Quantitative attenuation accuracy of virtual non-enhanced imaging compared to that of true non-enhanced imaging on dual-source dual-energy CT

Dual-energy CT for differentiation of hypodense liver lesions in pancreatic adenocarcinoma

OBJECTIVE

To assess the accuracy of CT spectral HU curve assessment of hypodense liver lesions.

METHODS

In this retrospective HIPAA-compliant study (January 2016 through May 2023), patients with biopsy-proven pancreatic adenocarcinoma and a biopsied indeterminate liver lesion underwent a DECT abdominal CT scan. Spectral HU curves were provided for each hypodense liver lesion, and slopes were calculated. Lesion Hounsfield units, iodine concentration and virtual enhancement were recorded. The Wilcoxon rank sum test was used to compare malignant and benign lesions. Optimal cutoff points were estimated using ROC curves and Youden's Index.

RESULTS

Thirty-six patients (19 men, 17 women) with a mean age of 63 years ± 9 (standard deviation), a mean height of 170.9 cm ± 9.5, a mean weight of 69.8 kg ± 14.5, and a body mass index of 23.9 kg/m2 ± 3.5. Reference standard assessment identified 92 liver lesions (50 metastases, 24 cysts, 13 abscesses, 3 regions of inflammation, 2 hemangiomas) with a mean size of 1.1 cm ± 0.5. The mean interval between the CT and liver lesion biopsy was 24 days. A diagnosis of benign versus malignant was determined based on optimal cutoffs: spectral curve slope of 1.36, iodine concentration of 6.47 (100 µg/cm3), and enhancement of 10.25. The receiver operating curves (ROC) for diagnosis using spectral curve slope, iodine concentration, and virtual enhancement resulted in an area under the curve (AUC) of 0.948, 0.946, and 0.937, respectively.

CONCLUSION

Spectral HU curves and iodine concentration of well-defined hypodense liver lesions are highly accurate in the diagnosis of benign versus malignant lesions.

KEY POINTS

Question Limited evidence exists for spectral imaging diagnosis of liver lesions-can DECT accurately differentiate between benign and metastatic hypodense liver lesions? Findings Ninety-two hypodense liver lesions evaluated using HU keV curve slope, iodine concentration, and virtual enhancement resulted in accurate benign versus metastatic differentiation. Clinical relevance Hypodense liver lesions are a challenging issue at staging, often requiring further imaging, follow-up, and/or biopsy. The additional information from multi-energy CT can be useful to differentiate between benign and malignant lesions, thereby reducing the need for costly additional evaluation.

Comparison of Muscle and Fat Measurements Between True Noncontrast and Virtual Noncontrast Images From Three Phases of Dual-Energy Dynamic Liver CT

RATIONALE AND OBJECTIVES

To determine whether virtual noncontrast (VNC) images derived from three-phase dynamic dual-energy CT (DECT) imaging can reliably substitute true noncontrast (TNC) images in assessing muscle and fat with automatic segmentation software.

MATERIALS AND METHODS

The data from 476 dynamic liver DECT examinations performed between April 2019 and December 2020 were retrospectively analyzed. VNC images were generated from arterial (VNCa), portal-venous (VNCp), and delayed (VNCd) phase images. Automated software measured muscle, visceral fat (VF), and subcutaneous fat (SF) areas. Sarcopenia was defined using muscle-related indices. Differences in muscle and fat measurements, as well as sarcopenia prevalence, between TNC and VNC images were assessed using paired t tests and McNemar tests, respectively.

RESULTS

The average age of the 476 patients (307 men) was 58.4±12.5years. Muscle density and area differed significantly between the TNC and VNC images; TNC images showed higher mean muscle density and smaller skeletal muscle area (SMA) than all VNC images (P<0.001). VF and SF attenuations were significantly lower on TNC images than on all VNC images (P<0.001). The proportions of sarcopenic patients did not differ significantly between TNC and VNCp or VNCd, regardless of the muscle index used.

CONCLUSION

Despite significant differences in muscle and fat attenuations between TNC and VNC images, VNCp and VNCd may be acceptable alternatives for measuring muscle and fat areas. However, TNC and VNC images should not be used interchangeably for assessing tissue attenuation or specific muscle components such as LAMA or NAMA.

