Virtual Non-contrast Research Articles

Validation of quantitative aortic valve calcification assessment by photon-counting computed tomography from contrast-enhanced data sets using virtual non-contrast reconstructions

Abstract Background The spectral information of photon-counting computed tomography (PCCT) permits the reconstruction of energy-selective images. Purpose We evaluated two methods for the reconstruction of virtual non-contrast-enhanced images from contrast-enhanced CT angiographic data sets regarding their ability to quantify aortic valve calcification. Results were validated by comparison to non-enhanced computed tomography. Methods A total of 100 subjects with severe aortic valve stenosis who underwent ECG-synchronized cardiac photon-counting CT for diagnostic workup prior to transcatheter aortic valve implantation (TAVI) were included in the current analysis. A standard true non-contrast acquisition was performed for aortic valve calcium quantification in each subject. In addition, two virtual non-enhanced data sets were reconstructed from contrast-enhanced CT angiographic data: a calcium-specific image ("pure calcium scan", PCS) and a virtual non-contrast (VNC) scan by subtraction of iodine. The PCS algorithm separates calcium and iodine in spectral PCCT images, creating a non-calcium mask for denoised spectral input and preserving calcium contrast in final material decomposition. Iodine extraction is achieved by measurement of the iodine content and its differentiation from native soft tissue attenuation. The Agatston Score was assessed in each of the three image data sets using semi-automated software. Results The mean age of the subjects was 78 ± 7 years, 66% were male. Mean EUROSCORE II was 4 ± 4%, mean STS Score for morbidity and mortality was 16 ± 11%. In the true non-contrast scan used for validation, the mean Agatston Score was 3028 ± 1820. The mean Agatston Score obtained from the PCS data set was 3004 ± 2042 and did not differ significantly from true non—contrast image sets (p=0.716). In the VNC data sets, the mean Agatston score was 1726 ± 1288, significantly lower than in the true non-contrast and the PCS data sets (both <0.001). Both, PCS and VNC data showed a significant correlation with true non-contrast scans (correlation coefficient PCS: 0.978, VNC scan: 0.975, both p<0.001). Conclusion In conclusion, the current results show the possibility to reliably quantify the Agatston Score of larger cardiac calcifications in virtual non-contrast data sets – however, there is substantial influence of the algorithm that is used so that validation of new techniques is of key importance.

The reliability of virtual non-contrast reconstructions of photon-counting detector CT scans in assessing abdominal organs

BackgroundSpectral imaging of photon-counting detector CT (PCD-CT) scanners allows for generating virtual non-contrast (VNC) reconstruction. By analyzing 12 abdominal organs, we aimed to test the reliability of VNC reconstructions in preserving HU values compared to real unenhanced CT images.MethodsOur study included 34 patients with pancreatic cystic neoplasm (PCN). The VNC reconstructions were generated from unenhanced, arterial, portal, and venous phase PCD-CT scans using the Liver-VNC algorithm. The observed 11 abdominal organs were segmented by the TotalSegmentator algorithm, the PCNs were segmented manually. Average densities were extracted from unenhanced scans (HUunenhanced), postcontrast (HUpostcontrast) scans, and VNC reconstructions (HUVNC). The error was calculated as HUerror=HUVNC–HUunenhanced. Pearson’s or Spearman’s correlation was used to assess the association. Reproducibility was evaluated by intraclass correlation coefficients (ICC).ResultsSignificant differences between HUunenhanced and HUVNC[unenhanced] were found in vertebrae, paraspinal muscles, liver, and spleen. HUVNC[unenhanced] showed a strong correlation with HUunenhanced in all organs except spleen (r = 0.45) and kidneys (r = 0.78 and 0.73). In all postcontrast phases, the HUVNC had strong correlations with HUunenhanced in all organs except the spleen and kidneys. The HUerror had significant correlations with HUunenhanced in the muscles and vertebrae; and with HUpostcontrast in the spleen, vertebrae, and paraspinal muscles in all postcontrast phases. All organs had at least one postcontrast VNC reconstruction that showed good-to-excellent agreement with HUunenhanced during ICC analysis except the vertebrae (ICC: 0.17), paraspinal muscles (ICC: 0.64–0.79), spleen (ICC: 0.21–0.47), and kidneys (ICC: 0.10–0.31).ConclusionsVNC reconstructions are reliable in at least one postcontrast phase for most organs, but further improvement is needed before VNC can be utilized to examine the spleen, kidneys, and vertebrae.

