Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future.

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. However, the increasing adoption of AI systems also raises concerns about their environmental impact, particularly in the context of climate change. This review explores the intersection of climate change and AI in healthcare, examining the challenges posed by the energy consumption and carbon footprint of AI systems, as well as the potential solutions to mitigate their environmental impact. The review highlights the energy-intensive nature of AI model training and deployment, the contribution of data centers to greenhouse gas emissions, and the generation of electronic waste. To address these challenges, the development of energy-efficient AI models, the adoption of green computing practices, and the integration of renewable energy sources are discussed as potential solutions. The review also emphasizes the role of AI in optimizing healthcare workflows, reducing resource waste, and facilitating sustainable practices such as telemedicine. Furthermore, the importance of policy and governance frameworks, global initiatives, and collaborative efforts in promoting sustainable AI practices in healthcare is explored. The review concludes by outlining best practices for sustainable AI deployment, including eco-design, lifecycle assessment, responsible data management, and continuous monitoring and improvement. As the healthcare industry continues to embrace AI technologies, prioritizing sustainability and environmental responsibility is crucial to ensure that the benefits of AI are realized while actively contributing to the preservation of our planet.

Super resolution deep learning reconstruction for coronary CT angiography: A structured phantom study.

Purpose: Super-resolution deep-learning-based reconstruction: SR-DLR is a newly developed and clinically available deep-learning-based image reconstruction method that can improve the spatial resolution of CT images. The image quality of the output from non-linear image reconstructions, such as DLR, is known to vary depending on the structure of the object being scanned, and a simple phantom cannot explicitly evaluate the clinical performance of SR-DLR. This study aims to accurately investigate the quality of the images reconstructed by SR-DLR by utilizing a structured phantom that simulates the human anatomy in coronary CT angiography. Methods: The structural phantom had ribs and vertebrae made of plaster, a left ventricle filled with dilute contrast medium, a coronary artery with simulated stenosis, and an implanted stent graft. By scanning the structured phantom, we evaluated noise and spatial resolution on the images reconstructed with SR-DLR and conventional reconstructions. Results: The spatial resolution of SR-DLR was higher than conventional reconstructions; the 10 % modulation transfer function of hybrid IR (HIR), DLR, and SR-DLR were 0.792-, 0.976-, and 1.379 cycle/mm, respectively. At the same time, image noise was lowest (HIR: 21.1-, DLR: 19.0-, and SR-DLR: 13.1 HU). SR-DLR could accurately assess coronary artery stenosis and the lumen of the implanted stent graft. Conclusions: SR-DLR can obtain CT images with high spatial resolution and lower noise without special CT equipments, and will help diagnose coronary artery disease in CCTA and other CT examinations that require high spatial resolution.

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

Fairness of artificial intelligence in healthcare: review and recommendations.

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

Revolutionizing radiation therapy: the role of AI in clinical practice.

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

From FDG and beyond: the evolving potential of nuclear medicine.

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

New trend in artificial intelligence-based assistive technology for thoracic imaging.

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction.

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.

18F-sodium fluoride positron emission tomography following coronary computed tomography angiography in predicting long-term coronary events: a 5-year follow-up study.

