Altman plots. Additionally, the signal-to-noise ratio (SNR) was calculated for each ROI in the different series. In the second step, two board-certified veterinary radiologists made a qualitative assessment of VNC images vs. TUE images in consensus by rating the iodine subtraction, image noise, and image quality of VNC images based on a specific 5-point Likert scale. The difference in corresponding Hounsfield units (HUs) between TUE and VNC images was less than 10 HU in 78.67% of all ROIs. Regarding the limit of less than 10 HU, in the performed TOST, significant p-values of < 0.05 were reached for the liver, spleen, pancreas, and musculature, implying equivalence of both modalities. The quality of spectral base image (SBI) data was rated equivalent to calculated conventional images in the subjective assessment by reaching an average Likert scale score of 3.2 points. VNC images calculated from SDCT data prove a valid alternative to conventional TUE images in the abdominal organs of canine patients without abdominal pathology. VNC offers the possibility to reduce time under general anesthesia and minimize radiation exposure. Future studies are needed to prove the application of this method in clinically diseased patients.

Coronary calcium scoring on virtual non-contrast and virtual non-iodine reconstructions compared to true non-contrast images using photon-counting computed tomography

ObjectivesTo compare coronary artery calcification (CAC) scores measured on virtual non-contrast (VNC) and virtual non-iodine (VNI) reconstructions computed from coronary computed tomography angiography (CCTA) using photon-counting computed tomography (PCCT) to true non-contrast (TNC) images.MethodsWe included 88 patients (mean age = 59 years ± 13.5, 69% male) who underwent a TNC coronary calcium scan followed by CCTA on PCCT. VNC images were reconstructed in 87 patients and VNI in 88 patients by virtually removing iodine from the CCTA images. For all reconstructions, CAC scores were determined, and patients were classified into risk categories. The overall agreement of the reconstructions was analyzed by Bland–Altman plots and the level of matching classifications.ResultsThe median CAC score on TNC was 27.8 [0–360.4] compared to 8.5 [0.2–101.6] (p < 0.001) on VNC and 72.2 [1.3–398.8] (p < 0.001) on VNI. Bland–Altman plots depicted a bias of 148.8 (ICC = 0.82, p < 0.001) and − 57.7 (ICC = 0.95, p < 0.001) for VNC and VNI, respectively. Of all patients with CACTNC = 0, VNC reconstructions scored 63% of the patients correctly, while VNI scored 54% correctly. Of the patients with CACTNC > 0, VNC and VNI reconstructions detected the presence of coronary calcium in 90% and 92% of the patients. CACVNC tended to underestimate CAC score, whereas CACVNI overestimated, especially in the lower risk categories. According to the risk categories, VNC misclassified 55% of the patients, while VNI misclassified only 32%.ConclusionCompared to TNC images, VNC underestimated and VNI overestimated the actual CAC scores. VNI reconstructions quantify and classify coronary calcification scores more accurately than VNC reconstructions.Clinical relevance statementPhoton-counting CT enables spectral imaging, which might obviate the need for non-contrast enhanced coronary calcium scoring, but optimization is necessary for the clinical implementation of the algorithms.Key Points• Photon-counting computed tomography uses spectral information to virtually remove the signal of contrast agents from contrast-enhanced scans.• Virtual non-contrast reconstructions tend to underestimate coronary artery calcium scores compared to true non-contrast images, while virtual non-iodine reconstructions tend to overestimate the calcium scores.• Virtual non-iodine reconstructions might obviate the need for non-contrast enhanced calcium scoring, but optimization is necessary for the clinical implementation of the algorithms.

Dual-layer spectral computed tomography aortography using a seventy-five-percent-reduced iodine dose protocol and multiparameter spectral imaging: comparison with conventional computed tomography imaging.

