Single-Energy, Dual-Energy, and Photon-Counting Computed Tomography of the Liver: Current Development and Clinical Utility for the Assessment of Focal Liver Lesions

ABSTRACT

Advancements in computed tomography (CT) technology, particularly the emergence of dual-energy CT (DE-CT) and photon-counting detector CT (PCD-CT), can improve detection, characterization, and treatment monitoring of focal liver lesions. DE-CT, through its ability to differentiate tissues with similar densities and produce diverse datasets, has enhanced lesion visibility and diagnostic precision. PCD-CT further advances imaging with superior spatial resolution and material decomposition capabilities, offering potential for complex diagnostic scenarios. This review aimed to highlight the role of CT in hepatic imaging and its application to focal liver lesions.DE-CT improves lesion detectability using low-energy virtual monochromatic images, which enhance iodine contrast and reduce radiation and contrast agent doses. It also facilitates treatment response evaluation after locoregional therapies for hepatocellular carcinoma by quantifying biomarkers, such as the extracellular volume fraction. This review underscores the transformative impact of DE-CT and PCD-CT on liver imaging, emphasizing their complementary roles alongside magnetic resonance imaging. These innovations have paved the way for more precise diagnostics, improved treatment planning, and enhanced patient outcomes in the management of liver diseases.

Photon-Counting CT Effects on Sensitivity for Liver Lesion Detection: A Reader Study Using Virtual Imaging

Background Detection of hepatic metastases at CT is a daily task in radiology departments that influences medical and surgical treatment strategies for oncology patients. Purpose To compare simulated photon-counting CT (PCCT) with energy-integrating detector (EID) CT for the detection of small liver lesions. Materials and Methods In this reader study (July to December 2023), a virtual imaging framework was used with 50 anthropomorphic phantoms and 183 generated liver lesions (one to six lesions per phantom, 0.4-1.5 cm in diameter). Virtual CT platforms simulated PCCT and EID CT scanners. Phantoms were virtually scanned using routine (6 mGy) and low-dose (1.5 mGy) conditions and reconstructed with three kernels. A subset of 300 scans (150 PCCT vs EID CT pairs) were selected. Four radiologists independently reviewed all scans to mark liver lesions, assigned confidence scores for detection, and rated scan quality. Analysis was performed on a per-lesion basis to determine sensitivity for several variables and on a per-scan basis for scan quality. The McNemar test, two-sided paired t tests, and mixed-effects logistic regression models were fitted; P < .05 was considered indicative of statistically significant difference. Results Consensus reader sensitivity in detecting lesions was 82.1% (451 of 549) for PCCT versus 77.6% (426 of 549) for EID CT (P < .001), with a mean sensitivity gain of 4.3 percentage points ± 1.3 (P < .001 to P = .02 per reader). Readers had better subjective confidence for lesions at PCCT (mean score, 61.5 ± 22 vs 56.1 ± 24 [on a 101-point scale]; P < .001). Sensitivity was lower for lesions smaller than 1 cm, with more pronounced difference between PCCT and EID CT (74.0% [271 of 366] vs 67.2% [246 of 366]; P < .001). At the lower dose level, PCCT showed higher sensitivity than EID CT (68.9% [168 of 244] vs 61.1% [149 of 244]; P < .001) for subcentimeter lesions. In a multivariable model, PCCT was independently associated with increased odds of lesion detection (odds ratio, 1.55; P < .001). Image quality was slightly higher for PCCT (mean score, 55.3 vs 50.6 [on a 101-point scale]; P < .001). Conclusion Compared with EID CT, PCCT showed better sensitivity in the detection of small liver lesions. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Menu in this issue.

Enhancing the Opportunistic Bone Status Assessment Using Radiomics Based on Dual-Energy Spectral CT Material Decomposition Images

The dual-energy spectral CT (DEsCT) employs material decomposition (MD) technology, opening up novel avenues for the opportunistic assessment of bone status. Radiomics, a powerful tool for elucidating the structural and textural characteristics of bone, aids in the detection of mineral loss. Therefore, this study aims to compare the efficacy of bone status assessment using both bone density measurements and radiomics models derived from MD images and to further explore the clinical value of radiomics models.

METHODS

Retrospective data were collected from 307 patients who underwent both quantitative computed tomography (QCT) and full-abdomen DEsCT scans at our institution. Based on QCT measurements, patients were divided into three categories: normal bone mineral density (BMD), osteopenia, and osteoporosis. Using the abdominal DEsCT data, six types of MD images were reconstructed, including HAP (Water), HAP (Fat), Ca (Water), Ca (Fat), Fat (Ca), and Fat (HAP). Patients were randomly divided into a training cohort (n = 214) and a validation cohort (n = 93) at a ratio of 7:3. Focusing on the L1 to L3 vertebrae, density values from the six MD images were measured. Six density value models and six radiomics models were constructed using a random forest (RF) classifier. The performance of these models in assessing bone status was evaluated using the receiver operating characteristic (ROC) curves, and the DeLong test was employed to compare performance differences between the models.

RESULTS

The macro-area under the curve (AUC) values for the density value models based on HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images were 0.870, 0.870, 0.847, and 0.765, respectively, which outperformed those of Fat (Ca) (AUC = 0.623) and Fat (HAP) (AUC = 0.618) density value models. In the comparison of radiomics models, the trends of model performance were consistent with the density value models across the six MD images. However, the models based on HAP (Water), Ca (Water), HAP (Fat), Ca (Fat), Fat (Ca), and Fat (HAP) images exhibited superior performance than those of the density value models with the corresponding MD images, with values of 0.946, 0.941, 0.934, 0.926, 0.831, and 0.824, respectively.

CONCLUSIONS

Bone status assessment can be accurately conducted using density values from HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images. However, radiomics models derived from MD images surpass traditional density measurement methods in evaluating bone status, highlighting their superior diagnostic potential.

Photon counting CT clinical adoption, integration, and workflow

Photon counting CT was recently introduced into clinical practice [Rajendran K, Petersilka M, Henning A, Shanblatt ER, Schmidt B, Flohr TG, Ferrero A, Baffour F, Diehn FE, Yu L, Rajiah P, Fletcher JG, Leng S, McCollough CH. First Clinical Photon-counting Detector CT System: Technical Evaluation. Radiology 2022;303(1):130-138. doi: https://doi.org/10.1148/radiol.212579 ]. Photon counting detectors (PCD) afford better spatial resolution, radiation dose efficiency, and iodine contrast-to-noise than EID-CT [Leng S, Bruesewitz M, Tao S, Rajendran K, Halaweish AF, Campeau NG, Fletcher JG, McCollough CH. Photon-counting Detector CT: System Design and Clinical Applications of an Emerging Technology. Radiographics 2019;39(3):729-743. doi: https://doi.org/10.1148/rg.2019180115 ); (Leng S, Rajendran K, Gong H, Zhou W, Halaweish AF, Henning A, Kappler S, Baer M, Fletcher JG, McCollough CH. 150-mum Spatial Resolution Using Photon-Counting Detector Computed Tomography Technology: Technical Performance and First Patient Images. Invest Radiol 2018;53(11):655-662. doi: https://doi.org/10.1097/RLI.0000000000000488 )(Booij R, van der Werf NR, Dijkshoorn ML, van der Lugt A, van Straten M. Assessment of Iodine Contrast-To-Noise Ratio in Virtual Monoenergetic Images Reconstructed from Dual-Source Energy-Integrating CT and Photon-Counting CT Data. Diagnostics (Basel) 2022;12(6). doi: https://doi.org/10.3390/diagnostics12061467 ); (Sawall S, Klein L, Amato C, Wehrse E, Dorn S, Maier J, Heinze S, Schlemmer HP, Ziener CH, Uhrig M, Kachelriess M. Iodine contrast-to-noise ratio improvement at unit dose and contrast media volume reduction in whole-body photon-counting CT. Eur J Radiol 2020;126:108909. doi: https://doi.org/10.1016/j.ejrad.2020.108909 ] while also maintaining multienergy CT (MECT) capabilities[Flohr T, Petersilka M, Henning A, Ulzheimer S, Ferda J, Schmidt B. Photon-counting CT review. Phys Med 2020;79:126-136. doi: https://doi.org/10.1016/j.ejmp.2020.10.030 ]. This article will review the clinical adoption of PCD-CT including protocol development, clinical applications, clinical integration and workflow considerations. Protocol development is institution specific and involves collaborative decision-making among radiologists, physicists, and technologists. Key PCD clinical applications include radiation exposure reduction, intravenous contrast volume reduction, and improved lesion conspicuity. Patients who would most benefit from these improvements may preferentially be scanned with PCD CT. With numerous available reconstructions, radiologists should be strategic in the series sent to PACS for interpretation and routinely sending spectral series to PACS can facilitate integration with clinical workflow. The Society of Abdominal Radiology PCD Emerging Technology Commission endorsed this article.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer

OBJECTIVE

The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa).

MATERIALS AND METHODS

A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine (SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values.

RESULTS

In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status.

CONCLUSIONS

By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

Dual-energy CT applications on liver imaging: what radiologists and radiographers should know? A systematic review

PURPOSE

This review aims to provide a comprehensive summary of DECT techniques, acquisition workflows, and post-processing methods. By doing so, we aim to elucidate the advantages and disadvantages of DECT compared to conventional single-energy CT imaging.

METHODS

A systematic search was conducted on MEDLINE/EMBASE for DECT studies in liver imaging published between 1980 and 2024. Information regarding study design and endpoints, patient characteristics, DECT technical parameters, radiation dose, iodinated contrast agent (ICA) administration and postprocessing methods were extracted. Technical parameters, including DECT phase, field of view, pitch, collimation, rotation time, arterial phase timing (from injection), and venous timing (from injection) from the included studies were reported, along with formal narrative synthesis of main DECT applications for liver imaging.