PURPOSE: The predictive value of 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) in combination with coronary computed tomography (CT) angiography (CCTA) for future coronary events has attracted interest. We evaluated the potential of 18F-NaF PET/CT following CCTA to predict major coronary events (MACE) during a 5-year follow-up period. METHODS: Forty patients with coronary atherosclerotic lesions detected on CCTA underwent 18F-NaF PET/CT examination. Each lesion was evaluated for luminal stenosis and high-risk plaque (HRP) with < 30 Hounsfield units and a > 1.1 remodeling index on CCTA. Focal 18F-NaF uptake in each lesion was quantified using the maximum tissue-to-background ratio (TBRmax), and the maximum TBRmax per patient (M-TBRmax) was determined. We followed MACE (cardiac death, acute coronary syndrome, and/or coronary revascularization > 6 months after 18F-NaF PET/CT) for 5 years. RESULTS: In total, 142 coronary lesions were analyzed. Eleven patients experienced any MACE. Patients with MACE showed a higher M-TBRmax than those without (1.40 ± .19 vs. 1.18 ± .18, P = .0011), and the optimal M-TBRmax cutoff to predict MACE was 1.29. Patients with M-TBRmax of ≥ 1.29 had a higher risk of MACE than those with lower values (P = .012, log-rank test), whereas patients with obstructive stenosis and those with HRP did not. Multivariate Cox proportional analysis adjusted for age, sex, coronary risk factors, and CCTA findings showed that M-TBRmax of ≥ 1.29 remained an independent predictor of 5-year MACE (hazard ratio, 5.4; 95% confidence interval, 1.1-25.4; P = .034). CONCLUSION: 18F-NaF PET/CT following CCTA provides useful strategies to predict 5-year MACE.

Clinical applications of artificial intelligence in liver imaging.

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

The reliability and utility of on-site CT-derived fractional flow reserve (FFR) based on fluid structure interactions: comparison with FFRCT based on computational fluid dynamics, invasive FFR, and resting full-cycle ratio.

Fractional flow reserve (FFR) derived off-site by coronary computed tomography angiography (CCTA) (FFRCT) is obtained by applying the principles of computational fluid dynamics. This study aimed to validate the overall reliability of on-site CCTA-derived FFR based on fluid structure interactions (CT-FFR) and assess its clinical utility compared with FFRCT, invasive FFR, and resting full-cycle ratio (RFR). We calculated the CT-FFR for 924 coronary vessels in 308 patients who underwent CCTA for clinically suspected coronary artery disease. Of these patients, 35 patients with at least one obstructive stenosis (> 50%) detected on CCTA underwent both CT-FFR and FFRCT for further investigation. Furthermore, 24 and 20 patients underwent invasive FFR and RFR in addition to CT-FFR, respectively. The inter-observer correlation (r) of CT-FFR was 0.93 (95% confidence interval [CI] 0.85-0.97, P < 0.0001) with a mean absolute difference of - 0.0042 (limits of agreement - 0.073, 0.064); 97.3% of coronary arteries without obstructive lesions on CCTA had negative results for ischemia on CT-FFR (> 0.80). The correlation coefficient between CT-FFR and FFRCT for 105 coronary vessels was 0.87 (95% CI 0.82-0.91, P < 0.0001) with a mean absolute difference of - 0.012 (limits of agreement - 0.12, 0.10). CT-FFR correlated well with both invasive FFR (r = 0.66, 95% CI 0.36-0.84, P = 0.0003) and RFR (r = 0.78, 95% CI 0.51-0.91, P < 0.0001). These data suggest that CT-FFR can potentially substitute for FFRCT and correlates closely with invasive FFR and RFR with high reproducibility. Our findings should be proven by further clinical investigation in a larger cohort.

Iodine maps derived from sparse-view kV-switching dual-energy CT equipped with a deep learning reconstruction for diagnosis of hepatocellular carcinoma.

Deep learning-based spectral CT imaging (DL-SCTI) is a novel type of fast kilovolt-switching dual-energy CT equipped with a cascaded deep-learning reconstruction which completes the views missing in the sinogram space and improves the image quality in the image space because it uses deep convolutional neural networks trained on fully sampled dual-energy data acquired via dual kV rotations. We investigated the clinical utility of iodine maps generated from DL-SCTI scans for assessing hepatocellular carcinoma (HCC). In the clinical study, dynamic DL-SCTI scans (tube voltage 135 and 80 kV) were acquired in 52 patients with hypervascular HCCs whose vascularity was confirmed by CT during hepatic arteriography. Virtual monochromatic 70 keV images served as the reference images. Iodine maps were reconstructed using three-material decomposition (fat, healthy liver tissue, iodine). A radiologist calculated the contrast-to-noise ratio (CNR) during the hepatic arterial phase (CNRa) and the equilibrium phase (CNRe). In the phantom study, DL-SCTI scans (tube voltage 135 and 80 kV) were acquired to assess the accuracy of iodine maps; the iodine concentration was known. The CNRa was significantly higher on the iodine maps than on 70 keV images (p < 0.01). The CNRe was significantly higher on 70 keV images than on iodine maps (p < 0.01). The estimated iodine concentration derived from DL-SCTI scans in the phantom study was highly correlated with the known iodine concentration. It was underestimated in small-diameter modules and in large-diameter modules with an iodine concentration of less than 2.0 mgI/ml. Iodine maps generated from DL-SCTI scans can improve the CNR for HCCs during hepatic arterial phase but not during equilibrium phase in comparison with virtual monochromatic 70 keV images. Also, when the lesion is small or the iodine concentration is low, iodine quantification may result in underestimation.

