Search Results (55)

Background/Objectives: Dual-energy CT (DECT) enables iodine quantification as a snapshot perfusion indicator. Understanding pulmonary iodine distribution in lung-healthy individuals is crucial for clinical applications. This study aimed to automate iodine quantification and assess demographic effects in a lung-healthy reference cohort. Methods: This retrospective cohort study included 112 adults (53% female, mean age 60.3 ± 16.6 years) who underwent repeated portal venous phase chest DECT on a spectral detector dual-layer scanner between 2016 and 2019 at an academic medical center. Patients had dermato-oncological diseases but no visible thoracic tumors. Automatic lung volumetry was merged with reconstructed iodine maps to assess volume and mean iodine concentrations of each lung lobe. Pulmonary iodine perfusion ratios (PIPRs) were calculated by normalizing the pulmonary iodine density against iodine concentration in the portal vein and the main pulmonary artery (mPA). Results: Mean lung volume (f: 3.9 L vs. m: 5.2 L) and iodine concentration (f: 0.87 mg/mL vs. m: 0.69 mg/mL) differed between ages. However, no difference was observed when comparing PIPRs after normalizing against the iodine level in the mPA. PIPR_mPA were consistent across two timepoints (r = 0.88) and decreased with increasing age (≤50 years: 0.18 vs. ≥70 years: 0.15). Conclusions: This study demonstrates that automated pulmonary iodine quantification is feasible. Normalized pulmonary iodine concentration is a more reliable and effective method for evaluating iodine distribution. Our study also highlights the need to account for sex and age variations in future research and clinical applications. Full article

Background/Objectives: Thrombus composition influences the success of endovascular therapy in stroke, but conventional CT is limited in determining it. Spectral-detector-CT (SDCT) can apply material-decomposition and virtual monoenergetic (MonoE) imaging, which may provide a way to gain information on thrombus composition. This experimental study aimed to evaluate the differentiability of heterogeneous thrombi with variable red blood cell (RBC) content using SDCT. Methods: Ten thrombus entities with different compositions on RBC and plasma, thus fibrin content, were manufactured (volumetric RBC%/Plasma% = 90/10; 80/20; 70/30; 60/40; 50/50; 40/60; 30/70; 20/80; 10/90; 5/95) and scanned in an SDCT. Conventional Hounsfield-unit (HU) values, spectral electron density (ED), effective atomic number (Z-effective) and HU in MonoE maps ranging from 40– to 200 keV were evaluated for thrombus differentiation. Results: Conventional HU increased with RBC content, allowing us to differentiate the entities (p < 0.001). ED values also increased with RBC content and allowed for differentiation too (p < 0.001). Z-effective values showed no differences among the different entities (p > 0.05). Regarding the mass-attenuation curves from 40 to 200 keV the different thrombi showed a similar curve progression with highest HU values at 40 and lowest at 200 keV. The thrombi could be distinguished overall at each monoenergetic level by HU (p < 0.001 for each level). The absolute decrease in HU between 40 and 200 keV was thereby not significantly different between the different entities, but the relative decrease was, as it was more pronounced in thrombi with lower RBC content (p < 0.001). Conclusions: Spectral CT enables differentiation between thrombi with different RBC and fibrin contents by means of ED or analysis of the mass-attenuation curve. This offers alternative possibilities that go beyond characterisation based on CT-density alone. The additional inclusion of spectral parameters in thrombus diagnostics could therefore improve diagnosis and treatment. Full article

Spectral detector computed tomography (SDCT) is an innovative imaging technique in veterinary medicine that utilises simultaneous data acquisition at different energy levels using two rows of detectors. This technique provides several interesting applications which improve insights into tissue composition. One implementation is the generation of virtual non-contrast (VNC) images from post-contrast spectral CT data by identifying and subtracting iodine pixels. Preliminary studies suggest that VNC images may offer diagnostic quality comparable to true unenhanced (TUE) images in healthy dogs; however, this technique has yet to be evaluated in clinical patients. This study compared the Hounsfield units (HUs) measured in VNC to those in TUE images of canine hepatic pathologies, taking into account specific types of pathologies based on their imaging characteristics. The attenuation values of the VNC and TUE series were analysed using two one-sided t-tests (TOST), and the signal-to-noise ratio (SNR) was calculated for each region of interest (ROI). A 5-point Likert scale was utilised to assess image noise, quality, and iodine subtraction in the VNC images. A total of 287 ROIs were analysed in the liver, gallbladder, paravertebral muscle, and pancreatic body of 28 dogs with histopathologically confirmed hepatic pathologies. 92.61% of the hepatic ROIs displayed a “negligible” difference of ≤10 HUs between VNC and TUE images, with significant p-values of <0.05 maintained for all ROIs within the limit of ≤10 HUs in the TOST, confirming equivalence between the two imaging modalities. The image quality assessment indicated that SDCT-derived images provided equal or superior quality compared to conventional CT. Therefore, it can be concluded that VNC images calculated from SDCT data could be an alternative to conventional TUE images for hepatic pathologies. Full article

