Principles and Applications of Dual-Layer Spectral CT in Gastrointestinal Imaging

A Clinical-Radiomic Combined Model based on Dual-Layer Spectral CT for Predicting Pathological T4 in Gastric Cancer

RATIONALE AND OBJECTIVES

This study aimed to develop and validate a dual-layer spectral CT based clinical-radiomic model for pre-treatment prediction of pathological T4 (pT4) in gastric cancer (GC) patients.

MATERIALS AND METHODS

This retrospective study included 148 surgically confirmed GC patients who underwent dual-layer spectral CT scanning before surgery and were divided into a training (n=104) and test (n=44) cohorts. Subjective assessments were performed based on conventional 120-kV CT images by two readers. Clinical models were developed using patient demographics, serum tumor markers, and image features from CT scans. Radiomics model included features extracted from conventional 120-kV CT and dual-layer CT-derived spectral base image (SBI), such as virtual monoenergetic images (40 keV, 70 keV, 100 keV), iodine density (ID), effective atomic number (Zeff), and electron density (ED) images for both the arterial phase (AP) and portal venous phase (PVP). A clinical-radiomic combined model was developed and visualized using a nomogram.

RESULTS

Tumor thickness on CT and serum level of CA19-9 levels were identified as independent predictors. The clinical-radiomic combined model demonstrated superior performance compared to subjective image interpretation and other models, with an AUC of 0.906 (95% CI, 0.848-0.963) in the training cohort and 0.873 in the test cohort. The nomogram was significantly associated with pT4 status, supporting its potential utility in clinical prediction.

CONCLUSION

The integration of clinical characteristics with radiomic features from conventional CT and dual-layer CT-derived SBI achieved a high diagnostic accuracy for predicting pT4 in GC patients. This combined approach could assist in treatment planning and patient management in GC.

Performance of novel multiparametric second-generation dual-layer spectral detector CT in gouty arthritis

OBJECTIVES

This study aimed to compare the performance of different dual-energy computed tomography (DECT) technologies in detecting monosodium urate (MSU) crystals and evaluate the potential clinical value of novel second-generation dual-layer spectral detector CT (dlDECT) in gouty arthritis.

METHODS

Using data collected from a tertiary hospital, we examined the diagnostic accuracy of different DECT technologies for the diagnosis of MSU. We used two standards: (1) demonstration of MSU crystals in synovial fluid (gold) and (2) 2015 ACR/EULAR gout classification criteria (silver). Furthermore, six novel spectral parameters derived from dlDECT were quantitatively calculated and analyzed for MSU diagnostic efficiency.

RESULTS

Of the 243 patients with 387 joints, 68 (27.98%) had synovial fluid analysis. Compared with the gold standard, MSU diagnostic accuracy statistics for dlDECT, dual-source DECT (dsDECT) and rapid kilovolt peak switching DECT (rsDECT) were as follows: area under the curve (AUC): 0.85, 0.80 and 0.75, respectively. Findings were replicated compared with the silver standard. Multiparametric analysis in dlDECT demonstrated the highest MSU detection rate (92.86%), significantly higher than rsDECT (42.08%) and dsDECT (85.80%). Among novel parameters in dlDECT, Calcium-suppressed index 25 (CaSupp-I 25) exhibited the best performance in distinguishing materials (MSU, muscle, and bone), with an AUC of 0.992. The differentiation was also aided by histograms, scatter plots, and attenuation curves.

CONCLUSION

The novel dlDECT is likely not inferior to other DECT technologies in MSU detection, especially its spectral parameter CaSupp-I 25. Multiparameter analysis showed the potential value for detecting MSU crystals in gouty arthritis, providing valuable clinical insights for gout diagnosis.

KEY POINTS

Question The performance of different DECT technologies in detecting monosodium urate (MSU), and the value of dual-layer spectral detector CT (dlDECT) in gouty arthritis remains unclear. Findings The dlDECT was likely not inferior to other DECT technologies in MSU detection, and its multiparametric analysis provided valuable information for MSU diagnosis. Clinical relevance Novel dlDECT may improve the accurate detection of MSU crystals in gouty arthritis compared to other DECT technologies, providing valuable clinical insights and potentially improving patient outcomes for more precise gout diagnosis.