Evaluation of the second-generation whole-heart motion correction algorithm (SSF2) used to demonstrate the aortic annulus on cardiac CT.

The main purpose of pre-transcatheter aortic valve implantation (TAVI) cardiac computed tomography (CT) for patients with severe aortic stenosis is aortic annulus measurements. However, motion artifacts present a technical challenge because they can reduce the measurement accuracy of the aortic annulus. Therefore, we applied the recently developed second-generation whole-heart motion correction algorithm (SnapShot Freeze 2.0, SSF2) to pre-TAVI cardiac CT and investigated its clinical utility by stratified analysis of the patient's heart rate during scanning. We found that SSF2 reconstruction significantly reduced aortic annulus motion artifacts and improved the image quality and measurement accuracy compared to standard reconstruction, especially in patients with high heart rate or a 40% R-R interval (systolic phase). SSF2 may contribute to improving the measurement accuracy of the aortic annulus.

Improvement of Spatial Resolution on Coronary CT Angiography by Using Super-Resolution Deep Learning Reconstruction.

RATIONALE AND OBJECTIVES: Our objective was to compare the image quality of coronary CT angiography reconstructed with super-resolution deep learning reconstruction (SR-DLR) and with hybrid iterative reconstruction (IR) images. MATERIALS AND METHODS: This retrospective study included 100 patients who underwent coronary CT angiography using a 320-detector-row CT scanner. The CT images were reconstructed with hybrid IR and SR-DLR. The standard deviation of the CT number was recorded and the CT attenuation profile across the left main coronary artery was generated to calculate the contrast-to-noise ratio (CNR) and measure the edge rise slope (ERS). Overall image quality was evaluated and plaque detectability was assessed on a 4-point scale (1 = poor, 4 = excellent). For reference, invasive coronary angiography of 14 patients was used. RESULTS: The mean image noise on SR-DLR was significantly lower than on hybrid IR images (15.6 vs 22.9 HU; p < 0.01). The mean CNR was significantly higher and the ERS was steeper on SR-DLR- compared to hybrid IR images (CNR: 32.4 vs 20.4, p < 0.01; ERS: 300.0 vs 198.2 HU/mm, p < 0.01). The image quality score was better on SR-DLR- than on hybrid IR images (3.6 vs 3.1; p < 0.01). SR-DLR increased the detectability of plaques with < 50% stenosis (p < 0.01). CONCLUSION: SR-DLR was superior to hybrid IR with respect to the image noise, the sharpness of coronary artery margins, and plaque detectability.

A longitudinal pilot study to assess temporal changes in coronary arterial 18F-sodium fluoride uptake.