RESULTS

Out of the initially identified 234 articles, 153 met the inclusion criteria. Extensive variability in acquisition parameters was observed, except for tube voltage (80/140 kVp combination reported in 50% of articles) and ICA administration (1.5 mL/kg at 3-4 mL/s, reported in 91% of articles). Radiation dose information was provided in only 40% of articles (range: 6-80 mGy), and virtual non-contrast imaging (VNC) emerged as a common strategy to reduce the radiation dose. The primary application of DECT post-processed images was in detecting focal liver lesions (47% of articles), with predominance of study focusing on hepatocellular carcinoma (HCC) (27%). Furthermore, a significant proportion of the articles (16%) focused on enhancing DECT protocols, while 15% explored metastasis detection.

CONCLUSION

Our review recommends using 80/140 kVp tube voltage with 1.5 mL/kg ICA at 3-4 mL/s flow rate. Post-processing should include low keV-VMI for enhanced lesion detection, IMs for tumor iodine content evaluation, and VNC for dose reduction. However, heterogeneous literature hinders protocol standardization.

Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model

While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50 keV (sVMI50keV) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI50keV. The image quality was evaluated, and the results were compared with those of the convolutional neural network-based model, Unet. The mean absolute errors from the true VMI50keV were 36.5 ± 4.9 and 33.0 ± 4.4 Hounsfield units for Unet and SwinUNETR, respectively. SwinUNETR yielded smaller errors in tissue attenuation values compared with those of Unet. The contrast changes in sVMI50keV generated by SwinUNETR from SECT were closer to those of DECT-derived VMI50keV than the contrast changes in Unet-generated sVMI50keV. This study demonstrated the potential of transformer-based models for generating synthetic low-energy VMIs from SECT images, thereby improving the image quality of head and neck cancer imaging. It provides a practical and feasible solution to obtain low-energy VMIs from SECT data that can benefit a large number of facilities and patients without access to DECT technology.

Head-to-head comparison of contrast-enhanced CT, dual-layer spectral-detector CT, and Gd-EOB-DTPA-enhanced MR in detecting neuroendocrine tumor liver metastases

PURPOSE

To explore the optimal of kiloelectron voltage (keV) of virtual monoenergetic imaging (VMI) of dual-layer spectral-detector CT (DLCT) in detecting neuroendocrine tumor liver metastases (NETLM) and to investigate diagnostic performance of polyenergetic images (PEI), DLCT, and Gd-EOB-DTPA-enhanced MR.

METHODS

Seventy-two patients with suspected NETLM who underwent DLCT and Gd-EOB-DTPA-enhanced MR were retrospectively enrolled. Tumor signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between PEI and VMI at 40-140 keV. Two radiologists read the CT examinations with and without VMI separately in consensus. Two other radiologists read the Gd-EOB-DTPA-enhanced MR in consensus. The diagnostic performance was evaluated. Reference standard was histopathology, follow-up, and interpretation of all available imaging.

RESULTS

The highest SNR and CNR were observed at VMI40keV, significantly higher than PEI in the arterial and venous phases (all P<0.01). A total of 477 lesions were identified (396 metastases, 81 benign lesions). Per-lesion AUC was 0.86, 0.91, and 0.97 (PEI, DLCT, and Gd-EOB-DTPA-enhanced MR, respectively). Sensitivity of PEI, DLCT, and Gd-EOB-DTPA-enhanced MRI were 0.76, 0.86, and 0.95, respectively. DLCT significantly improved sensitivity compared to PEI. MR had significantly higher sensitivity than DLCT and PEI. Subgroup analysis demonstrated that the difference in diagnostic performance was concentrated on lesions < 10 mm.

CONCLUSION

The image quality of VMI40keV is higher than that of PEI. DLCT with VMI40keV provides better diagnostic sensitivity for NETLM detection than PEI. Gd-EOB-DTPA-enhanced MR yielded the best diagnostic performance for NETLM detection.

Dual-Energy Computed Tomography in the Evaluation and Management of Subarachnoid Hemorrhage, Intracranial Hemorrhage, and Acute Ischemic Stroke

Dual-energy computed tomography (DECT) has emerged as a valuable imaging modality in the diagnosis and management of various cerebrovascular pathologies, including subarachnoid hemorrhage, intracranial hemorrhage, and acute ischemic stroke. This article reviews the principles of DECT and its applications in the evaluation and management of these conditions. The authors discuss the advantages of DECT over conventional computed tomography, as well as its limitations, and provide an overview of current research and future directions in the field.

Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer

PURPOSE

While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to establish an artificial intelligence (AI)-based image-domain material decomposition technique using multiple keV-output learning of virtual monochromatic images (VMIs) to create DECT-equivalent images from SECT images.

METHODS

This study involved 82 patients with head and neck cancer. Of these, the AI model was built with data from the 67 patients with only DECT scans, while 15 patients with both SECT and DECT scans were used for SECT testing. Our AI model generated VMI50keV and VMI100keV from VMI70keV equivalent to 120-kVp SECT images. We introduced a loss function for material density images (MDIs) in addition to the loss for VMIs. For comparison, we trained the same model with the loss for VMIs only. DECT-equivalent images were generated from SECT images and compared with the true DECT images.

RESULTS

The prediction time was 5.4 s per patient. The proposed method with the MDI loss function quantitatively provided more accurate DECT-equivalent images than the model trained with the loss for VMIs only. Using real 120-kVp SECT images, the trained model produced precise DECT images of excellent quality.

CONCLUSION

In this study, we developed an AI-based material decomposition approach for head and neck cancer patients by introducing the loss function for MDIs via multiple keV-output learning. Our results suggest the feasibility of AI-based image-domain material decomposition in a conventional SECT system without a DECT scanner.

Dual-Energy, Spectral and Photon Counting Computed Tomography for Evaluation of the Gastrointestinal Tract

The use of dual-energy computed tomography (CT) allows for reconstruction of energy- and material-specific image series. The combination of low-energy monochromatic images, iodine maps, and virtual unenhanced images can improve lesion detection and disease characterization in the gastrointestinal tract in comparison with single-energy CT.

Dual-Energy Computed Tomography: Integration Into Clinical Practice and Cost Considerations

Optimization of dual-energy CT (DECT) workflow is critical for successful integration of DECT into practice. Patient selection strategies differ by scanner type and may be based on patient size, exam indication, or both. All stakeholders involved in patient scheduling and scan acquisition should be involved in patient triage to DECT. Automation of DECT postprocessing frees up technologist and radiologist time, but care must be taken to avoid sending unnecessary reconstructions to PACS. DECT use in the Emergency Department aids in incidentaloma characterization and improves reader diagnostic confidence, and results in quantifiable cost savings by eliminating the need for follow-up exams.

Synthetic dual-energy CT reconstruction from single-energy CT Using artificial intelligence

PURPOSE

To develop and assess the utility of synthetic dual-energy CT (sDECT) images generated from single-energy CT (SECT) using two state-of-the-art generative adversarial network (GAN) architectures for artificial intelligence-based image translation.

METHODS

In this retrospective study, 734 patients (389F; 62.8 years ± 14.9) who underwent enhanced DECT of the chest, abdomen, and pelvis between January 2018 and June 2019 were included. Using 70-keV as the input images (n = 141,009) and 50-keV, iodine, and virtual unenhanced (VUE) images as outputs, separate models were trained using Pix2PixHD and CycleGAN. Model performance on the test set (n = 17,839) was evaluated using mean squared error, structural similarity index, and peak signal-to-noise ratio. To objectively test the utility of these models, synthetic iodine material density and 50-keV images were generated from SECT images of 16 patients with gastrointestinal bleeding performed at another institution. The conspicuity of gastrointestinal bleeding using sDECT was compared to portal venous phase SECT. Synthetic VUE images were generated from 37 patients who underwent a CT urogram at another institution and model performance was compared to true unenhanced images.

RESULTS

sDECT from both Pix2PixHD and CycleGAN were qualitatively indistinguishable from true DECT by a board-certified radiologist (avg accuracy 64.5%). Pix2PixHD had better quantitative performance compared to CycleGAN (e.g., structural similarity index for iodine: 87% vs. 46%, p-value < 0.001). sDECT using Pix2PixHD showed increased bleeding conspicuity for gastrointestinal bleeding and better removal of iodine on synthetic VUE compared to CycleGAN.

CONCLUSIONS

sDECT from SECT using Pix2PixHD may afford some of the advantages of DECT.

Spectral CT: Current Liver Applications

Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.

Radiation Dose Reduction in Contrast-Enhanced Abdominal CT: Comparison of Photon-Counting Detector CT with 2nd Generation Dual-Source Dual-Energy CT in an oncologic cohort

RATIONAL AND OBJECTIVES

Comparison of radiation dose and image quality in routine abdominal and pelvic contrast-enhanced computed tomography (CECT) between a photon-counting detector CT (PCD-CT) and a dual energy dual source CT (DSCT).

MATERIALS AND METHODS

70 oncologic patients (mean age 66 ± 12 years, 29 females) were prospectively enrolled between November 2021 and February 2022. Abdominal CECT were clinically indicated and performed first on a 2nd-generation DSCT and at follow-up on a 1st-generation dual-source PCD-CT. The same contrast media (Imeron 350, Bracco imaging) and pump protocol was used for both scans. For both scanners, polychromatic images were reconstructed with 3mm slice thickness and comparable kernel (I30f[DSCT] and Br40f[PCD-CT]); for PCD-CT data from all counted events above the lowest energy threshold at 20 keV ("T3D") were used. Results were compared in terms of radiation dose metrics of CT dose index (CTDIvol), dose length product (DLP) and size-specific dose estimation (SSDE), objective and subjective measurements of image quality were scored by two emergency radiologists including lesion conspicuity.