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.

Improved Detection of Small and Low-Density Plaques in Virtual Noncontrast Imaging-based Calcium Scoring at Photon-Counting Detector CT.

Purpose To investigate the impact of plaque size and density on virtual noncontrast (VNC)-based coronary artery calcium scoring (CACS) using photon-counting detector CT and to provide safety net reconstructions for improved detection of subtle plaques in patients whose VNC-based CACS would otherwise be erroneously zero when compared with true noncontrast (TNC)-based CACS. Materials and Methods In this prospective study, CACS was evaluated in a phantom containing calcifications with different diameters (5, 3, and 1 mm) and densities (800, 400, and 200 mg/cm3) and in participants who underwent TNC and contrast-enhanced cardiac photon-counting detector CT (July 2021-March 2022). VNC images were reconstructed at different virtual monoenergetic imaging (55-80 keV) and quantum iterative reconstruction (QIR) levels (QIR,1-4). TNC scans at 70 keV with QIR off served as the reference standard. In vitro CACS was analyzed using standard settings (3.0-mm sections, kernel Qr36, 130-HU threshold). Calcification detectability and CACS of small and low-density plaques were also evaluated using 1.0-mm sections, kernel Qr44, and 120- or 110-HU thresholds. Safety net reconstructions were defined based on background Agatston scores and evaluated in vivo in TNC plaques initially nondetectable using standard VNC reconstructions. Results The in vivo cohort included 63 participants (57.8 years ± 15.5 [SD]; 37 [59%] male, 26 [41%] female). Correlation and agreement between standard CACSVNC and CACSTNC were higher in large- and medium-sized and high- and medium-density than in low-density plaques (in vitro: intraclass correlation coefficient [ICC] ≥ 0.90; r > 0.9 vs ICC = 0.20-0.48; r = 0.5-0.6). Small plaques were not detectable using standard VNC reconstructions. Calcification detectability was highest using 1.0-mm sections, kernel Qr44, 120- and 110-HU thresholds, and QIR level of 2 or less VNC reconstructions. Compared with standard VNC, using safety net reconstructions (55 keV, QIR 2, 110-HU threshold) for in vivo subtle plaque detection led to higher detection (increased by 89% [50 of 56]) and improved correlation and agreement of CACSVNC with CACSTNC (in vivo: ICC = 0.51-0.61; r = 0.6). Conclusion Compared with TNC-based calcium scoring, VNC-based calcium scoring was limited for small and low-density plaques but improved using safety net reconstructions, which may be particularly useful in patients with low calcium scores who would otherwise be treated based on potentially false-negative results. Keywords: Coronary Artery Calcium CT, Photon-Counting Detector CT, Virtual Noncontrast, Plaque Size, Plaque Density Supplemental material is available for this article. © RSNA, 2024.

Safety Net Reconstruction to Catch Low-Density and Low-Volume Calcifications at Photon-Counting Detector CT Using Virtual Noncontrast Imaging.

Safety Net Reconstruction to Catch Low-Density and Low-Volume Calcifications at Photon-Counting Detector CT Using Virtual Noncontrast Imaging.

Dual-Energy Computed Tomography in Urological Diseases: A Narrative Review.

Dual-Energy computed tomography (DECT) with its various advanced techniques, including Virtual Non-Contrast (VNC), effective atomic number (Z-eff) calculation, Z-maps, Iodine Density Index (IDI), and so on, holds great promise in the diagnosis and management of urogenital tumours. In this narrative review, we analyze the current status of knowledge of this technology to provide better lesion characterization, improve the staging accuracy, and give more precise treatment response assessments in relation to urological tumours.