Generation of deep learning based virtual non-contrast CT using dual-layer dual-energy CT and its application to planning CT for radiotherapy

This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficient data pairs of contrast-enhanced and non-contrast CT images, we trained our model on dual-energy CT (DECT) images, using virtual non-contrast CT (VNC CT) images as outputs instead of true non-contrast CT images. We used a deterministic method to convert CE-pCT images into pseudo DECT images for model application. Model training and evaluation were conducted on 45 patients. The performance of our model, 'VNC-Net', was evaluated using various metrics, demonstrating high scores for quantitative performance. Moreover, our model accurately replicated target VNC CT images, showing close correspondence in CT numbers. The versatility of our model was further demonstrated by applying it to pseudo VNC DECT generation, followed by conversion to VNC-pCT. CE-pCT images of ten liver cancer patients and ten left-sided breast cancer patients were used. A quantitative comparison with true non-contrast planning CT (TNC-pCT) images validated the accuracy of the generated VNC-pCT images. Furthermore, dose calculations on CE-pCT and VNC-pCT images from patients undergoing volumetric modulated arc therapy for liver and breast cancer treatment showed the clinical relevance of our approach. Despite the model's overall good performance, limitations remained, particularly in maintaining CT numbers of bone and soft tissue less influenced by contrast agent. Future research should address these challenges to further improve the model's accuracy and applicability in radiotherapy planning. Overall, our study highlights the potential of deep learning models to improve imaging protocols and accuracy in radiotherapy planning.

A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study

OBJECTIVES

We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making.

MATERIALS AND METHODS

This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models.

RESULTS

Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model.

CONCLUSIONS

The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients.

CLINICAL RELEVANCE STATEMENT

The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS.

KEY POINTS

Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.

AAPM Task Group Report 299: Quality control in multi-energy computed tomography

Multi-energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single-energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user-selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task-specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single-energy CT, the substantial differences between single-energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Prediction of pathological activity in Crohn's disease based on dual-energy CT enterography

PURPOSE

To explore the feasibility of predicting the pathological activity of Crohn's disease (CD) based on dual-energy CT enterography (DECTE).

METHODS

The clinical, endoscopic, imaging and pathological data of 55 patients with CD scanned by DECTE were retrospectively analyzed; the pathological results were used as a reference standard to classify the diseased bowel segments into active and inactive phases. The normalized iodine concentration (NIC), energy-spectrum curve slope K, dual energy index (DEI), fat fraction (FF) of the arterial phases and venous phases were compared. To assess the parameters' predictive ability, receiver-operating characteristic curves were used. The Delong test was used to compare the differences between the diagnostic efficiency of each parameter.

RESULTS

A total of 84 intestinal segments were included in the study, including 54 active intestinal segments and 30 inactive intestinal segments. The NIC, energy-spectrum curve slope K and DEI were significantly different between active and inactive bowel segments in the arterial and venous phases (P < 0.05), while FF were not significantly different (P > 0.05). The largest area under the curve (AUC) of NIC, energy-spectrum curve slope K and DEI were higher in arterial phase than in venous phase. For identifying the intestinal activity of CD, the maximum AUC of NIC in arterial phase was 0.908, with a sensitivity of 0.833 and a specificity of 0.800, and the DEI in arterial phase had the highest sensitivity (0.944).

CONCLUSION

The NIC, energy-spectrum curve slope K and DEI can effectively distinguish the active and inactive phases of the intestinal segments of CD patients and provide good assistance for determining further treatment.

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.

Quantitative evaluation of disc degeneration using dual-energy CT: advantages of R-VH, D-VH values and the IVNCa + CT model

OBJECTIVE

To evaluate the correlation between dual-energy CT (DECT) virtual calcium free (VNCA), CT attenuation, the ratio and difference of VNCA to CT attenuation, and Pfirrmann grading of lumbar disc degeneration.

METHODS

A retrospective analysis on 135 intervertebral discs from 30 patients who underwent DECT and MR. Discs was graded using the Pfirrmann system. ROIs on the sagittal plane assessed HU value, VNCA value, Rho value, Z value, R-VH value, and D-VH value. Correlation, grade differences, and multivariate regression models were assessed. Diagnostic performance and cut-off values were determined using AUC.