Objectives: The BRAF^V600E is the most common oncogene in thyroid cancer and is associated with the aggressiveness of papillary thyroid carcinoma (PTC). The aim of this study was to investigate the effectiveness of the arterial enhancement fraction (AEF) and dual-layer detector spectral computed tomography (DLCT) parameters for predicting the BRAF^V600E mutation in PTC. Methods: A total of 237 patients with PTC who underwent DLCT and BRAF^V600E mutation detection (mutant group: n = 187; wild group: n = 50) were retrospectively reviewed. The receiver operating characteristic curves evaluated the effectiveness of the prediction models based on the significantly different variables using logistic regression analysis. The nomogram of the prediction model with the highest AUC in the validation cohort was constructed. Results: The AUCs of the DLCT+ Hashimoto’s thyroiditis (HT) and AEF + DLCT + HT prediction models were 0.901 and 0.896, respectively, in the training cohort and 0.801 and 0.853 in the validation cohort. The calibration curve revealed the good agreement between the prediction results and the actual observations using the AEF + DLCT + HT model. The DCA demonstrated that the model can provide net benefit for all threshold probabilities. Conclusions: As an effective and visually noninvasive prediction tool, the AEF + DLCT + HT-based nomogram presented satisfactory effectiveness in preoperatively predicting the BRAF^V600E mutation in PTC. Full article

Background: Computed tomography (CT)-based image-guided surgery (IGS) is of great importance in functional endoscopic sinus surgery (FESS) and requires IGS-specific imaging protocols to ensure high intraoperative accuracy. This study aimed to compare photon-counting CT (PCCT), dual-energy dual-source CT (DECT), and spectral detector CT (SDCT) of the paranasal sinuses with respect to image quality, IGS accuracy and radiation dose. Methods: A formalin-fixed cadaver skull was examined using PCCT, DECT and SDCT at 100 kV tube voltage with descending tube currents (mAs). The setup of electromagnetic IGS was evaluated using a visual analog scale. Accuracy was analyzed endoscopically using defined anatomical landmarks. Diagnostic image quality as well as bone and soft tissue noise were assessed qualitatively using a 5-point Likert scale and quantitatively by determination of signal-to-noise ratio. Radiation dose was evaluated using the dose length product. Results: While PCCT datasets could be registered and navigated accurately down to 10 mAs (1.5 mm error at 10 mAs), both DECT and SDCT exhibited significantly increased inaccuracies below 40 mAs (4.35/5.15 mm for DECT/SDCT at 25 mAs). Using PCCT therefore enabled a 45% radiation dose reduction at the minimally required dose length product using PCCT. Quantitative and qualitative image quality were superior for PCCT compared to DECT and SDCT. Conclusions: PCCT provides excellent accuracy of anatomical landmarks in IGS with superior image quality of the paranasal sinuses in low-mA scans and substantially reduced radiation exposure. Full article