Synthetic hematocrit from virtual non-contrast images for myocardial extracellular volume evaluation with photon-counting detector CT.

To assess the accuracy of a synthetic hematocrit derived from virtual non-contrast (VNC) and virtual non-iodine images (VNI) for myocardial extracellular volume (ECV) computation with photon-counting detector computed tomography (PCD-CT). Consecutive patients undergoing PCD-CT including a coronary CT angiography (CCTA) and a late enhancement (LE) scan and having a blood hematocrit were retrospectively included. In the first 75 patients (derivation cohort), CCTA and LE scans were reconstructed as VNI at 60, 70, and 80 keV and as VNC with quantum iterative reconstruction (QIR) strengths 2, 3, and 4. Blood pool attenuation (BPmean) was correlated to blood hematocrit. In the next 50 patients (validation cohort), synthetic hematocrit was calculated using BPmean. Myocardial ECV was computed using the synthetic hematocrit and compared with the ECV using the blood hematocrit as a reference. In the derivation cohort (49 men, mean age 79 ± 8 years), a correlation between BPmean and blood hematocrit ranged from poor for VNI of CCTA at 80 keV, QIR2 (R2 = 0.12) to moderate for VNI of LE at 60 keV, QIR4; 70 keV, QIR3 and 4; and VNC of LE, QIR3 and 4 (all, R2 = 0.58). In the validation cohort (29 men, age 75 ± 14 years), synthetic hematocrit was calculated from VNC of the LE scan, QIR3. Median ECV was 26.9% (interquartile range (IQR), 25.5%, 28.8%) using the blood hematocrit and 26.8% (IQR, 25.4%, 29.7%) using synthetic hematocrit (VNC, QIR3; mean difference, -0.2%; limits of agreement, -2.4%, 2.0%; p = 0.33). Synthetic hematocrit calculated from VNC images enables an accurate computation of myocardial ECV with PCD-CT. Virtual non-contrast images from cardiac late enhancement scans with photon-counting detector CT allow the calculation of a synthetic hematocrit, which enables accurate computation of myocardial extracellular volume. Blood hematocrit is mandatory for conventional myocardial extracellular volume computation. Synthetic hematocrit can be calculated from virtual non-iodine and non-contrast photon-counting detector CT images. Synthetic hematocrit from virtual non-contrast images enables computation of the myocardial extracellular volume.

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 noncontrast images reveal gouty tophi in contrast-enhanced dual-energy CT: a phantom study

BackgroundDual-energy computed tomography (DECT) is useful for detecting gouty tophi. While iodinated contrast media (ICM) might enhance the detection of monosodium urate crystals (MSU), higher iodine concentrations hamper their detection. Calculating virtual noncontrast (VNC) images might improve the detection of enhancing tophi. The aim of this study was to evaluate MSU detection with VNC images from DECT acquisitions in phantoms, compared against the results with standard DECT reconstructions.MethodsA grid-like and a biophantom with 25 suspensions containing different concentrations of ICM (0 to 2%) and MSU (0 to 50%) were scanned with sequential single-source DECT using an ascending order of tube current time product at 80 kVp (16.5–220 mAs) and 135 kVp (2.75–19.25 mAs). VNC images were equivalently reconstructed at 80 and 135 kVp. Two-material decomposition analysis for MSU detection was applied for the VNC and conventional CT images. MSU detection and attenuation values were compared in both modalities.ResultsFor 0, 0.25, 0.5, 1, and 2% ICM, the average detection indices (DIs) for all MSU concentrations (35–50%) with VNC postprocessing were respectively 25.2, 36.6, 30.9, 38.9, and 45.8% for the grid phantom scans and 11.7, 9.4, 5.5, 24.0, and 25.0% for the porcine phantom scans. In the conventional CT image group, the average DIs were respectively 35.4, 54.3, 45.4, 1.0, and 0.0% for the grid phantom and 19.4, 17.9, 3.0, 0.0, and 0.0% for the porcine phantom scans.ConclusionsVNC effectively reduces the suppression of information caused by high concentrations of ICM, thereby improving the detection of MSU.Relevance statementContrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition.Key points• Highly concentrated contrast media hinders monosodium urate crystal detection in CT imaging• Virtual noncontrast imaging redetects monosodium urate crystals in high-iodinated contrast media concentrations.• Contrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition.Graphical