RESULTS

Median time interval between the scans was 4 months (IQR: 3-6). CNRvessel and SNRvessel of T3D reconstructions from PCD-CT were significantly higher than those of DSCT (all, p < 0.05). Qualitative image noise analysis from PCD-CT and DSCT yielded a mean of 4 each. Lesion conspicuity was rated significantly higher in PCD-CT (Q3 strength) compared to DSCT images. CTDI, DLP and SSDE mean values for PCD-CT and DSCT were 7.98 ± 2.56 mGy vs. 14.11 ± 2.92 mGy, 393.13 ± 153.55 mGy*cm vs. 693.61 ± 185.76 mGy*cm and 9.98 ± 2.41 vs. 14.63 ± 1.63, respectively, translating to a dose reduction of around 32% (SSDE).

CONCLUSION

PCD-CT enables oncologic abdominal CT with a significantly reduced dose while keeping image quality similar to 2nd-generation DSCT.

Is There Still a Role for Two-Phase Contrast-Enhanced CT and Virtual Monoenergetic Images in the Era of Photon-Counting Detector CT?

BACKGROUND

To compare the diagnostic characteristics between arterial phase imaging versus portal venous phase imaging, applying polychromatic T3D images and low keV virtual monochromatic images using a 1st generation photon-counting CT detector, of CT in patients with hepatocellular carcinoma (HCC).

METHODS

Consecutive patients with HCC, with a clinical indication for CT imaging, were prospectively enrolled. Virtual monoenergetic images (VMI) were reconstructed at 40 to 70 keV for the PCD-CT. Two independent, blinded radiologists counted all hepatic lesions and quantified their size. The lesion-to-background ratio was quantified for both phases. SNR and CNR were determined for T3D and low VMI images; non-parametric statistics were used.

RESULTS

Among 49 oncologic patients (mean age 66.9 ± 11.2 years, eight females), HCC was detected in both arterial and portal venous scans. The signal-to-noise ratio, the CNR liver-to-muscle, the CNR tumor-to-liver, and CNR tumor-to-muscle were 6.58 ± 2.86, 1.40 ± 0.42, 1.13 ± 0.49, and 1.53 ± 0.76 in the arterial phase and 5.93 ± 2.97, 1.73 ± 0.38, 0.79 ± 0.30, and 1.36 ± 0.60 in the portal venous phase with PCD-CT, respectively. There was no significant difference in SNR between the arterial and portal venous phases, including between "T3D" and low keV images (p > 0.05). CNR_{tumor-to-liver} differed significantly between arterial and portal venous contrast phases (p < 0.005) for both "T3D" and all reconstructed keV levels. CNR_{liver-to-muscle} and CNR_{tumor-to-muscle} did not differ in either the arterial or portal venous contrast phases. CNR_{tumor-to-liver} increased in the arterial contrast phase with lower keV in addition to SD. In the portal venous contrast phase, CNR_{tumor-to-liver} decreased with lower keV; whereas, CNR_{tumor-to-muscle} increased with lower keV in both arterial and portal venous contrast phases. CTDI and DLP mean values for the arterial upper abdomen phase were 9.03 ± 3.59 and 275 ± 133, respectively. CTDI and DLP mean values for the abdominal portal venous phase were 8.75 ± 2.99 and 448 ± 157 with PCD-CT, respectively. No statistically significant differences were found concerning the inter-reader agreement for any of the (calculated) keV levels in either the arterial or portal-venous contrast phases.

CONCLUSIONS

The arterial contrast phase imaging provides higher lesion-to-background ratios of HCC lesions using a PCD-CT; especially, at 40 keV. However, the difference was not subjectively perceived as significant.

Non-invasive mass and temperature quantifications with spectral CT

Spectral CT has been increasingly implemented clinically for its better characterization and quantification of materials through its multi-energy results. It also facilitates calculation of physical density, allowing for non-invasive mass measurements and temperature evaluations by manipulating the definition of physical density and thermal volumetric expansion, respectively. To develop spectral physical density quantifications, original and parametrized Alvarez-Macovski model and electron density-physical density model were validated with a phantom. The best physical density model was then implemented on clinical spectral CT scans of ex vivo bovine muscle to determine the accuracy and effect of acquisition parameters on mass measurements. In addition, the relationship between physical density and changes in temperature was evaluated by scanning and subjecting the tissue to a range of temperatures. The parametrized Alvarez-Macovski model performed best in both model development and validation with errors within ± 0.02 g/mL. No effect from acquisition parameters was observed in mass measurements, which demonstrated accuracy with a maximum percent error of 0.34%. Furthermore, physical density was strongly correlated (R of 0.9781) to temperature changes through thermal volumetric expansion. Accurate and precise spectral physical density quantifications enable non-invasive mass measurements for pathological detection and temperature evaluation for thermal therapy monitoring in interventional oncology.

A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images

OBJECTIVE

Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT.

METHODS

Monochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations.

RESULTS

Slice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images.

CONCLUSIONS

Deep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, "plastic") relative to FBP.

Added value of contrast enhancement boost images in routine multiphasic contrast-enhanced CT for the diagnosis of small (<20 mm) hypervascular hepatocellular carcinoma

PURPOSE

To investigate the added value of contrast enhancement boost (CE-boost) images in multiphasic contrast-enhanced CT (CE-CT) for diagnosing small (<20 mm) hypervascular hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

This retrospective study included 69 patients (age, 74 ± 8 years; 52 men) with 70 hypervascular HCCs (<20 mm) who underwent multiphasic CE-CT (pre-contrast, late arterial phase [LAP], portal venous phase [PVP], and equilibrium phase). Two types of CE-boost images were generated by subtracting PVP from LAP (LA-PV) images and LAP from PVP (PV-LA) images to enhance the contrast effect of hepatic arterial and portal venous perfusion more selectively. Tumor-to-liver contrast-to-noise ratios (CNRs) in CE-boost images were compared with those in CE-CT images using the Wilcoxon signed-rank test. Two independent readers reviewed the imaging datasets: CE-CT alone and CE-CT with CE-boost images. The diagnostic performance of each dataset was compared using jackknife alternative free-response receiver operating characteristics (JAFROC-1).

RESULTS

The tumor-to-liver CNRs in the LA-PV (6.4 ± 3.0) and PV-LA (-3.3 ± 2.1) images were greater than those in the LAP (3.2 ± 1.7) and PVP images (-1.1 ± 1.4) (p <.001 for both). The reader-averaged figures of merit were 0.751 for CE-CT alone and 0.807 for CE-CT with CE-boost images (p <.001). Sensitivities increased by adding CE-boost images for both readers (p <.001 and = 0.03), while positive predictive values were equivalent (p >.99).

CONCLUSION

Adding CE-boost images to multiphasic CE-CT can improve the diagnostic accuracy and sensitivity for small hypervascular HCC by increasing the tumor-to-liver CNR.

Liver Iodine Quantification With Photon-Counting Detector CT: Accuracy in an Abdominal Phantom and Feasibility in Patients

RATIONALE AND OBJECTIVES

To compare the accuracy of iodine quantification in liver parenchyma and lesions between dual-source photon-counting detector CT (PCD-CT) and dual-source energy-integrating detector CT (EID-CT) in a phantom and to demonstrate the feasibility of iodine quantification with PCD-CT in liver parenchyma and lesions in patients.

MATERIALS AND METHODS

An anthropomorphic abdominal phantom with a liver insert containing parenchyma and lesions was imaged on a clinical PCD-CT at 120kV and in the dual-energy mode on an EID-CT with kV-combinations of 80/Sn150kV, 90/Sn150kV, and 100/Sn150kV. Three patient sizes were imaged at three different radiation doses (CTDI_vol: 5, 10, 15mGy). Thirty patients with liver cysts, hemangiomas or metastases imaged with PCD-CT were retrospectively included. Iodine maps were reconstructed and iodine concentrations were measured in liver parenchyma and lesions. For the phantom, iodine error was quantified as the absolute difference to the vendor's specifications as reference.

RESULTS

Overall iodine error was 0.33 ± 0.29, 0.34 ± 0.32, 0.39 ± 0.37, 0.35 ± 0.39 mgI/mL for 80/Sn150kV, 90/Sn150kV, 100/Sn150kV of EID-CT, and PCD-CT, respectively, without significant differences between PCD-CT and EID-CT (p > 0.05). Radiation dose did not significantly influence error of PCD-CT (p > 0.05) nor EID-CT (p > 0.05). For both scanners, smaller patient sizes were associated with lower errors (p < 0.05). Iodine concentration and base material attenuation significantly influenced quantification for EID-CT (p < 0.05) but not PCD-CT (p > 0.05). In patients, iodine quantification was feasible in liver parenchyma, cysts, hemangiomas, and metastases.

CONCLUSION

Iodine quantification with PCD-CT is accurate in simulated liver parenchyma and lesions irrespective of radiation dose, iodine concentration, and base attenuation and is feasible in common liver lesions in patients.

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study

OBJECTIVES

To investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to that of other reconstruction algorithms in a phantom experiment and an abdominal clinical study.

METHODS

An elliptical phantom with five different iodine concentrations (1-12 mgI/mL) was imaged five times with fast-kilovoltage-switching DECT for three target volume CT dose indexes. All images were reconstructed using filtered back-projection, iterative reconstruction (two levels), and DLIR algorithms. Measured and nominal iodine concentrations were compared among the algorithms. Contrast-enhanced CT of the abdomen with the same scanner was acquired in clinical patients. In arterial and portal venous phase images, iodine concentration, image noise, and coefficients of variation for four locations were retrospectively compared among the algorithms. One-way repeated-measures analyses of variance were used to evaluate differences in the iodine concentrations, standard deviations, coefficients of variation, and percentages of error among the algorithms.