RESULTS

VNCA (r = 0.589, P < 0.001), R-VH (r = 0.622, P < 0.001), and D-VH (r = 0.613, P < 0.001) moderately correlated with Pfirrmann grading. HU (r = 0.388, P < 0.001), Rho (r = 0.142, P = 0.102), and Z (r = -0.125, P = 0.153) showed a weak correlation. R-VH, D-VH, and VNCA had significantly higher correlation than HU. Statistically significant differences were observed in P values of VNCA, HU, R-VH, and D-VH in relative groups (P < 0.05), but not in Rho and Z values (P > 0.05). R-VH and D-VH had significant differences between Pfirrmann grades 1 and 2, and grades 2 and 3 (early stage) (P < 0.05). AUC readings of R-VH and D-VH (≥2,  ≥3, ≥4) were higher. The multivariate model IVNCa + CT had the highest AUC.

CONCLUSION

The new quantitative indices R-VH value and D-VH value of DECT have advantages over VNCA value and HU value in evaluating early-stage disc degeneration (≥2 grades, ≥3 grades). The multivariate model IVNCa + CT has the best AUC values for evaluating disc degeneration at all stages.

Dual-energy computed tomography angiography-based quantification of lesion net water uptake to identify stroke onset time

Objectives

To explore whether dual-energy computed tomography (DECT) angiography can provide reliable quantitative information on net water uptake (NWU) of ischemic brain to identify stroke patients within 4.5 h.

Methods

We retrospectively reviewed 142 patients with stroke occurrence and who underwent DECT angiography between August 2016 and May 2022. DECT angiography manual drawn the ischemic area by referring to the normal area of the contralateral hemisphere and follow-up images. The NWU in the ischemic area was determined using virtual non-contrast and monoenergetic (VNC &VM) images acquired from DECT angiography. The NWU values in the ischemic area were compared between stroke patients within and beyond 4.5 h. The diagnostic performance of the NWU values derived from the VNC and VM images was assessed through receiver operating characteristic curve analysis. Additionally, Furthermore, we examined the correlation between the NWU values and the stroke onset time.

Results

Seventy-eight (54.93 %) stroke patients underwent DECT angiography and within 4.5 h. These patients with lower median National Institute of Health stroke scale (NIHSS) scores on admission than those beyond 4.5 h (p < 0.05). Furthermore, the group within 4.5 h had lower NWU values than did the group beyond 4.5 h on all VNC and VM images (p < 0.001). The analysis revealed that the NWU values determined using the VM (60 keV) images had the highest predictive efficiency (AUC, 0.95; sensitivity, 100 %; and specificity, 89.06 %) and showed the strongest positive correlation with stroke onset time (r-value = 0.58, p < 0.001).

Conclusions

Our findings showed that DECT angiography-based quantification of NWU helps identify the stroke patients within 4.5 h with high predictive efficiency. Thus, NWU values determined using VM (60 keV) images could serve as a significant biomarker for stroke onset time.

Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype

BACKGROUND

Spectral CT material decomposition provides quantitative information but is challenged by the instability of the inversion into basis materials. We have previously proposed the constrained One-Step Spectral CT Image Reconstruction (cOSSCIR) algorithm to stabilize the material decomposition inversion by directly estimating basis material images from spectral CT data. cOSSCIR was previously investigated on phantom data.

PURPOSE

This study investigates the performance of cOSSCIR using head CT datasets acquired on a clinical photon-counting CT (PCCT) prototype. This is the first investigation of cOSSCIR for large-scale, anatomically complex, clinical PCCT data. The cOSSCIR decomposition is preceded by a spectrum estimation and nonlinear counts correction calibration step to address nonideal detector effects.

METHODS

Head CT data were acquired on an early prototype clinical PCCT system using an edge-on silicon detector with eight energy bins. Calibration data of a step wedge phantom were also acquired and used to train a spectral model to account for the source spectrum and detector spectral response, and also to train a nonlinear counts correction model to account for pulse pileup effects. The cOSSCIR algorithm optimized the bone and adipose basis images directly from the photon counts data, while placing a grouped total variation (TV) constraint on the basis images. For comparison, basis images were also reconstructed by a two-step projection-domain approach of Maximum Likelihood Estimation (MLE) for decomposing basis sinograms, followed by filtered backprojection (MLE + FBP) or a TV minimization algorithm (MLE + TVmin ) to reconstruct basis images. We hypothesize that the cOSSCIR approach will provide a more stable inversion into basis images compared to two-step approaches. To investigate this hypothesis, the noise standard deviation in bone and soft-tissue regions of interest (ROIs) in the reconstructed images were compared between cOSSCIR and the two-step methods for a range of regularization constraint settings.