Differentiation Between Abscesses and Unnecessary Intervention Fluid After Pancreas Surgery Using Dual-Energy Computed Tomography.

This study aimed to evaluate the potential of dual-energy computed tomography (CT) to distinguish postoperative ascites, pancreatic fistula, and abscesses. Patients who underwent biliary and pancreatic surgery performed at our institution between June 2021 and February 2022 were included in the study. Postoperative body fluid samples were collected through a drain or percutaneous drainage. These samples were set in a phantom, and imaging data were obtained using dual-energy CT. Image analysis was performed to obtain CT values at each energy in virtual monoenergetic images (VMIs), effective atomic number, iodine map, and virtual non-contrast (VNC) images. VMIs were calculated from 80 and 140 kVp tube data at 10 kV each from 40-140 kV. Additionally, the effective atomic number, iodine map, and VNC images were reconstructed from the material decomposition process using water and iodine as the base material pair. In this study, 25 patients (eight with abscess and 17 with ascites) were included. No significant association was observed between the presence or absence of abscess and malignancy or surgical procedure. The intervention was performed in six of the eight patients with abscesses. In contrast, five of the 17 patients with postoperative ascites required intervention. A significant relationship was observed between the intervention and the presence of an abscess. Significant differences in C-reactive protein values and the incidence of fever were observed between the groups. Only VNC showed a significant difference between the groups. VNC using dual-energy CT could differentiate abscesses from postoperative fluid.

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.

Utility of Dual-Energy Computed Tomography in Clinical Conundra.

Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems.

CT Urology에서 가돌리늄 조영제 제거를 위한 이중에너지 CT의 Virtual Non-Contrast 기술의 평가

Residual gadolinium contrast agents in the urinary tract following MRI contrast-enhanced examinations often presents as elevated Hounsfield Unit (HU) values on CT scans, impeding the efficacy of CT urology procedures. This study aims to explore the feasibility of addressing this challenge through the application of the Dual-Energy Computed Tomography (DECT) Virtual Non-Contrast (VNC) technique across various energy levels. HU values of residual gadolinium contrast agent in the renal pelvis and ureter were measured in patients who underwent MRI contrast-enhanced examinations followed by non-contrast CT chest examinations. Subsequently, gadolinium contrast agent mixtures, such as Max, Mean, and Min Mixtures, were created and scanned together with normal saline using DECT at various energy levels. Additionally, an iodine mixture and a virtual stone representing urolithiasis were fabricated and evaluated. The results demonstrated a significant reduction in contrast enhancement of gadolinium contrast agent with DECT VNC at 70 kV and Sn150 kV. The Min Mixture closely resembled normal saline in both HU and Contrast-to-Noise Ratio (CNR), facilitating the differentiation between iodine contrast agent and virtual urolithiasis. Therefore, utilizing DECT VNC after a 1-hour and 30-minute interval following MRI contrast-enhanced examinations effectively eliminates gadolinium contrast agents, improving the efficacy of CT Urology imaging procedures.