RESULTS

In the phantom study, the measured iodine concentrations were equivalent among the algorithms: within ± 8% of the nominal values, with root-mean-square deviations of 0.08-0.36 mgI/mL, regardless of radiation dose. In the clinical study (50 patients; 35 men; mean age, 68 ± 11 years), iodine concentrations were equivalent among the algorithms for each location (all p > .99). Image noise and coefficients of variation were lower with DLIR than with the other algorithms (all p < .01).

CONCLUSIONS

The DLIR algorithm reduced image noise and variability of iodine concentration values compared with other reconstruction algorithms in the fast-kilovoltage-switching dual-energy CT.

KEY POINTS

• In the phantom study, standard deviations and coefficients of variation in iodine quantification were lower on images with the deep learning image reconstruction algorithm than on those with other algorithms. • In the clinical study, iodine concentrations of measurement location in the upper abdomen were consistent across four reconstruction algorithms, while image noise and variability of iodine concentrations were lower on images with the deep learning image reconstruction algorithm.

The Role of Dual-Energy CT for the Assessment of Liver Metastasis Response to Treatment: Above the RECIST 1.1 Criteria

Imaging assessment of liver lesions is fundamental to predict therapeutic response and improve patient survival rates. Dual-Energy Computed Tomography (DECT) is an increasingly used technique in the oncologic field with many emerging applications. The assessment of iodine concentration within a liver lesion reflects the biological properties of the tumor and provides additional information to radiologists that is normally invisible to the human eye. The possibility to predict tumor aggressiveness and therapeutic response based on quantitative and reproducible parameters obtainable from DECT images could improve clinical decisions and drive oncologists to choose the best therapy according to metastasis biological features. Moreover, in comparison with standard dimensional criteria, DECT provides further data on the cancer microenvironment, especially for patients treated with antiangiogenic-based drugs, in which tumor shrinkage is a late parameter of response. We investigated the predictive role of DECT in the early assessment of liver metastasis response to treatment in comparison with standard dimensional criteria during antiangiogenetic-based therapy.

Effect of contrast medium on early detection and analysis of mediastinal lymph nodes in computed tomography

OBJECTIVE

This study aimed to evaluate the diagnostic efficiency of contrast-to-noise and signal-to-noise ratios created by the contrast medium in detecting lymph nodes.

METHODS

In this study, 57 short-axis subcentimeter lymph nodes in 40 cardiac computed tomography patients with noncontrast- and contrast-enhanced phases were evaluated. The contrast-to-noise ratios and signal-to-noise ratios of noncontrast- and contrast-enhanced lymph node-mediastinal fat and aortic-mediastinal fat tissues were determined. In addition, lymph nodes in noncontrast- and contrast-enhanced series were evaluated subjectively.

RESULTS

There was a significant difference in lymph node-mediastinal fat signal-to-noise values between the contrast and noncontrast phases (p=0.0002). In the contrast phase, aortic density values were found to be 322.04±18.51 HU, lymph node density values were 76.41±23.41 HU, and mediastinal adipose tissue density values were -65.73±22.96 HU. Aortic-mediastinal fat contrast-to-noise ratio value was 20.23±6.92 and the lymph node-mediastinal fat contrast-to-noise ratio value was 6.43±2.07. A significant and moderate correlation was observed between aortic-mediastinal fat and lymph node-mediastinal fat contrast-to-noise ratio values in the contrast phase (r=0.605; p<0.001). In the contrast-enhanced series, there was a significant increase in the subjective detection of lymph nodes (p=0.0001).

CONCLUSION

In the detection of paratracheal lymph nodes, the contrast agent increases the detection of short-axis subcentimeter lymph nodes quantitatively and qualitatively. Contrast enhances and facilitates the detection of paratracheal lymph nodes.

DECT Numbers in Upper Abdominal Organs for Differential Diagnosis: A Feasibility Study

Evaluating the similarity between two entities such as primary and suspected metastatic lesions using quantitative dual-energy computed tomography (DECT) numbers may be useful. However, the criteria for the similarity between two entities based on DECT numbers remain unclear. We therefore considered the possibility that a similarity in DECT numbers within the same organ could provide suitable standards. Thus, we assumed that the variation in DECT numbers within a single organ is sufficiently minimal to be considered clinically equivalent. Therefore, the purpose of this preliminary study is to investigate the differences in DECT numbers within upper abdominal organs. This retrospective study included 30 patients with data from hepatic protocol DECT scans. DECT numbers of the following parameters were collected: (a, b) 70 and 40 keV CT values, (c) slope, (d) effective Z, and (e, f) iodine and water concentration. The agreement of DECT numbers obtained from two regions of interest in the same organ (liver, spleen, and kidney) were assessed using Bland-Altman analysis. The diagnostic ability of each DECT parameter to distinguish between the same or different organs was also assessed using receiver operating characteristic analysis. The 95% limits of agreement within the same organ exhibited the narrowest value range on delayed phase (DP) CT [(c) -11.2-8.3%, (d) -2.0-1.5%, (e) -11.3-8.4%, and (f) -0.59-0.62%]. The diagnostic ability was notably high when using differences in DECT numbers on portal venous (PVP) and DP images (the area under the curve of DP: 0.987-0.999 in (c)-(f)). Using the variability in DECT numbers in the same organ as a criterion for defining similarity may be helpful in making a differential diagnosis by comparing the DECT numbers of two entities.

Photon-counting detector CT improves quality of arterial phase abdominal scans: A head-to-head comparison with energy-integrating CT

PURPOSE

Photon-counting detector (PCD)-CT is expected to have a substantial impact on oncologic abdominal imaging. We compared subjective and objective image quality between PCD-CT and conventional energy-integrating detector (EID-)CT arterial phase abdominal scans.

METHODS

This study included 84 patients undergoing both types of abdominal CT. EID-CT scans were acquired with a tube voltage of 100 kVp. With PCD-CT, acquired with 120-kVp, we reconstructed polychromatic T3D images and virtual monoenergetic images (VMIs) in 10-keV intervals from 40 to 90 keV. Quantitative image analysis included noise and contrast-to-noise ratio (CNR) of hepatic vessels, kidney cortex, and hypervascular liver lesions to liver parenchyma. Three raters used a 5-point Likert scale for qualitative image analysis of image noise and contrast, lesion conspicuity, and overall image quality. Radiation dose exposure (CT dose index) was compared between the two CT types.

RESULTS

Mean CT dose index and effective dose were respectively 18 % and 26 % lower with PCD-CT versus EID-CT. Compared with EID-CT, CNRs of kidney cortex and vessel to liver parenchyma were significantly higher in PCD-CT VMIs at energies ≤ 60 keV and in polychromatic T3D images (p < 0.004). Overall image quality of PCD-CT VMIs at 50 and 60 keV was rated as significantly better (p < 0.01) than the EID-CT images (inter-reader agreement alpha = 0.80). Lesion conspicuity was significantly better in low-keV VMIs (p < 0.03) and worse in > 70-keV VMIs.

CONCLUSIONS

With low-keV VMI, PCD-CT yields significantly improved objective and subjective quality of arterial phase oncological imaging compared with EID-CT. This advantage may translate into higher diagnostic confidence and lower radiation dose protocols.

Dual-Energy CT of the Abdomen: Radiology In Training

A 61-year-old man with an esophageal cancer diagnosis underwent staging dual-energy CT of the chest and abdomen in the portal venous phase after contrast media administration. Aside from the primary tumor and suspicious local lymph nodes, CT revealed hypoattenuating ambiguous liver lesions, an incidental right adrenal nodule, and a right renal lesion with soft-tissue attenuation. In addition, advanced atherosclerosis of the abdominal aorta and its major branches was noted. This article provides a case-based review of dual-energy CT technologies and their applications in the abdomen. The clinical utility of virtual monoenergetic images, virtual unenhanced images, and iodine maps is discussed.

Deep learning-based reconstruction of virtual monoenergetic images of kVp-switching dual energy CT for evaluation of hypervascular liver lesions: Comparison with standard reconstruction technique

OBJECTIVE

To investigate clinical applicability of deep learning(DL)-based reconstruction of virtual monoenergetic images(VMIs) of arterial phase liver CT obtained by rapid kVp-switching dual-energy CT for evaluation of hypervascular liver lesions.

MATERIALS AND METHODS

We retrospectively included 109 patients who had available late arterial phase liver CT images of the liver obtained with a rapid switching kVp DECT scanner for suspicious intra-abdominal malignancies. Two VMIs of 70 keV and 40 keV were reconstructed using adaptive statistical iterative reconstruction (ASiR-V) for arterial phase scans. VMIs at 40 keV were additionally reconstructed with a vendor-agnostic DL-based reconstruction technique (ClariCT.AI, ClariPi, DL 40 keV). Qualitative, quantitative image quality and subjective diagnostic acceptability were compared according to reconstruction techniques.

RESULTS

In qualitative analysis, DL 40 keV images showed less image noise (4.55 vs 3.11 vs 3.95, p < 0.001), better image sharpness (4.75 vs 4.16 vs 4.3, p < 0.001), better image contrast (4.98 vs 4.72 vs 4.19, p < 0.017), better lesion conspicuity (4.61 vs 4.23 vs 3.4, p < 0.001) and diagnostic acceptability (4.59 vs 3.88 vs 4.09, p < 0.001) compared with ASiR-V 40 keV or 70 keV image sets. In quantitative analysis, DL 40 keV significantly reduced image noise relative to ASiR-V 40 keV images (49.9%, p < 0.001) and ASiR-V 70 keV images (85.2%, p = 0.012). DL 40 keV images showed significantly higher CNR_{lesion to the liver} and SNR_liver than ASiR-V 40 keV image and 70 keV images (p < 0.001).