RESULTS

cOSSCIR reduced the noise standard deviation in the basis images by a factor of two to six compared to that of MLE + TVmin , when both algorithms were constrained to produce images with the same TV. The cOSSCIR images demonstrated qualitatively improved spatial resolution and depiction of fine anatomical detail. The MLE + TVmin algorithm resulted in lower noise standard deviation than cOSSCIR for the virtual monoenergetic images (VMIs) at higher energy levels and constraint settings, while the cOSSCIR VMIs resulted in lower noise standard deviation at lower energy levels and overall higher qualitative spatial resolution. There were no statistically significant differences in the mean values within the bone region of images reconstructed by the studied algorithms. There were statistically significant differences in the mean values within the soft-tissue region of the reconstructed images, with cOSSCIR producing mean values closer to the expected values.

CONCLUSIONS

The cOSSCIR algorithm, combined with our previously proposed spectral model estimation and nonlinear counts correction method, successfully estimated bone and adipose basis images from high resolution, large-scale patient data from a clinical PCCT prototype. The cOSSCIR basis images were able to depict fine anatomical details with a factor of two to six reduction in noise standard deviation compared to that of the MLE + TVmin two-step approach.

The Value of Dual-Energy Computed Tomography Angiography-Derived Parameters in the Evaluation of Clot Composition

OBJECTIVES

We aimed to assess the value of dual-energy computed tomography angiography (DE-CTA) derived parameters as a quantitative biomarker of thrombus composition in acute ischemic stroke (AIS).

METHODS

AIS patients who underwent DE-CTA before thrombectomy between August 2016 and September 2022 were included in this study. We assessed the relative proportion of red blood cells (RBCs) and the fibrin/platelet ratio (F/P) of the retrieved clots and categorized the clots as RBC-dominant (RBCs > F/P) or F/P-dominant (F/P > RBCs). The thrombus based parameters were measured on polyenergetic images (PEI), virtual monoenergetic (VM), virtual non-contrast (VNC), iodine concentration (IC), and effective atomic number (Zeff) images respectively, and the slope of the spectral Hounsfield unit curve (λHU) was calculated. These parameters were compared in the DE-CTA images of RBC- and F/P-dominant thrombi. The diagnostic performance of the parameters was analyzed using the ROC curve. Correlations between thrombus composition and DE-CTA-derived parameters were assessed.

RESULTS

The retrieved clots in 54 of 88 patients (61.36%) were RBC-dominant. The RBC-dominant thrombi showed significantly higher VNC values and lower IC, λHU, and Zeff values than the F/P-dominant thrombi (p < 0.05). The CT density measured on IC images showed the largest AUC value (AUC, 0.94; sensitivity, 77.78%; specificity, 100.00%). The Spearman rank-order correlation coefficient values showed that CT density measured on IC images of the thrombus showed the strongest association with the proportion of RBCs (r = -0.64, p < 0.001) and F/P (r = 0.65, p < 0.001).

CONCLUSIONS

DE-CTA-derived parameters, especially the CT density measured on IC images, could be associated with thrombus composition and allow for personalized thrombectomy strategies.

Dual-energy CT of the pancreas: comparison between virtual non-contrast images and true non-contrast images in the detection of pancreatic lesion

PURPOSE

To evaluate the image quality and diagnostic performance for pancreatic lesion between true non-contrast (TNC) and virtual non-contrast (VNC) images obtained from the dual-energy computed tomography (DECT).

METHODS

One hundred six patients with pancreatic mass underwent contrast-enhanced DECT examinations were retrospectively included in this study. VNC images of the abdomen were generated from late arterial (aVNC) and portal (pVNC) phases. For quantitative analysis, the attenuation differences and reproducibility of abdominal organs were compared between TNC and aVNC/pVNC measurements. Qualitatively image quality was assessed by two radiologists using a five-point scale, and they independently compared the detection accuracy of pancreatic lesions between TNC and aVNC/pVNC images. The volume CT dose index (CTDIvol) and size-specific dose estimates (SSDE) were recorded to evaluate the potential dose reduction when using VNC reconstruction to replace the unenhanced phase.