Potential of photon counting computed tomography derived spectral reconstructions to reduce beam-hardening artifacts in chest CT

PurposeAim of the recent study is to point out a method to optimize quality of CT scans in oncological patients with port systems. This study investigates the potential of photon counting computed tomography (PCCT) for reduction of beam hardening artifacts caused by port-implants in chest imaging by means of spectral reconstructions. MethodIn this retrospective single-center study, 8 ROIs for 19 spectral reconstructions (polyenergetic imaging, monoenergetic reconstructions from 40 to 190 keV as well as iodine maps and virtual non contrast (VNC)) of 49 patients with pectoral port systems undergoing PCCT of the chest for staging of oncologic disease were measured. Mean values and standard deviation (SD) Hounsfield unit measurements of port-chamber associated hypo- and hyperdense artifacts, bilateral muscles and vessels has been carried out. Also, a structured assessment of artifacts and imaging findings was performed by two radiologists. ResultsA significant association of keV with iodine contrast as well as artifact intensity was noted (all p < 0.001). In qualitative assessment, utilization of 120 keV monoenergetic reconstructions could reduce severe and pronounced artifacts completely, as compared to lower keV reconstructions (p < 0.001). Regarding imaging findings, no significant difference between monoenergetic reconstructions was noted (all p > 0.05). In cases with very high iodine concentrations in the subclavian vein, image distortions were noted at 40 keV images (p < 0.01). ConclusionsThe present study demonstrates that PCCT derived spectral reconstructions can be used in oncological imaging of the thorax to reduce port-derived beam-hardening artefacts. When evaluating image data sets within a staging, it can be particularly helpful to consider the 120 keV VMIs, in which the artefacts are comparatively low.

PET/SPECT/Spectral-CT/CBCT imaging in a small-animal radiation therapy platform: A Monte Carlo study-Part II: Biologically guided radiotherapy.

This study addresses the technical gap between clinical radiation therapy (RT) and preclinical small-animal RT, hindering the comprehensive validation of innovative clinical RT approaches in small-animal models of cancer and the translation of preclinical RT studies into clinical practices. The main aim was to explore the feasibility of biologically guided RT implemented within a small-animal radiation therapy (SART) platform, with integrated quad-modal on-board positron emission tomography (PET), single-photon emission computed tomography, photon-counting spectral CT, and cone-beam CT (CBCT) imaging, in a Monte Carlo model as a proof-of-concept. We developed a SART workflow employing quad-modal imaging guidance, integrating multimodal image-guided RT and emission-guided RT (EGRT). The EGRT algorithm was outlined using positron signals from a PET radiotracer, enabling near real-time adjustments to radiation treatment beams for precise targeting in the presence of a 2-mm setup error. Molecular image-guided RT, incorporating a dose escalation/de-escalation scheme, was demonstrated using a simulated phantom with a dose painting plan. The plan involved delivering a low dose to the CBCT-delineated planning target volume (PTV) and a high dose boosted to the highly active biological target volume (hBTV) identified by the 18F-PET image. Additionally, the Bayesian eigentissue decomposition method illustrated the quantitative decomposition of radiotherapy-related parameters, specifically iodine uptake fraction and virtual noncontrast (VNC) electron density, using a simulated phantom with Kidney1 and Liver2 inserts mixed with an iodine contrast agent at electron fractions of 0.01-0.02. EGRT simulations generated over 4,000 beamlet responses in dose slice deliveries and illustrated superior dose coverage and distribution with significantly lower doses delivered to normal tissues, even with a 2-mm setup error introduced, demonstrating the robustness of the novel EGRT scheme compared to conventional image-guided RT. In the dose-painting plan, doubling the dose to the hBTV while maintaining a low dose for the PTV resulted in an organ-at-risk (OAR) dose comparable to the low-dose treatment for the PTV alone. Furthermore, the decomposition of radiotherapy-related parameters in Kidney1 and Liver2 inserts, including iodine uptake fractions and VNC electron densities, exhibited average relative errors of less than 1.0% and 2.5%, respectively. The results demonstrated the successful implementation of biologically guided RT within the proposed quad-model image-guided SART platform, with potential applications in preclinical RT and adaptive RT studies.

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.

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

PurposeTo 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 methodsWe 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.Results19 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.ConclusionPCD-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.

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.