CONCLUSION

DL-based reconstruction of 40 keV images using vendor-agnostic software showed greater noise reduction, better lesion conspicuity, image contrast, image sharpness, and higher overall image diagnostic acceptability than ASiR for 40 keV or 70 keV images in patients with hypervascular liver lesions.

Evaluation of Hypervascular Focal Liver Lesions Utilizing Virtual Monoenergetic Images from Third-Generation Dual-Source Dual-Energy Computed Tomography

Objectives The purpose of our study was to evaluate the virtual monochromatic imaging in detecting hypervascular focal liver lesions in the late arterial phase with third-generation dual-source dual-energy computed tomography and to assess its image quality.

Materials and Methods In our study, 80 patients were included. Contrast-enhanced images in the late arterial phase (in the dual-energy mode) were acquired and were post-processed in Syngo, via workstation, using Monoenergetic + software. Five sets of images, one polychromatic energy image (corresponding to 120 kVp single-energy image) and four virtual monoenergetic image (VMI) sets at 40, 50, 60, and 70 keV levels, were generated. All these images were analyzed both objectively and subjectively. The attenuation values were measured, and the contrast-to-noise ratio (CNR) of liver and tumor were measured and compared objectively in each dataset. Image noise, image contrast, and diagnostic confidence for liver lesion detection were analyzed subjectively using a five-point scale system. Statistical analysis was performed using Kolmogorov–Smirnov, analysis of variance, and Kruskal–Wallis tests.

Results Among the VMI, maximum image noise was observed in the 40 keV image, with a gradual reduction in the image noise being noted with an increase in the VMI energy. The CNR of the hepatic parenchyma and the tumor gradually increased with a reduction in VMI energy from 70 to 40 keV. On subjective analysis, image contrast and image noise were observed to be more in low VMI datasets. In lesion detection, diagnostic confidence with an excellent confidence level was observed with a decrease in VMI energy.

Conclusion VMI datasets of 40 to 70 keV from third-generation dual-source DECT provide superior diagnostic accuracy for detecting hypervascular liver lesions. Considering the image noise and lesion detection rate among the VMI datasets, 60 keV VMI is the most helpful dataset for increased liver lesion detection with good image quality.

Utility of Dual-Energy CT in Abdominal Interventions

Dual-energy computed tomography (DECT) is an emerging CT technique based on data acquisition at two different settings. Various postprocessing techniques generate different sets of images, each with unique advantages. With DECT, it is possible to obtain virtual unenhanced images from monochromatic reconstructions and attenuation maps of different elements, thereby improving the detection and characterization of a variety of lesions. Presently, DECT is widely used to evaluate pulmonary embolism, characterize abdominal masses, determine the composition of urinary calculi, and detect tophi in gout. CT angiography is an essential prerequisite for endovascular intervention. DECT allows a better quality of angiographic images with a lesser dose of contrast. Various postprocessing techniques in DECT also help in a better evaluation of response to locoregional therapy. Virtual noncontrast images and iodine map differentiate residual or recurrent tumors from intrinsically hyperdense materials. Superior metallic artifact reduction allows better evaluation of vascular injuries adjacent to bony fractured fragments or previously deployed embolization coils. In addition to metal artifacts reduction, virtual monochromatic spectral imaging could further mitigate metal artifacts during CT-guided biopsy, providing an improved depiction of lesions and safe and versatile access for long puncture pathways. This article reviews and illustrates the different applications of DECT in various abdominal interventions. Familiarity with the capabilities of DECT may help interventional radiologists to improve their practice and ameliorate patient care.

Virtual monoenergetic images from dual-energy CT: systematic assessment of task-based image quality performance

Background

To compare task-based image quality (TB-IQ) among virtual monoenergetic images (VMI) and linear-blended images (LBI) from dual-energy CT as a function of contrast task, radiation dose, size, and lesion diameter.

Methods

A TB-IQ phantom (Mercury Phantom 4.0, Sun Nuclear Corporation) was imaged on a third-generation dual-source dual-energy CT with 100/Sn150 kVp at three volume CT dose levels (5, 10, 15 mGy). Three size sections (diameters 16, 26, 36 cm) with subsections for image noise and spatial resolution analysis were used. High-contrast tasks (e.g., calcium-containing stone and vascular lesion) were emulated using bone and iodine inserts. A low-contrast task (e.g., low-contrast lesion or hematoma) was emulated using a polystyrene insert. VMI at 40-190 keV and LBI were reconstructed. Noise power spectrum (NPS) determined the noise magnitude and texture. Spatial resolution was assessed using the task-transfer function (TTF) of the three inserts. The detectability index (d') served as TB-IQ metric.

Results

Noise magnitude increased with increasing phantom size, decreasing dose, and decreasing VMI-energy. Overall, noise magnitude was higher for VMI at 40-60 keV compared to LBI (range of noise increase, 3-124%). Blotchier noise texture was found for low and high VMIs (40-60 keV, 130-190 keV) compared to LBI. No difference in spatial resolution was observed for high contrast tasks. d' increased with increasing dose level or lesion diameter and decreasing size. For high-contrast tasks, d' was higher at 40-80 keV and lower at high VMIs. For the low-contrast task, d' was higher for VMI at 70-90 keV and lower at 40-60 keV.

Conclusions

Task-based image quality differed among VMI-energy and LBI dependent on the contrast task, dose level, phantom size, and lesion diameter. Image quality could be optimized by tailoring VMI-energy to the contrast task. Considering the clinical relevance of iodine, VMIs at 50-60 keV could be proposed as an alternative to LBI.

Virtual Monoenergetic Spectral Detector CT for Preoperative CT Angiography in Liver Donors

OBJECTIVE

The purpose of this study was to evaluate the use of virtual monoenergetic images (VMI) in pre-operative CT angiography of potential donors for living donor adult liver transplantation (LDALT), and to determine the optimal energy level to maximize vascular signal-to-noise and contrast-to-noise ratios (SNR and CNR, respectively).

MATERIALS AND METHODS

We retrospectively evaluated 29 CT angiography studies performed preoperatively in potential liver donors on a spectral detector CT scanner. All studies included arterial, early venous, and delayed venous phase imaging. Conventional polyenergetic images were generated for each patient, as well as virtual monoenergetic images in 10 keV increments from 40 -100 keV. Arteries (aorta and celiac, superior mesenteric, common hepatic, right and left hepatic arteries) were assessed on arterial phase images; portal venous system branches (splenic, superior mesenteric, main, right, and left portal veins) on early venous phase images; and hepatic veins on late venous phase images. Vascular attenuation, background parenchymal attenuation, and noise were measured on each set of virtual monoenergetic and conventional images.

RESULTS

Background hepatic and vascular noise decreased with increasing keV, with the lowest noise at 100 keV. Vascular SNR and CNR increased with decreasing keV and were highest at 40 keV, with statistical significance compared with conventional ( P < 0.05).

CONCLUSIONS

In preoperative CT angiography for potential liver donors, the optimal keV for assessing the vasculature to improve SNR and CNR is 40 keV. Use of low keV VMI in LDALT CT protocols may facilitate detection of vascular anatomical variants that can impact surgical planning.

Impact of dual-energy 50-keV virtual monoenergetic images on radiologist confidence in detection of key imaging findings of small hepatocellular carcinomas using multiphase liver CT

BACKGROUND

Dual-energy virtual monoenergetic images can increase iodine signal, potentially increasing the conspicuity of hepatic masses.

PURPOSE

To determine if dual-energy 50-keV virtual monoenergetic images improve visualization of key imaging findings or diagnostic confidence for small (≤2 cm) hepatocellular carcinomas (HCC) at multiphase, contrast-enhanced liver computed tomography (CT).

MATERIAL AND METHODS

Patients with chronic liver disease underwent multiphase dual-energy CT imaging for HCC, with late arterial and delayed phase dual-energy 50-keV images reconstructed. Two non-reader subspecialized gastrointestinal (GI) radiologists established the reference standard, determining the location and diagnosis of all hepatic lesions using predetermined criteria. Three GI radiologists interpreted mixed kV CT images without or with dual-energy 50-keV images. Radiologists identified potential HCCs and rated their confidence (0-100 scales) in imaging findings of arterial enhancement, enhancing capsule, tumor washout, and LI-RADS 5 (2018) category.

RESULTS

In total, 45 patients (14 women; mean age = 59.5 ± 10.9 years) with chronic liver disease were included. Of them, 19 patients had 25 HCCs ≤2 cm (mean size = 1.5 ± 0.4 cm). There were 17 LI-RADS 3 and 4 lesions and 19 benign lesions. Reader confidence in imaging findings of arterial enhancement, enhancing capsule, and non-peripheral washout significantly increased with dual-energy images (P ≤ 0.022). Overall confidence in HCC diagnosis increased significantly with dual-energy 50-keV images (52.4 vs. 68.8; P = 0.001). Dual-energy images demonstrated a slight but significant decrease in overall image quality.

CONCLUSION

Radiologist confidence in key imaging features of small HCCs and confidence in imaging diagnosis increases with use of dual-energy 50-keV images at multiphase, contrast-enhanced liver CT.