RESULTS

A total of 78.38% (765/976) of the attenuation measurement pairs were reproducible between TNC and aVNC images, and 71.0% (693/976) between TNC and pVNC images. In triphasic examinations, a total of 108 pancreatic lesions were found in 106 patients, and no significant difference in detection accuracy was found between TNC and VNC images (p = 0.587-0.957). Qualitatively, image quality was rated diagnostic (score ≥ 3) in all the VNC images. Calculated CTDIvol and SSDE reduction of about 34% could be achieved by omitting the non-contrast phase.

CONCLUSION

VNC images of DECT provide diagnostic image quality and accurate pancreatic lesions detection, which are a promising alternative to unenhanced phase with a substantial reduction of radiation exposure in clinical routine.

[Comparison of True and Virtual Non-Contrast Images of Liver Obtained with Single-Source Twin Beam and Dual-Source Dual-Energy CT]

PURPOSE

To assess the magnitude of differences between attenuation values of the true non-contrast image (TNC) and virtual non-contrast image (VNC) derived from twin-beam dual-energy CT (tbDECT) and dual-source DECT (dsDECT).

MATERIALS AND METHODS

This retrospective study included 62 patients who underwent liver dynamic DECT with tbDECT (n = 32) or dsDECT (n = 30). Arterial VNC (AVNC), portal VNC (PVNC), and delayed VNC (DVNC) were reconstructed using multiphasic DECT. Attenuation values of multiple intra-abdominal organs (n = 11) on TNCs were subsequently compared to those on multiphasic VNCs. Further, we investigated the percentage of cases with an absolute difference between TNC and VNC of ≤ 10 Hounsfield units (HU).

RESULTS

For the mean attenuation values of TNC and VNC, 33 items for each DECT were compared according to the multiphasic VNCs and organs. More than half of the comparison items for each DECT showed significant differences (tbDECT 17/33; dsDECT 19/33; Bonferroni correction p < 0.0167). The percentage of cases with an absolute difference ≤ 10 HU was 56.7%, 69.2%, and 78.6% in AVNC, PVNC, and DVNC in tbDECT, respectively, and 70.5%, 78%, and 78% in dsDECT, respectively.

CONCLUSION

VNCs derived from the two DECTs were insufficient to replace TNCs because of the considerable difference in attenuation values.

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

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

Increased regional ventilation as early imaging marker for future disease progression of interstitial lung disease: a feasibility study

OBJECTIVES

Idiopathic pulmonary fibrosis (IPF) is a disease with a poor prognosis and a highly variable course. Pathologically increased ventilation-accessible by functional CT-is discussed as a potential predecessor of lung fibrosis. The purpose of this feasibility study was to investigate whether increased regional ventilation at baseline CT and morphological changes in the follow-up CT suggestive for fibrosis indeed occur in spatial correspondence.

METHODS

In this retrospective study, CT scans were performed at two time points between September 2016 and November 2020. Baseline ventilation was divided into four categories ranging from low, normal to moderately, and severely increased (C1-C4). Correlation between baseline ventilation and volume and density change at follow-up was investigated in corresponding voxels. The significance of the difference of density and volume change per ventilation category was assessed using paired t-tests with a significance level of p ≤ 0.05. The analysis was performed separately for normal (NAA) and high attenuation areas (HAA).

RESULTS

The study group consisted of 41 patients (73 ± 10 years, 36 men). In both NAA and HAA, significant increases of density and loss of volume were seen in areas of severely increased ventilation (C4) at baseline compared to areas of normal ventilation (C2, p < 0.001). In HAA, morphological changes were more heterogeneous compared to NAA.

CONCLUSION

Functional CT assessing the extent and distribution of lung parenchyma with pathologically increased ventilation may serve as an imaging marker to prospectively identify lung parenchyma at risk for developing fibrosis.