Computed tomography angiography (CTA) is the recommended diagnostic and follow-up imaging modality for acute aortic dissection (AD). However, the high-contrast medium burden associated with repeated CT aortography follow-ups remains a significant concern. This prospective study aimed to assess whether an ultra-low contrast dose (75% cutoff) aortic CTA protocol on dual-layer spectral CT could achieve comparable image quality with the full dose protocol. We also investigated the image quality of the virtual noncontrast (VNC) images derived from the ultra-low dose protocol. This study included 37 consecutive patients who were referred to aortic CTA from May 2022 to August 2022. The enrolled patients underwent full-dose contrast CTA and ultra-low dose (reduced to 25% of conventional) contrast CTA on dual-layer spectral CT in 1 day. Virtual monochromatic images (VMIs) were reconstructed with 40 and 70 keV. The VNC images were reconstructed for both protocols. Objective image quality evaluation, recorded as signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs), was compared between the groups using 1-way analysis of variance and post hoc analysis with Bonferroni correction. Subjective image quality was also compared between the groups. Finally, VNC images derived from the low-dose (VNClow) and full-dose (VNCfull) protocols were compared to the true noncontrast (TNC) images. Neither CNR nor SNR was lower for the 40-keV images reconstructed from the ultra-low dose group compared to the conventional images. Both were significantly higher than those of the 70-keV images. Regarding subjective image quality, vessel enhancement was not significantly different between the 40-keV VMI and full-dose images [ascending aorta (AAO): 4.37±0.46 vs. 4.57±0.48, P=0.096; brachiocephalic arteries: 4.34±0.45 vs. 4.51±0.49, P=0.152; abdominal aortic side branch: 4.42±0.48 vs. 4.51±0.49, P=0.480]. The VNClow images were similar to the TNC images but significantly different from the VNCfull images (P<0.001). Ultra-low contrast aortic CTA with a 75%-reduced iodine dose using dual-layer spectral CT and the derived VNC achieved image quality comparable to that of conventional CTA and TNC images.

Multiphase photon counting detector CT data sets – Which combination of contrast phase and virtual non-contrast algorithm is best suited to replace true non-contrast series in the assessment of active bleeding?

PurposeAim of this study was to determine which virtual non-contrast (VNC) reconstruction algorithm, applied to which contrast phase of computed tomography angiography, best matches true non-contrast (TNC) images in the assessment of active bleeding. MethodPatients who underwent a triphasic scan (pre-contrast, arterial, portal venous contrast) on a photon-counting detector CT (PCD-CT) (120 kV, image quality level 68) with suspected active (tumor, postoperative, spontaneous or other) bleeding were retrospectively included in this study. Conventional (VNCConv) and a calcium-preserving VNC algorithm (VNCPC) were derived from both arterial (art) and portal venous (pv) contrast scans, and analyzed quantitatively and qualitatively by two independent and blinded raters. Results40 patients (22 female, mean age 76 years) were included. Measurements of CT values showed significant albeit small differences between TNC and VNC for most analyzed tissue regions without clear superiority of a VNC algorithm or contrast phase (e.g. ΔHU fat TNC to VNCPCpv 3.1 HU). However, qualitative analysis showed a preference to VNCPCpv in terms of image quality (on a 5-point Likert scale VNCConvart = 3.5 ± 0.8, VNCPCart = 3.7 ± 0.7, VNCConvpv = 3.7 ± 0.7, VNCPCpv = 3.8 ± 0.7) and residual calcium contrast (VNCConvart = 3.0 ± 0.8, VNCPCart = 3.5 ± 0.7, VNCConvpv = 3.6 ± 0.7, VNCPCpv = 3.9 ± 0.6). ConclusionsWhen multiple post-contrast phases are available, VNCPC series based on portal venous phase are the most suitable replacement for an additional pre-contrast scan, with the prospect of a significant reduction in patient radiation dose.

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

IntroductionObtaining 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. MethodsCCTA 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. ResultsWhile 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). ConclusionThe 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.

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

PurposePurpose 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. MethodsFifty-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. ResultsAttenuation 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%. ConclusionAlthough 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.

Can virtual non-contrast imaging replace true non-contrast imaging in multiphase scanning of the neck region?