Generating synthetic contrast enhancement from non-contrast chest computed tomography using a generative adversarial network

This study aimed to evaluate a deep learning model for generating synthetic contrast-enhanced CT (sCECT) from non-contrast chest CT (NCCT). A deep learning model was applied to generate sCECT from NCCT. We collected three separate data sets, the development set (n = 25) for model training and tuning, test set 1 (n = 25) for technical evaluation, and test set 2 (n = 12) for clinical utility evaluation. In test set 1, image similarity metrics were calculated. In test set 2, the lesion contrast-to-noise ratio of the mediastinal lymph nodes was measured, and an observer study was conducted to compare lesion conspicuity. Comparisons were performed using the paired t-test or Wilcoxon signed-rank test. In test set 1, sCECT showed a lower mean absolute error (41.72 vs 48.74; P < .001), higher peak signal-to-noise ratio (17.44 vs 15.97; P < .001), higher multiscale structural similarity index measurement (0.84 vs 0.81; P < .001), and lower learned perceptual image patch similarity metric (0.14 vs 0.15; P < .001) than NCCT. In test set 2, the contrast-to-noise ratio of the mediastinal lymph nodes was higher in the sCECT group than in the NCCT group (6.15 ± 5.18 vs 0.74 ± 0.69; P < .001). The observer study showed for all reviewers higher lesion conspicuity in NCCT with sCECT than in NCCT alone (P ≤ .001). Synthetic CECT generated from NCCT improves the depiction of mediastinal lymph nodes.

Virtual Monoenergetic Images of Dual-Energy CT-Impact on Repeatability, Reproducibility, and Classification in Radiomics

Simple Summary

Virtual monoenergetic images from dual-energy CT are incrementally used in routine clinical practice. Thus, radiomic analysis will be more often performed on these images in the future. This study characterized the test–retest repeatability and reproducibility of radiomic features from virtual monoenergetic images and their impact on machine-learning-based lesion classification. The results of this study provide a basis to improve radiomic analyses and identify the role of feature stability in classification tasks when using virtual monoenergetic imaging with different scan or reconstruction parameters in multicenter clinical studies.

Abstract

The purpose of this study was to (i) evaluate the test–retest repeatability and reproducibility of radiomic features in virtual monoenergetic images (VMI) from dual-energy CT (DECT) depending on VMI energy (40, 50, 75, 120, 190 keV), radiation dose (5 and 15 mGy), and DECT approach (dual-source and split-filter DECT) in a phantom (ex vivo), and (ii) to assess the impact of VMI energy and feature repeatability on machine-learning-based classification in vivo in 72 patients with 72 hypodense liver lesions. Feature repeatability and reproducibility were determined by concordance–correlation–coefficient (CCC) and dynamic range (DR) ≥0.9. Test–retest repeatability was high within the same VMI energies and scan conditions (percentage of repeatable features ranging from 74% for SFDE mode at 40 keV and 15 mGy to 86% for DSDE at 190 keV and 15 mGy), while reproducibility varied substantially across different VMI energies and DECTs (percentage of reproducible features ranging from 32.8% for SFDE at 5 mGy comparing 40 with 190 keV to 99.2% for DSDE at 15 mGy comparing 40 with 50 keV). No major differences were observed between the two radiation doses (<10%) in all pair-wise comparisons. In vivo, machine learning classification using penalized regression and random forests resulted in the best discrimination of hemangiomas and metastases at low-energy VMI (40 keV), and for cysts at high-energy VMI (120 keV). Feature selection based on feature repeatability did not improve classification performance. Our results demonstrate the high repeatability of radiomics features when keeping scan and reconstruction conditions constant. Reproducibility diminished when using different VMI energies or DECT approaches. The choice of optimal VMI energy improved lesion classification in vivo and should hence be adapted to the specific task.

Comparison of low kVp CT and dual-energy CT for the evaluation of hypervascular hepatocellular carcinoma

PURPOSE

To compare lesion conspicuity and image quality of arterial phase images obtained from low kVp (90-kVp) and dual-energy (DE) scans for the evaluation of hypervascular hepatocellular carcinoma (HCC).

METHODS

This retrospective study included 229 patients with HCC who underwent either 90 kVp (n = 106) or DE scan (80- and 150-kVp with a tin filter) (n = 123) during the arterial phase. DE scans were reconstructed into a linearly blended image with a mixed ratio of 0.6 (60% 80kVp and 40% 150 kVp) and post-processed for 40 keV and 50 keV images. The contrast-to-noise ratio (CNR) of HCC to the liver and image noise was measured. Lesion conspicuity, liver parenchymal image quality, and overall image preference were assessed qualitatively by three independent radiologists.

RESULTS

DE 40 keV images had the highest CNR of HCC, and DE blended images had the lowest image noise among four image sets (p = 0.01 and p < 0.001, respectively). There was no significant difference in mean volume CT dose index and dose-length product between DE and low kVp scan (ps > 0.05). For qualitative analyses, DE blended images had the highest scores for image quality and overall image preference (ps < 0.001).

CONCLUSION

At an equal radiation dose, DE 40 keV showed higher CNR of HCC and DE blended image showed higher image quality and image preference compared with low kVp CT.

Inter-scan and inter-scanner variation of quantitative dual-energy CT: evaluation with three different scanner types

OBJECTIVES

To investigate inter-scan and inter-scanner variation of iodine concentration (IC) and attenuation in virtual monoenergetic images at 65 keV (HU65keV) in patients with repeated abdominal examinations on dual-source (dsDECT), rapid kV switching (rsDECT), and dual-layer detector DECT (dlDECT).

METHODS

We retrospectively included 131 patients who underwent two abdominal DECT examinations on the same scanner (dsDECT: n = 46, rsDECT: n = 45, dlDECT: n = 40). IC and HU65keV were measured by placing regions of interest in the liver, spleen, kidneys, aorta, portal vein, and inferior vena cava. Overall IC and HU65keV for each scanner, their inter-scan differences and proportional variation were calculated and compared between scanner types.

RESULTS

The three scanner-specific cohorts showed similar weight, body diameter, age, sex, and contrast media injection parameters as well as inter-scan differences hereof (p range: 0.23-0.99). Absolute inter-scan differences of HU65keV and IC were comparable between scanners (p range: 0.08-1.0). Overall inter-scan variation was significantly higher in IC than HU65keV (p < 0.05). For the liver, rsDECT showed significantly lower inter-scan variation of IC compared to dsDECT/dlDECT (p = 0.005/0.01), while for the spleen, this difference was only significant compared to dsDECT (p = 0.015). Normalizing IC of the liver to the portal vein and of the spleen to the aorta did not significantly reduce inter-scan variation (p = 0.97 and 0.50).

CONCLUSIONS

Iodine measurements across different DECT scanners show inter-scan variation which is higher compared to variation of attenuation values. Inter-scanner differences in longitudinal variation and overall iodine concentration depend on the scanner pairs and organs assessed and should be acknowledged in clinical and scientific DECT applications.

KEY POINTS

• All scanner types showed comparable inter-scan variation of attenuation, while for iodine, the rapid kV switching DECT showed lower variability in the liver and spleen. • Iodine concentration showed higher inter-scan variation than attenuation measurements; normalization to vessels did not significantly improve inter-scan reproducibility of iodine concentration in parenchymal organs. • Differences between the three scanner types regarding overall iodine concentration and attenuation obtained from both timepoints were within the range of average intra-patient, inter-scan differences for most assessed organs and vessels.

Differentiation of Hepatocellular Carcinoma from Hepatic Hemangioma and Focal Nodular Hyperplasia using Computed Tomographic Spectral Imaging

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy. This study was designed to investigate the value of computed tomography (CT) spectral imaging in differentiating HCC from hepatic hemangioma (HH) and focal nodular hyperplasia (FNH).

METHODS

This was a retrospective study of 51 patients who underwent spectral multiple-phase CT at 40-140 keV during the arterial phase (AP) and portal venous phase (PP). Slopes of the spectral curves, iodine density, water density derived from iodine- and water-based material decomposition images, iodine uptake ratio (IUR), normalized iodine concentration, and the ratio of iodine concentration in liver lesions between AP and PP were measured or calculated.

RESULTS

As energy level decreased, the CT values of HCC (n=31), HH (n=17), and FNH (n=7) increased in both AP and PP. There were significant differences in IUR in the AP, IUR in the PP, normalized iodine concentration in the AP, slope in the AP, and slope in the PP among HCC, HH, and FNH. The CT values in AP, IUR in the AP and PP, normalized iodine concentration in the AP, slope in the AP and PP had high sensitivity and specificity in differentiating HH and HCC from FNH. Quantitative CT spectral data had higher sensitivity and specificity than conventional qualitative CT image analysis during the combined phases.

CONCLUSIONS

Mean CT values at low energy (40-90 keV) and quantitative analysis of CT spectral data (IUR in the AP) could be helpful in the differentiation of HCC, HH, and FNH.

An Individualized Contrast-Enhanced Liver Computed Tomography Imaging Protocol Based on Body Mass Index in 126 Patients Seen for Liver Cirrhosis

Background

Computed tomography (CT) imaging using iodinated contrast medium is associated with the radiation dose to the patient, which may require reduction in individual circumstances. This study aimed to evaluate an individualized liver CT protocol based on body mass index (BMI) in 126 patients investigated for liver cirrhosis.

Material/Methods

From November 2017 to December 2020, in this prospective study, 126 patients with known or suspected liver cirrhosis were recruited. Patients underwent liver CT using individualized protocols based on BMI, as follows. BMI ≤24.0 kg/m²: 80 kV, 352 mg I/kg; BMI 24.1–28.0 kg/m²: 100 kV, 440 mg I/kg; BMI ≥28.1 kg/m²: 120 kV, 550 mg I/kg. Figure of merit (FOM) and size-specific dose estimates (SSDEs) were calculated and compared using the Mann-Whitney U test. Subjective image quality and timing adequacy of the late arterial phase were evaluated with Likert scales.