Background/Objectives: Accurately distinguishing malignancy in thyroid micronodules (≤10 mm) is crucial for clinical management, yet it is challenging due to the limitations of conventional ultrasonography-guided biopsy. This study aims to evaluate the predictive value of dual-layer spectral computed tomography (DSCT)-derived arterial enhancement fraction (AEF) in diagnosing thyroid microcarcinomas. Methods: In the study, 321 pathologically confirmed thyroid micronodules (benign = 131, malignant = 190) from Chongqing General Hospital underwent preoperative DSCT. Quantitative parameters of DSCT, including the normalized iodine concentration (NIC), normalized effective atomic number (NZ_eff), and slope of the spectral Hounsfield unit curve (λHU_(40–100)), were assessed. Both single-energy CT (SECT)-derived AEF (AEF_S) and DSCT-derived AEF (AEF_D) were calculated. Conventional image features included microcalcifications and enhancement blurring. Correlation between AEF_D and AEF_S was determined using Spearman’s correlation coefficient. Diagnostic performance was evaluated by calculating the area under the curve (AUC) using receiver operating characteristic (ROC) analysis. Results: Malignant micronodules exhibited significantly lower AEF_D (0.958 vs. 1.259, p < 0.001) and AEF_S (0.964 vs. 1.436, p < 0.001) versus benign nodules. Arterial phase parameters—APλHU_(40–100), APNIC, APNZ_eff—differed significantly between groups (all p < 0.001), whereas venous phase parameters (VPλHU_(40–100), VPNIC, VPNZ_eff) showed no differences (all p > 0.05). Multivariate analysis revealed that λHU_(40–100) as an independent predictor of malignancy, with an odds ratio (OR) of 0.600 (95% confidence interval (CI): 0.437–0.823; p = 0.002) and an AUC of 0.752 (95% CI: 0.698–0.806). A significant positive correlation was identified between AEF_D and AEF_S (r = 0.710; p < 0.001). For diagnosing malignancy, AEF_D demonstrated superior overall performance (AUC: 0.794; sensitivity: 70.5%; specificity: 81.7%; accuracy: 75.1%) to AEF_S (0.753; 71.1%; 74.0%; 72.3%), APλHU_(40–100) (0.752; 68.9%; 75.6%; 71.7%), and calcification (0.573; 21.6%; 92.4%; 50.5%). Clinically, AEF_D reduced the unnecessary biopsy rate to 18.3%, preventing 107 procedures in our cohort. Conclusions: AEF_D and AEF_S demonstrated strong correlation and comparable diagnostic performance in the evaluation of thyroid micronodules. Furthermore, AEF_D showed favorable diagnostic efficacy compared to both spectral parameters and conventional imaging feature. More importantly, the application of AEF_D significantly reduced unnecessary biopsy rates, highlighting its clinical value in optimizing patient management. Full article

Background and Clinical Significance: Bladder neoplasms often present with coexisting thrombi and hematuria, appearing as complex intraluminal masses on imaging, and posing a key diagnostic challenge in distinguishing neoplastic tissue from thrombus, to prevent harmful overstaging. Case Presentation: An 82-year-old man with recurrent gross hematuria and urinary disturbances was evaluated by ultrasound, which identified a large endoluminal lesion in the anterior bladder wall. The patient subsequently underwent contrast-enhanced CT using a second-generation dual-layer spectral CT system, which utilizes a dual-layer detector to simultaneously acquire high- and low-energy X-ray data. Conventional CT images confirmed a multifocal, bulky hyperdense lesion along the bladder wall, protruding into the lumen and raising suspicion for a heterogeneous mass, though further characterization was not possible. Spectral imaging enabled the reconstruction of additional maps—such as iodine density, effective atomic number (Z-effective), and electron density—which were used to further characterize these findings. The combination of these techniques clearly demonstrated differences in iodine uptake and tissue composition within the parietal lesions, allowing for a reliable differentiation between neoplastic tissue and intraluminal thrombus. Conclusions: The integration of conventional CT imaging with spectral-derived maps generated in post-processing allowed for accurate and reliable tissue differentiation between bladder neoplasm and thrombus. Spectral imaging holds the potential to prevent tumor overstaging, thereby supporting more appropriate clinical management. The dual-layer technology enables the generation of these maps from every acquisition without altering the scan protocol, thereby having minimal impact on the daily clinical workflow. Full article

Objective: The objective of this study was to compare the performance and robustness of a deep learning reconstruction method against established alternatives for soft tissue CT image reconstruction. Materials and Methods: Images were generated from portal venous phase chest–abdomen–pelvis CT scans (n = 99) acquired on a dual-layer spectral detector CT using filtered back projection, iterative model reconstruction (IMR), and deep learning reconstruction (DLR) with three parameter settings, namely ‘standard’, ‘sharper’, and ‘smoother’. Experienced raters performed a quantitative assessment by considering attenuation stability and image noise levels in ten representative structures across all reconstruction methods, as well as a qualitative assessment using a four-point Likert scale (1 = poor, 2 = fair, 3 = good, 4 = excellent) for their overall perception of ‘smoother’ DLR and IMR images. One scan was excluded due to cachexia, which limited the quantitative measurements. Results: The inter-rater reliability for quantitative measurements ranged from moderate to excellent (r = 0.63–0.96). Attenuation values did not differ significantly between reconstruction methods except for DLR against IMR in the psoas muscle (mean + 3.0 HU, p < 0.001). Image noise levels differed significantly between reconstruction methods for all structures (all p < 0.001) and were lower than FBP with any DLR parameter setting. Image noise levels with ‘smoother’ DLR were predominantly lower than or equal to IMR, while they were higher with ‘standard’ DLR and ‘sharper’ DLR. The ‘smoother’ DLR images received a higher mean rating for overall image quality than the IMR images (3.7 vs. 2.3, p < 0.001). Conclusions: ‘Smoother’ DLR images were perceived by experienced readers as having improved quality compared to FBP and IMR while also exhibiting objectively lower or equivalent noise levels. Full article