KEY POINTS

• Voxelwise correlation of serial CT scans suggests spatial correspondence between increased ventilation at baseline and structural changes at follow-up. • Regional assessment of pathologically increased ventilation at baseline has the potential to prospectively identify tissue at risk for developing fibrosis. • Presence and extent of pathologically increased ventilation may serve as an early imaging marker of disease activity.

A Preliminary Study on the Feasibility of the Quantitative Parameters of Dual-Energy Computed Tomography Enterography in the Assessment of the Activity of Intestinal Crohn's Disease

Objective

To investigate the value of dual energy CT enterography (DECTE) in evaluating the activity of Crohn's disease (CD).

Methods

The endoscopy and imaging data of 29 patients with CD confirmed by clinic and pathology were analyzed retrospectively. The clinical CD activity index (CDAI) was used as the disease activity grouping standard, 29 patients with CD were grouped into activity groups, 18 patients in the active group (CDAI ≥ 150) with 36 intestinal segments, and 11 patients in the remission group (CDAI < 150) with 20 intestinal segments.The virtual single energy CT value, slope of energy spectrum curve and iodine content were analyzed to evaluate the evaluation of intestinal CD activity by DECTE.

Results

There were statistically significant differences in virtual single energy CT value (except 90 keV and 100 keV virtual single energy CT value), curve slope and iodine content between remission group and active group (P < 0.05), and has more diagnostic value for active phase (AUC > 0.5). ① Virtual single energy CT value: the AUC of 60 keV in arterial phase was the highest (0.924). The specificity of diagnosing CD in active stage was high (95%). ② Curve slope: the AUC of portal vein phase was the largest (0.731). The specificity of diagnosing CD in active stage was higher (85%). ③ Iodine content: the AUC of arterial phase was the highest (0.885). The specificity of diagnosing CD lesions in the active stage was 100%.

Conclusion

The virtual single energy CT value, energy spectrum curve slope and iodine content can provide reference for clinical accurate diagnosis of CD activity.

Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1204 Healthy Adults Using Unenhanced CT as a Reference Standard

BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% (n = 603), 12.5% (n = 151) and 4.8% (n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.

Technical Note: Using virtual noncontrast images from dual-energy CT to eliminate the need of precontrast CT for x-ray radiation treatment planning of abdominal tumors†

PURPOSE

Radiation therapy (RT) planning frequently utilizes contrast-enhanced CT. However, dose calculations should not be performed on a contrast-enhanced CT because the patient will not receive bolus during treatment. It is typical to acquire CT twice during RT simulation: once before injection of bolus and once after. The registration between these datasets introduces errors. In this work, we investigate the use of virtual noncontrast images (VNC) derived from dual-energy CT (DECT) to eliminate the precontrast CT and the registration error.

METHODS

CT datasets, including conventional 120 kVp pre- and postcontrast CTs and postcontrast DECT, acquired for ten pancreatic cancer patients were evaluated. The DECTs were acquired simultaneously using a dual source (DS) CT simulator. VNC and virtual mono-energetic images (VMI) were derived from DECTs. Gross tumor volumes (GTV), planning target volumes (PTV), and organs at risks (OAR) were delineated on the postcontrast CT and then populated to the precontrast CT and the VNC. An IMRT plan (50.4 Gy in 28 fractions) was then optimized on the precontrast CT. Dose distributions were recalculated on the VNC images. Contours from the pre- and postcontrast CTs and the dose distributions based on both were compared.

RESULTS

On average, the distance of centroids of the populated duodenum contours on precontrast CT differed by 6.0 ± 4.0 mm from those on postcontrast CTs. The dose distributions on the precontrast CT and VNC were almost identical. The PTV mean and maximum doses differed by 0.1% and 0.2% between the two plans, respectively.

CONCLUSION

The VNC derived from DECT can be used to replace the conventional precontrast CT scan for RT planning, eliminating the need for an additional precontrast CT scan and eliminating the registration errors. Thus, VNC can become an important asset to the future of RT.

Optimization of the differentiation and quantification of high-Z nanoparticles incorporated in medical devices for CT-guided interventions

PURPOSE

Material differentiation has been made possible using dual-energy computed tomography (DECT), in which the unique, energy-dependent attenuating characteristics of materials can provide new diagnostic information. One promising application is the clinical integration of biodegradable polymers as temporary implantable medical devices impregnated with high-atomic number (high-Z) materials. The purpose of this study was to explore the incorporation of high atomic number (high-Z) contrast materials in a bioresorbable inferior vena cava filter for advanced CT-based monitoring of its location and differentiating from surrounding materials.