Results

The SSDE was significantly lower in the 80 kV protocol, corresponding to a dose reduction of 36% and 50% compared with the others (all P<0.001). In the comparison of 80-, 100-, and 120-kV protocols, no statistically significant differences were found in FOMs (P=0.108~0.620). Of all the examinations, 95.2% (120 of 126) were considered as appropriate timing for the late arterial phase. In addition, overall image quality, hepatocellular carcinoma conspicuity, and detection rate did not differ significantly among the 3 protocols (P=0.383~0.737).

Conclusions

This study demonstrated the feasibility of using an individualized liver CT protocol based on BMI, and showed that patients with lower BMI should receive lower doses of iodinated contrast medium and significantly reduced radiation dose.

Value of spectral detector computed tomography for the early assessment of technique efficacy after microwave ablation of hepatocellular carcinoma

OBJECTIVES

To investigate whether virtual monoenergetic images (VMI) and iodine maps derived from spectral detector computed tomography (SDCT) improve early assessment of technique efficacy in patients who underwent microwave ablation (MWA) for hepatocellular carcinoma (HCC) in liver cirrhosis.

METHODS

This retrospective study comprised 39 patients with 49 HCC lesions treated with MWA. Biphasic SDCT was performed 7.7±4.0 days after ablation. Conventional images (CI), VMI and IM were reconstructed. Signal- and contrast-to-noise ratio (SNR, CNR) in the ablation zone (AZ), hyperemic rim (HR) and liver parenchyma were calculated using regions-of-interest analysis and compared between CI and VMI between 40-100 keV. Iodine concentration and perfusion ratio of HR and residual tumor (RT) were measured. Two readers evaluated subjective contrast of AZ and HR, technique efficacy (complete vs. incomplete ablation) and diagnostic confidence at determining technique efficacy.

RESULTS

Attenuation of liver parenchyma, HR and RT, SNR of liver parenchyma and HR, CNR of AZ and HR were significantly higher in low-keV VMI compared to CI (all p<0.05). Iodine concentration and perfusion ratio differed significantly between HR and RT (all p<0.05; e.g. iodine concentration, 1.6±0.5 vs. 2.7±1.3 mg/ml). VMI50keV improved subjective AZ-to-liver contrast, HR-to-liver contrast, visualization of AZ margin and vessels adjacent to AZ compared to CI (all p<0.05). Diagnostic accuracy for detection of incomplete ablation was slightly higher in VMI50keV compared to CI (0.92 vs. 0.89), while diagnostic confidence was significantly higher in VMI50keV (p<0.05).

CONCLUSIONS

Spectral detector computed tomography derived low-keV virtual monoenergetic images and iodine maps provide superior early assessment of technique efficacy of MWA in HCC compared to CI.

Dual-energy CT arthrography: a feasibility study

OBJECTIVE

To evaluate the feasibility of producing 2-dimensional (2D) virtual noncontrast images and 3-dimensional (3D) bone models from dual-energy computed tomography (DECT) arthrograms and to determine whether this is best accomplished using 190 keV virtual monoenergetic images (VMI) or virtual unenhanced (VUE) images.

MATERIALS AND METHODS

VMI and VUE images were retrospectively reconstructed from patients with internal derangement of the shoulder or knee joint who underwent DECT arthrography between September 2017 and August 2019. A region of interest was placed in the area of brightest contrast, and the mean attenuation (in Hounsfield units [HUs]) was recorded. Two blinded musculoskeletal radiologists qualitatively graded the 2D images and 3D models using scores ranging from 0 to 3 (0 considered optimal).

RESULTS

Twenty-six patients (mean age ± SD, 57.5 ± 16.8 years; 6 women) were included in the study. The contrast attenuation on VUE images (overall mean ± SD, 10.5 ± 16.4 HU; knee, 19.3 ± 10.7 HU; shoulder, 5.0 ± 17.2 HU) was significantly lower (p < 0.001 for all comparisons) than on VMI (overall mean ± SD, 107.7 ± 43.8 HU; knee, 104.6 ± 31.1 HU; shoulder, 109.6 ± 51.0 HU). The proportion of cases with optimal scores (0 or 1) was significantly higher with VUE than with VMI for both 2D and 3D images (p < 0.001).

CONCLUSIONS

DECT arthrography can be used to produce 2D virtual noncontrast images and to generate 3D bone models. The VUE technique is superior to VMI in producing virtual noncontrast images.

Comparison of Multimaterial Decomposition Fat Fraction with DECT and Proton Density Fat Fraction with IDEAL IQ MRI for Quantification of Liver Steatosis in a Population Exposed to Chemotherapy

INTRODUCTION

Oncologic patients who develop chemotherapy-associated liver injury (CALI) secondary to chemotherapy treatment tend to have worse outcomes. Biopsy remains the gold standard for the diagnosis of hepatic steatosis. The purpose of this article is to compare 2 alternatives: Proton-Density-Fat-Fraction (PDFF) MRI and MultiMaterial-Decomposition (MMD) DECT.

MATERIALS AND METHODS

49 consecutive oncologic patients treated with Chemotherapy underwent abdominal DECT and abdominal MRI within 2 weeks of each other. Two radiologists tracked Regions of Interest independently both in the PDFF fat maps and in the MMD DECT fat maps. Non-parametric exact Wilcoxon signed rank test and Cohen's K were used to compare the 2 sequences and to evaluate the agreement.

RESULTS

There was no statistically significant difference in the fat fraction measured as a continuous value between PDFF and DECT between 2 readers. Within the same imaging method (PDFF) the degree of agreement based on the k coefficient between reader 1 and reader 2 is 0.88 (p-value < 0.05). Similarly, for single-source DECT(ssDECT) the degree of agreement based on the k coefficient between reader 1 and reader 2 is 0.97 (p-value < 0.05).

CONCLUSIONS

The results of this study demonstrate that the hepatic fat fraction of ssDECT with MMD are not significantly different from PDFF. This could be an advantage in an oncological population that undergoes serial CT scans for follow up of chemotherapy response.

Triple-Phase Computed Tomography May Replace Dual-Energy X-ray Absorptiometry Scan for Evaluation of Osteoporosis in Liver Transplant Candidates

Assessment of bone density is an important part of liver transplantation (LT) evaluation for early identification and treatment of osteoporosis. Dual-energy X-ray absorptiometry (DXA) is currently the standard clinical test for osteoporosis; however, it may contribute to the appointment burden on LT candidates during the cumbersome evaluation process, and there are limitations affecting its accuracy. In this study, we evaluate the utility of biomechanical analysis of vertebral images obtained during dual-energy abdominal triple-phase computed tomography (TPCT) in diagnosing osteoporosis among LT candidates. We retrospectively reviewed cases evaluated for LT between January 2017 and March 2018. All patients who underwent TPCT within 3 months of DXA were included. The biomechanical computed tomography (BCT) analysis was performed at a centralized laboratory (O.N. Diagnostics, Berkeley, CA) by 2 trained analysts blinded to the DXA data. DXA-based osteoporosis was defined as a T score ≤-2.5 at the hip or spine. BCT-based osteoporosis was defined as vertebral strength ≤4500 N for women or ≤6500 N for men or trabecular volumetric bone mineral density ≤80 mg/cm³ . Comparative data were available for 91 patients who had complete data for both DXA and BCT: 31 women and 60 men, age 54 ± 11 years (mean ± standard deviation), mean body mass index 28 ± 6 kg/m² . Using DXA as the clinical reference, sensitivity of BCT to detect DXA-defined osteoporosis was 83.3% (20/24 patients) and negative predictive value was 91.7%; specificity and positive predictive value were 65.7% and 46.5%, respectively. BCT analysis of vertebral images on triple-phase computed tomography, routinely obtained during transplant evaluation, can reliably rule out osteoporosis in LT candidates. Patients with suspicion of osteoporosis on TPCT may need further evaluation by DXA.

A Third-Generation Adaptive Statistical Iterative Reconstruction for Contrast-Enhanced 4-Dimensional Dual-Energy Computed Tomography for Pancreatic Cancer

OBJECTIVES

The objective of this study was to assess the objective and subjective qualities of the contrast-enhanced 4-dimensional dual-energy computed tomography using adaptive statistical iterative reconstruction (ASiR) and ASiR-V.

METHODS

The virtual monochromatic images at 60 keV were reconstructed using filtered back projection, ASiR, and ASiR-V (10%-100%) for 14 patients with pancreatic cancer. The contrast-to-noise ratio (CNR) was calculated, and the subjective measurements were compared based on a 5-point score scale.

RESULTS

The ASiR-V yielded a significantly higher CNR than ASiR (P < 0.05). The subjective image quality (peak) was significantly improved (P < 0.01) with ASiR (50%) (3.8, 3.5, and 4.0; overall image quality, tumor delineation, and noise, respectively) and with ASiR-V (50%) (3.9, 3.5, and 4.2, respectively) compared with the filtered back projection (3.2, 3.2, and 3.0, respectively).

CONCLUSIONS

The ASiR-V yielded higher CNR than ASiR and provided the highest subjective scores regarding the overall image quality.