Beyond clinical areas, photon-counting detector (PCD) CT is innovatively applied to study paleontological specimens. This study presents a preliminary investigation into the application of PCD-CT for imaging large dinosaur fossils, comparing it with standard energy-integrating detector (EID) CT. The left dentary of Tyrannosaurus and the skull of Camarasaurus were imaged using PCD-CT in ultra-high-resolution mode and EID-CT. The PCD-CT and EID-CT image quality of the dinosaurs were visually assessed. Compared with EID-CT, PCD-CT yielded higher-resolution anatomical images free of image deterioration, achieving a better definition of the Tyrannosaurus mandibular canal and the three semicircular canals of Camarasaurus. PCD-CT clearly depicts the internal structure and morphology of large dinosaur fossils without damaging them and also provides spectral information, thus allowing researchers to gain insights into fossil mineral composition and the preservation state in the future. Full article

Energy spectrum computed tomography (CT) technology based on photon-counting detectors has been widely used in many applications such as lesion detection, material decomposition, and so on. But severe noise in the reconstructed images affects the accuracy of these applications. The method based on tensor decomposition can effectively remove noise by exploring the correlation of energy channels, but it is difficult for traditional tensor decomposition methods to describe the problem of tensor sparsity and low-rank properties of all expansion modules simultaneously. To address this issue, an algorithm for spectral CT reconstruction based on photon-counting detectors is proposed, which combines Kronecker-Basis-Representation (KBR) tensor decomposition and total variational (TV) regularization (namely KBR-TV). The proposed algorithm uses KBR tensor decomposition to unify the sparse measurements of traditional tensor spaces, and constructs a third-order tensor cube through non-local image similarity matching. At the same time, the TV regularization term is introduced into the independent energy spectrum image domain to enhance the sparsity constraint of single-channel images, effectively reduce artifacts, and improve the accuracy of image reconstruction. The proposed objective minimization model has been tackled using the split-Bregman algorithm. To evaluate the algorithm’s performance, both numerical simulations and realistic preclinical mouse studies were conducted. The ultimate findings indicate that the KBR-TV method offers superior enhancement in the quality of spectral CT images in comparison to several existing methods. Full article

Background: Pulmonary hypertension (PH) is characterized by elevated pressure in the pulmonary artery. Currently, most dual-energy CT (DECT) research focuses on the application of iodine mapping in pulmonary embolism. However, little attention is paid to the parametric mapping of the lung parenchyma of PH. Methods: In total, 156 cases undergoing thoracic DECT from 2021 August to 2023 February were surveyed. For each case, the iodine density (Iod) and effective atomic number (Zeff) of four different levels of the lung, along with the iodine density of the pulmonary artery and aorta, were measured. The measured parameters and their derivatives were compared between PH cases and normal controls and between chronic thromboembolic PH (CTEPH) and non-CTEPH cases. Results: Region of interest (ROI)-Zeff was statistically lower in the PH group as compared to the normal controls on each level. The ratio of PA-iod/ROI-iod was significantly higher in the PH group than in the normal controls. ROI-iod was statistically lower in the CTEPH cases as compared with the non-CTEPH cases on each level. The CTEPH cases demonstrated a higher PA-iod/ROI-iod value as compared with the non-CTEPH cases. Conclusions: The PA-iodine density and effective Z of spectrum CT could serve as valuable imaging parameters for the diagnosis and characterization of PH and CTEPH. Full article

Background: Spectral detector computed tomography (SDCT) has demonstrated superior diagnostic performance and image quality in liver disease assessment compared with traditional CT. Selecting the right reconstruction algorithm and tube voltage is essential to avoid increased noise and diagnostic errors. Objectives: This study evaluated improvements in image quality achieved using various virtual tube voltages and reconstruction algorithms for diagnosing common liver diseases with spectral CT. Methods: This retrospective study involved forty-seven patients who underwent spectral CT scans for liver conditions, including fatty liver, hemangiomas, and metastatic lesions. The assessment utilized signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), with images reconstructed using various algorithms (IMR, iDose) at different levels and virtual tube voltages. Three experienced radiologists analyzed the reconstructed images to identify the best reconstruction methods and tube voltage combinations for diagnosing these liver pathologies. Results: The signal-to-noise ratio (SNR) was highest for spectral CT images using the IMR3 algorithm in metastatic, hemangioma, and fatty liver cases. A strong positive correlation was found between IMR3 at 120 keV and 70 keV (p-value = 0.000). In contrast, iDOSE2 at 120 keV and 70 keV showed a low correlation of 0.291 (p-value = 0.045). Evaluators noted that IMR1 at 70 keV provided the best visibility for liver lesions (mean = 3.58), while IMR3 at 120 keV had the lowest image quality (mean = 2.65). Conclusions: Improvements in image quality were noted with SDCT, especially in SNR values for liver tissues at low radiation doses and a specific IMR level. The IMR1 algorithm reduced noise, enhancing the visibility of liver lesions for better diagnosis. Full article