Diffuse and focal liver fat: advanced imaging techniques and diagnostic insights

The fatty liver disease represents a complex, multifaceted challenge, requiring a multidisciplinary approach for effective management and research. This article uses conventional and advanced imaging techniques to explore the etiology, imaging patterns, and quantification methods of hepatic steatosis. Particular emphasis is placed on the challenges and advancements in the imaging diagnostics of fatty liver disease. Techniques such as ultrasound, CT, MRI, and elastography are indispensable for providing deep insights into the liver's fat content. These modalities not only distinguish between diffuse and focal steatosis but also help identify accompanying conditions, such as inflammation and fibrosis, which are critical for accurate diagnosis and management.

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.

Multi-energy CT material decomposition using graph model improved CNN

In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD). We proposed a novel MMD method based on graph edge-conditioned convolution U-net (GECCU-net) to enhance material image quality. The GECCU-net focuses on developing a multi-scale encoder. At the network coding stage, three paths are applied to capture comprehensive image features. The local and non-local feature aggregation (LNFA) blocks are designed to integrate the local and non-local features from different paths. The graph edge-conditioned convolution based on non-Euclidean space excavates the non-local features. A hybrid loss function is defined to accommodate multi-scale input images and avoid over-smoothing of results. The proposed network is compared quantitatively with base CNN models on the simulated and real datasets. The material images generated by GECCU-net have less noise and artifacts while retaining more information on tissue. The Structural SIMilarity (SSIM) of the obtained abdomen and chest water maps reaches 0.9976 and 0.9990, respectively, and the RMSE reduces to 0.1218 and 0.4903 g/cm3. The proposed method can improve MMD performance and has potential applications.

Transcatheter Aortic Valve Implantation (TAVI) Planning with Dual-Layer Spectral CT Using Virtual Monoenergetic Image (VMI) Reconstructions and 20 mL of Contrast Media

Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to surgical implantation and its implementation is progressively increasing worldwide. We routinely perform pre-procedural aortic angiography CT to assess aortic dimensions and vascular anatomy. This study aims to evaluate the image quality of CTA for TAVI planning using dual-layer spectral CT, with virtual monoenergetic image reconstructions at 40 keV. Thirty-one patients underwent a CTA protocol with the injection of 20 mL of contrast media. Image quality was assessed by measuring the mean density in Hounsfield Units (HU), the signal-to-noise ratio, and the contrast-to-noise ratio in VMI reconstructions. Additionally, a blinded subjective analysis was conducted by two observers. The results showed significant enhancement at all sampled vascular levels with a gradual decrease in HU from proximal to distal regions. Favourable subjective ratings were given for all parameters, with greater variability in the evaluation of iliac axes. A significant negative correlation (p < 0.05) was observed between BMI and CA at all vascular levels, indicating reduced contrast enhancement with increasing BMI. Spectral CT, along with reducing iodine load, allows for obtaining high-quality images without a significant increase in noise. The reduction in iodine load can have positive implications in clinical practice, improving patient safety and resource efficiency.

Quantitative Dual-Energy X-ray Imaging Based on K-Edge Absorption Difference

Conventional flat panel X-ray imaging (FPXI) employs a single scintillator for X-ray conversion, which lacks energy spectrum information. The recent innovation of employing multilayer scintillators offers a route for multispectral X-ray imaging. However, the principles guiding optimal multilayer scintillator configuration selection and quantitative analysis models remain largely unexplored. Here, we propose to adopt the K-edge absorption coefficient as a key parameter for selecting tandem scintillator combinations and to utilize the coefficient matrix to calculate the absorption efficiency spectrum of the sample. Through a dual scintillator example comprising C4H12NMnCl3 and Cs3Cu2I5, we establish a streamlined quantitative framework for deducing X-ray spectra from scintillation spectra, with an average relative error of 6.28% between the calculated and measured sample absorption spectrum. This insight forms the foundation for our quantitative method to distinguish the material densities. Leveraging this tandem scintillator configuration, in conjunction with our analytical tools, we successfully demonstrate the inherent merits of dual-energy X-ray imaging for discerning materials with varied densities and thicknesses.