Dual-energy computed tomography (DECT) is an advanced imaging method that enables reconstruction of virtual non-contrast (VNC) images from a contrast-enhanced acquisition. This has the potential to reduce radiation exposure by eliminating the need for a true non-contrast (TNC) phase. The purpose is to evaluate the feasibility of VNC images in the neck region. A total of 100 patients underwent a DECT scan as part of diagnostic workup of primary hyperparathyroidism. VNC images were reconstructed from 30s (arterial) and 50s (venous) post-contrast scans. Regions of interest (ROIs) were placed in thyroid tissue, lymph node, carotid artery, jugular vein, fat, and sternocleidomastoid muscle. Mean densities of all anatomical structures were compared between VNC and TNC images. For all anatomical structures except the thyroid gland, the difference in mean density between TNC and VNC images was less than 15 HU. The mean difference in density between TNC and VNC images of the thyroid was 53.2 HU (95% CI 46.8; 59.6, p = <0.001). This study demonstrated an acceptable agreement in density between true non-contrast and virtual non-contrast images for most anatomical structures in the neck region. Therefore, VNC images may have the potential to replace TNC images in the neck. However, due to significant differences in CT density of thyroid tissue, true non-contrast imaging cannot be directly substituted by virtual non-contrast imaging when examining the thyroid and its surrounding tissue.

Virtual Non-Contrast Spectral CT in Renal Masses: Is It Time to Discard Conventional Unenhanced Phase?

Dual-layer Dual-Energy CT (dl-DECT) allows one to create virtual non-contrast (VNC) reconstructions from contrast-enhanced CT scans, with a consequent decrease of the radiation dose. This study aims to assess the reliability of VNC for the diagnostic evaluation of renal masses in comparison with true non-contrast (TNC) images. The study cohort included 100 renal masses in 40 patients who underwent dl-DECT between June and December 2021. Attenuation values and standard deviations were assessed through the drawing of regions of interest on TNC and VNC images reconstructed from corticomedullary and nephrographic phases. A Wilcoxon signed-rank test was performed in order to assess equivalence of data and Spearman's Rho correlation coefficient to evaluate correlations between each parameter. The diagnostic accuracy of VNC was estimated through the performance of receiver operating characteristic (ROC) curve analysis. Differences between attenuation values were, respectively, 74%, 18%, 5% and 3% (TNC-VNCcort), and 74%, 15%, 9% and 2% (TNC-VNCneph). The Wilcoxon signed-rank test demonstrated the equivalence of attenuation values between the TNC and VNC images. The diagnostic performance of VNC images in the depiction of kidney simple cysts remains high compared to TNC (VNCcort-AUC: 0.896; VNCneph-AUC: 0.901, TNC-AUC: 0.903). In conclusion, quantitative analysis of attenuation values showed a strong agreement between VNC and TNC images in the evaluation of renal masses.

Comparison of true non-contrast and virtual non-contrast images in the characterization of renal lesions using detector-based spectral CT.

Renal lesions are sometimes incidentally detected during computed tomography (CT) examinations in which an unenhanced series is not included, preventing the lesions from being fully characterized. The aim of this study was to investigate the feasibility to use virtual non-contrast (VNC) images, acquired using a detector-based dual-energy CT, for the characterization of renal lesions. Twenty-seven patients (12 women) underwent a renal CT scan, including a non-contrast, an arterial, and a venous phase contrast-enhanced series, using a detector-based dual-energy CT scanner. VNC images were reconstructed from the venous contrast-enhanced series. The mean attenuation values of 65 renal lesions in both the VNC and true non-contrast (TNC) images were measured and compared quantitatively. Three radiologists blindly assessed all lesions using either VNC or TNC images in combination with contrast-enhanced images. Sixteen patients had cystic lesions, five had angiomyolipoma (AML), and six had suspected renal cell carcinomas (RCC). Attenuation values in VNC and TNC images were strongly correlated (ρ = 0.7, mean difference -6.0 ± 13 HU). The largest differences were found for unenhanced high-attenuation lesions. Radiologists classified 86% of the lesions correctly using VNC images. In 70% of the patients, incidentally detected renal lesions could be accurately characterized using VNC images, resulting in less patient burden and a reduction in radiation exposure. This study shows that renal lesions can be accurately characterized using VNC images acquired by detector-based dual-energy CT, which is in agreement with previous studies using dual-source and rapid X-ray tube potential switching technique.