Background/Objectives: Diffuse myocardial fibrosis and altered deformation are relevant prognostic factors in aortic stenosis (AS) patients. The aim of this exploratory study was to investigate the relationship between myocardial strain, and myocardial extracellular volume (ECV) in patients with severe AS with a photon-counting detector (PCD)-CT. Methods: We retrospectively included 77 patients with severe AS undergoing PCD-CT imaging for transcatheter aortic valve replacement (TAVR) planning between January 2022 and May 2024 with a protocol including a non-contrast cardiac scan, an ECG-gated helical coronary CT angiography (CCTA), and a cardiac late enhancement scan. Myocardial strain was assessed with feature tracking from CCTA and ECV was calculated from spectral cardiac late enhancement scans. Results: Patients with cardiac amyloidosis (n = 4) exhibited significantly higher median mid-myocardial ECV (48.2% versus 25.5%, p = 0.048) but no significant differences in strain values (p > 0.05). Patients with prior myocardial infarction (n = 6) had reduced median global longitudinal strain values (−9.1% versus −21.7%, p < 0.001) but no significant differences in global mid-myocardial ECV (p > 0.05). Significant correlations were identified between the global longitudinal, circumferential, and radial strains and the CT-derived left ventricular ejection fraction (EF) (all, p < 0.001). Patients with low-flow, low-gradient AS and reduced EF exhibited lower median global longitudinal strain values compared with those with high-gradient AS (−15.2% versus −25.8%, p < 0.001). In these patients, the baso-apical mid-myocardial ECV gradient correlated with GLS values (R = 0.28, p = 0.02). Conclusions: In patients undergoing PCD-CT for TAVR planning, ECV and GLS may enable us to detect patients with cardiac amyloidosis and reduced myocardial contractility Full article

Introduction: The number of incidental renal lesions identified in CT scans of the abdomen is increasing. Objective: The aim of this study was to determine whether hyperdense renal lesions without solid components in a portal venous CT scan can be clearly classified as vascular or non-vascular by material decomposition into iodine and water. Methods: This retrospective single-center study included 26 patients (mean age 72 years ± 9; 16 male) with 42 hyperdense renal lesions (>20 HU) in a contrast-enhanced Photon-Counting Detector CT scan (PCD-CT) between May and December 2022. Spectral decomposition into virtual non-contrast (VNC) images and iodine quantification maps was performed, and HU values were quantified within the lesions. Further imaging and histopathological reports served as reference standards. Results: Mean VNC values were 55.7 (±24.2) HU for non-vascular and 32.2 (±11.1) HU for vascular renal lesions. Mean values in the iodine maps were 5.7 (±7.8) HU for non-vascular and 33.3 (±19.0) HU for vascular renal lesions. Using a threshold of >20.3 HU in iodine maps, a total of 7/8 (87.5%) vascular lesions were correctly identified. Conclusion: This proof-of-principle study suggests that the routine use of spectral information acquired in PCD-CT scans might be able to reduce the necessary workup for hyperdense renal lesions without solid components. Further studies with larger patient cohorts are necessary to validate the results of this study and to determine the usefulness of this method in clinical routine. Full article

Spectral Computed Tomography (CT) is a versatile imaging technique widely utilized in industry, medicine, and scientific research. This technique allows us to observe the energy-dependent X-ray attenuation throughout an object by using Photon Counting Detector (PCD) technology. However, a major drawback of spectral CT is the increase in noise due to a lower achievable photon count when using more energy channels. This challenge often complicates quantitative material identification, which is a major application of the technology. In this study, we investigate the Noise2Inverse image denoising approach for noise removal in spectral computed tomography. Our unsupervised deep learning-based model uses a multi-dimensional U-Net paired with a block-based training approach modified for additional energy-channel regularization. We conducted experiments using two simulated spectral CT phantoms, each with a unique shape and material composition, and a real scan of a biological sample containing a characteristic K-edge. Measuring the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) for the simulated data and the contrast-to-noise ratio (CNR) for the real-world data, our approach not only outperforms previously used methods—namely the unsupervised Low2High method and the total variation-constrained iterative reconstruction method—but also does not require complex parameter tuning. Full article