MATERIALS AND METHODS

Imaging optimization and calibration studies were performed using a body phantom. The dual-energy CT (DECT) ratios for iron, zirconium, barium, gadolinium, ytterbium, tantalum, tungsten, gold, and bismuth were generated for peak kilovoltage combinations of 80/150Sn, 90/150Sn, and 100/150Sn kVp in dual-source CT via linear regression of the CT numbers at low and high energies. A secondary calibration of the material map to the nominal material concentration was generated to correct for use of materials other than iodine. CT number was calibrated to the material concentration based on single-energy CT (SECT) with additional filtration (150Sn kVp). These quantification methods were applied to monitoring of biodegradable inferior vena cava filters (IVCFs) made of braided poly(p-dioxanone) sutures infused with ultrasmall bismuth nanoparticles (BiNPs) implanted in an adult domestic pig.

RESULTS

Qualitative material differentiation was optimal for high-Z (>73) contrast agents in DECT. However, quantification became nonlinear and inaccurate as the K-edge of the material increased. Using the high-energy (150Sn kVp) data component as a SECT scan, the linearity of quantification curves was maintained with lower limits of detection than with DECT. Among the materials tested, bismuth had optimal differentiation from iodine in DECT while maintaining increased contrast in high-energy SECT for quantification (11.5% error). Coating the IVCF with BiNPs resulted in markedly greater radiopacity (maximum CT number, 2028 HU) than that of an uncoated IVCF (maximum CT number, 127 HU). Using DECT imaging and processing, the BiNP-IVCF could be clearly differentiated from iodine contrast injected into the inferior vena cava of the pig.

CONCLUSIONS

These findings may improve widespread integration of medical devices incorporated with high-Z materials into the clinic, where technical success, possible complications, and device integrity can be assessed intraoperatively and postoperatively via DECT imaging.

Evaluating anemia using contrast-enhanced spectral detector CT of the chest in a large cohort of 522 patients

Objectives

The blood of patients with anemia demonstrates distinctly lower attenuation in unenhanced CT images. However, the frequent usage of intravenous contrast hampers evaluation of anemia. Spectral detector computed tomography (SDCT) allows for reconstruction of virtual non-contrast images (VNC) from contrast-enhanced data (CE). The purpose of this study was to evaluate whether VNC allow for prediction of anemia.

Methods

Five hundred twenty-two patients with CE-SDCT of the chest and accessible serum hemoglobin (HbS) were retrospectively included. Patients were assigned to three groups (severe anemia, moderate/mild anemia, and healthy) based on recent lab tests (≤ 7 days) for HbS following gender and the WHO definition of anemia. CT attenuation was determined using two ROI in the left ventricular lumen and one ROI in the descending thoracic aorta. ROI were placed on CE and copied to VNC. ANOVA, linear regression, and receiver operating characteristics were used for statistic evaluation.

Results

Average HbS was 11.6 ± 2.4 g/dl. Attenuation on VNC showed significant differences between healthy patients, patients with mild/moderate anemia, and severely anemic patients (all p ≤ 0.05). Applying cutoffs of 39.2/37.6 HU and 33.6/32.7 HU allowed to differentiate between healthy, mild/moderately, and severely anemic men/women (AUC 0.857/0.833 and 0.879/0.932). A linear relationship between HbS and attenuation on VNC was established (r² = 0.54, HbS = − 0.875 + 0.329 × HU).

Conclusions

An approximation of HbS and presence of anemia can be conducted based on simple attenuation measurements in contrast-enhanced SDCT examinations enabled by VNC imaging.

Key Points

• While the attenuation of blood is a previously described biomarker for anemia in non-contrast images, virtual non-contrast images from spectral detector CT circumvent this limitation and allow for diagnosis of anemia in contrast-enhanced scans.

• Attenuation of blood in virtual non-contrast images derived from spectral detector CT shows a moderate correlation to serum hemoglobin levels.

• Presence of anemia be estimated in virtual non-contrast images using proposed cutoffs of 39.2 HU and 37.6 HU for men and women, respectively, to differentiate between healthy and anemic patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07497-y.