Improvement of image quality and assessment of respiratory motion for hepatocellular carcinoma with portal vein tumor thrombosis using contrast-enhanced four-dimensional dual-energy computed tomography

To assess the objective and subjective image quality, and respiratory motion of hepatocellular carcinoma with portal vein tumor thrombosis (PVTT) using the contrast-enhanced four-dimensional dual-energy computed tomography (CE-4D-DECT). For twelve patients, the virtual monochromatic image (VMI) derived from the CE-4D-DECT with the highest contrast to noise ratio (CNR) was determined as the optimal VMI (O-VMI). To assess the objective and subjective image quality, the CNR and five-point score of the O-VMI were compared to those of the standard VMI at 77 keV (S-VMI). The respiratory motion of the PVTT and diaphragm was measured based on the exhale and inhale phase images. The VMI at 60 keV yielded the highest CNR (4.8 ± 1.4) which was significantly higher (p = 0.02) than that in the S-VMI (3.8 ± 1.2). The overall image quality (4.0 ± 0.6 vs 3.1 ± 0.5) and tumor conspicuity (3.8 ± 0.8 vs 2.8 ± 0.6) of the O-VMI determined by three radiation oncologists was significantly higher (p < 0.01) than that of the S-VMI. The diaphragm motion in the L-R (3.3 ± 2.5 vs 1.2 ± 1.1 mm), A-P (6.7 ± 4.0 vs 1.6 ± 1.3mm) and 3D (8.8 ± 3.5 vs 13.1 ± 4.9 mm) directions were significantly larger (p < 0.05) compared to the tumor motion. The improvement of objective and subjective image quality was achieved in the O-VMI. Because the respiratory motion of the diaphragm was larger than that of the PVTT, we need to be pay attention for localizing target in radiotherapy.

Clinical Implementation of Dual-Energy CT for Gastrointestinal Imaging

Dual-energy CT (DECT) overcomes several limitations of conventional single-energy CT (SECT) for the evaluation of gastrointestinal diseases. This article provides an overview of practical aspects of the DECT technology and acquisition protocols, reviews existing clinical applications, discusses current challenges, and describes future directions, with a focus on gastrointestinal imaging. A head-to-head comparison of technical specifications among DECT scanner implementations is provided. Energy- and material-specific DECT image reconstructions enable retrospective (i.e., after examination acquisition) image quality adjustments that are not possible using SECT. Such adjustments may, for example, correct insufficient contrast bolus or metal artifacts, thereby potentially avoiding patient recalls. A combination of low-energy monochromatic images, iodine maps, and virtual unenhanced images can be included in protocols to improve lesion detection and disease characterization. Relevant literature is reviewed regarding use of DECT for evaluation of the liver, gallbladder, pancreas, and bowel. Challenges involving cost, workflow, body habitus, and variability in DECT measurements are considered. Artificial intelligence and machine-learning image reconstruction algorithms, PACS integration, photon-counting hardware, and novel contrast agents are expected to expand the multienergy capability of DECT and further augment its value.

Virtual monoenergetic images from spectral detector computed tomography facilitate washout assessment in arterially hyper-enhancing liver lesions

Objectives

To investigate whether the increased soft tissue contrast of virtual monoenergetic images (VMIs) obtained from a spectral detector computed tomography (SDCT) system improves washout assessment of arterially hyper-enhancing liver lesions.

Methods

Fifty-nine arterially hyper-enhancing lesions in 31 patients (age 65 ± 9 years, M/W 20/11) were included in this IRB-approved study. All patients underwent multi-phase SDCT for HCC screening. MRI, CEUS or biopsy within 3 months served as standard of reference to classify lesions as LiRADS 3 or 4/5. VMIs and conventional images (CIs) were reconstructed. Visual analysis was performed on 40, 60, and 80 kiloelectronvolt (keV) and CIs by 3 radiologists. Presence and visibility of washout were assessed; image quality and confidence of washout evaluation were evaluated on 5-point Likert scales. Signal-to-noise ratio (SNR), lesion-to-liver contrast-to-noise ratio (CNR) (|HU_lesion–HU_liver|/SD_liver) and washout (|HU_lesion–HU_liver|) were calculated. Statistical assessment was performed using ANOVA and Wilcoxon test.

Results

On subjective lesion analysis, the highest level of diagnostic confidence and highest sensitivity for the detection of lesion washout were found for 40-keV VMIs (40 keV vs. CI, 81.3 vs. 71.3%). Image quality parameters were significantly better in low-kiloelectronvolt VMIs than in CIs (p < 0.05; e.g. SNR_liver: 40 keV vs. CIs, 12.5 ± 4.1 vs. 5.6 ± 1.6). In LiRADS 4/5 lesions, CNR and quantitative washout values were significantly higher in 40-keV VMIs compared to CIs (p < 0.05; e.g. CNR and washout in 40 keV vs. CIs, 2.3 ± 1.6 vs. 0.8 ± 0.5 and 29.0 ± 19.1 vs. 12.9 ± 6.9 HU, respectively).

Conclusion

By increasing lesion contrast, low-kiloelectronvolt VMIs obtained from SDCT improve washout assessment of hyper-enhancing liver lesions with respect to washout visibility and diagnostic confidence.

Key Points

• Low-kiloelectronvolt virtual monoenergetic images from spectral detector CT facilitate washout assessment in arterially hyper-enhancing liver lesions.

• Image quality and quantitative washout parameters as well as subjective washout visibility and diagnostic confidence benefit from low-kiloelectronvolt virtual monoenergetic images.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07379-3) contains supplementary material, which is available to authorized users.

Dual-energy CT in diffuse liver disease: is there a role?

Dual-energy CT (DECT) can be defined as the use of two different energy levels to identify and quantify material composition. Since its inception, DECT has benefited from remarkable improvements in hardware and clinical applications. DECT enables accurate identification and quantification of multiple materials, including fat, iron, and iodine. As a consequence, multiple studies have investigated the potential role of DECT in the assessment of diffuse liver diseases. While this role is evolving, this article aims to review the most relevant literature on use of DECT for assessment of diffuse liver diseases. Moreover, the basic concepts on DECT techniques, types of image reconstruction, and DECT-dedicated software will be described, focusing on the areas that are most relevant for the evaluation of diffuse liver diseases. Also, we will review the evidence of added value of DECT in detection and assessment of hepatocellular carcinoma which is a known risk in patients with diffuse liver disease.

New boundaries of liver imaging: from morphology to function

From an invisible organ to one of the most explored non-invasively, the liver is, today, one of the cornerstones for current cross-sectional imaging techniques and minimally invasive procedures. After the achievements of US, CT and, most recently, MRI in providing highly accurate morphological and structural information about the organ, a significant scientific development has gained momentum for the last decades, coupling morphology to liver function and contributing far most to what we know today as precision medicine. In fact, dedicated tailor-made investigations are now possible in order to detect and, most of all, quantify physiopathological processes with unprecedented certitude. It is the intention of this review to provide a better insight to the reader of several functional imaging techniques applied to liver imaging. Contrast enhanced imaging, diffusion weighted imaging, elastography, spectral computed tomography and fat and iron assessment techniques are commonly performed clinically. Diffusion kurtosis imaging, magnetic resonance spectroscopy, T1 relaxometry and radiomics remain largely limited to advanced clinical research. Each of them has its own value and place on the diagnostic armamentarium and provide unique qualitative and quantitative information regarding the pathophysiology of diseases, contributing at a large scale to model therapeutic decisions and patient follow-up. Therefore, state-of-the-art liver imaging acts today as a non-invasive surrogate biomarker of many focal and diffuse liver diseases.

Pancreatic Ductal Adenocarcinoma: Interface Enhancement Gradient Measured on Dual-Energy CT Images Improves Prognostic Evaluation

Purpose

To investigate the prognostic value of differential enhancement on baseline dual-energy CT images in patients with treatment-naive pancreatic ductal adenocarcinoma (PDAC), with a focus on tumor-host interface characterization.

Materials and Methods

This was a retrospective, institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study of 158 consecutive adult patients (mean age, 68 years; age range, 40.9-88.9 years; 50% women) with histopathologically proven, treatment-naive PDAC, who had undergone multiphasic pancreatic dual-energy CT from December 2011 to March 2017. Regions of interest in tumor core, tumor border, pancreas border with tumor, nontumoral pancreas, and aorta were recorded on pancreatic parenchymal phase (PPP) dual-energy CT 70-keV, 52-keV, and iodine material density (MD) images, plus portal venous phase (PVP) conventional CT images. Enhancement gradient (delta) across the tumor-pancreas interface was calculated. Delta was evaluated combining the dual-energy CT values with the PVP values and as individual predictors. Receiver operating characteristic analysis with logistic regression was used to determine the optimal cut point for each dual-energy CT delta to predict disease outcome based on highest Youden index. Survival curves were generated using Kaplan-Meier method, and comparison between two independent groups (high and low delta) was evaluated with log-rank test. Clinical outcomes included overall survival and distant metastasis-free survival. Three independent blinded radiologists visually scored tumor conspicuity (subjective delta score) on a 1-5 scale, and agreement was evaluated with κ statistic.

Results

Ninety-three patients had advanced stage (50 locally advanced and 43 metastatic) and 65 had lower stage (48 resectable and 17 borderline resectable) tumors. Patients with high delta tumors (≥ 40 HU) on either 70-keV PPP images or conventional PVP images had significantly shorter overall survival compared with those with low delta tumors (< 40 HU) in both early stage PDAC (13.5 months vs 23.3 months; hazard ratio [HR], 1.87; 95% confidence interval [CI]: 1.01, 3.5; P = .04) and advanced stage PDAC (10.8 months vs 18.0 months; HR, 2.1; 95% CI: 1.28, 3.6; P = .003). Qualitative visual scoring of tumor conspicuity also showed shorter overall survival in patients with more conspicuous tumors. Highest interreader agreement for subjective delta score was 0.73 and 0.60 using iodine MD and 52-keV images, respectively.

Conclusion

Increased quantitative and qualitative border conspicuity (high delta) is associated with shorter survival in patients with PDAC. Agreement on the subjective qualitative characterization of PDAC borders is best achieved using iodine MD and lower-energy simulated monoenergetic images at pancreatic protocol dual-energy CT.Keywords: Abdomen/GI, CT, CT-Dual Energy, CT-Quantitative, PancreasSupplemental material is available for this article.© RSNA, 2020.