PURPOSE: How coronary arterial 18F-sodium fluoride (18F-NaF) uptake on positron emission tomography changes over the long term and what clinical factors impact the changes remain unclear. We sought to investigate the topics in this study. METHODS: We retrospectively studied 15 patients with ≥1 coronary atherosclerotic lesion/s detected on cardiac computed tomography who underwent baseline and follow-up (interval of >3 years) 18F-NaF positron emission tomography/computed tomography scans. Focal 18F-NaF uptake in each lesion was quantified using maximum tissue-to-background ratio (TBRmax). The temporal change in TBRmax was assessed using a ratio of follow-up to baseline TBRmax (R-TBRmax). RESULTS: A total of 51 lesions were analyzed. Mean R-TBRmax was 0.96 ± 0.21. CT-based lesion features (location, obstructive stenosis, plaque types, features of high-risk plaque) did not correlate with an increase in R-TBRmax. In multivariate analysis, baseline TBRmax significantly correlated with higher follow-up TBRmax (ß = 0.57, P < 0.0001), and the presence of diabetes mellitus significantly correlated with both higher follow-up TBRmax (ß = 0.34, P = 0.001) and elevated R-TBRmax (ß = 0.40, P = 0.003). CONCLUSION: Higher coronary arterial 18F-NaF uptake is likely to remain continuously high. Diabetes mellitus affects the long-term increase in coronary arterial 18F-NaF uptake.

Understanding CT imaging findings based on the underlying pathophysiology in patients with small bowel ischemia.

Because acute small bowel ischemia has a high mortality rate, it requires rapid intervention to avoid unfavorable outcomes. Computed tomography (CT) examination is important for the diagnosis of bowel ischemia. Acute small bowel ischemia can be the result of small bowel obstruction or mesenteric ischemia, including mesenteric arterial occlusion, mesenteric venous thrombosis, and non-occlusive mesenteric ischemia. The clinical significance of each CT finding is unique and depends on the underlying pathophysiology. This review describes the definition and mechanism(s) of bowel ischemia, reviews CT findings suggesting bowel ischemia, details factors involved in the development of small bowel ischemia, and presents CT findings with respect to the different factors based on the underlying pathophysiology. Such knowledge is needed for accurate treatment decisions.

Native Myocardial T1 Value in Predicting 1-Year Outcomes in Patients with Nonischemic Dilated Cardiomyopathy Experiencing Recent Heart Failure.

The evidence for the clinical implications, especially the short-term utility, of native myocardial T1 value (T1native) on cardiac magnetic resonance (CMR) in nonischemic dilated cardiomyopathy (NIDCM) is scant. We investigated the potential of T1native to assess left ventricular (LV) myocardial characteristics and predict 1-year outcomes in patient with NIDCM experiencing recent heart failure (HF).Forty-five patients with NIDCM and HF symptoms within 3 months underwent CMR with cine, non-contrast T1 mapping, and late gadolinium enhancement (LGE). T1native per patient was defined as an averaged T1 value of 5 short-axis slices of base-to-apex LV myocardium. The appearance of LGE was visually examined. T1native correlated with the LV end-diastolic dimension normalized to height (LVEDD) (r = 0.38, P = 0.0103), ejection fraction (r = -0.39, P = 0.009), and serum N-terminal pro-brain natriuretic peptide levels (r = 0.48, P = 0.001), whereas the presence and segmental extent of LGE correlated only with LVEDD. In the 1-year follow-up cohort, the optimal cutoffs of T1native for predicting LV reverse remodeling (LVRR) and combined cardiac events (cardiac death, ventricular tachycardia/fibrillation, heart failure hospitalization) were 1366 ms and 1377 ms, respectively. In multivariate analysis, T1native < 1366 ms and T1native > 1377 ms remained significant predictors of LVRR (odds ratio, 11.3) and cardiac events (hazard ratio, 15.3), respectively, whereas the presence and segmental extent of LGE did not.T1native in patients with NIDCM experiencing recent HF may offer a promising strategy for assessing LV myocardial characteristics and predicting 1-year LVRR and cardiac events.

Triaging of COVID-19 patients using low dose chest CT: Incidence and factor analysis of lung involvement on CT images.

INTRODUCTION: Despite an increase in CT studies to evaluate patients with coronavirus disease 2019 (COVID-19), their indication in triage is not well-established. The purpose was to investigate the incidence of lung involvement and analyzed factors related to lung involvement on CT images for establishment of the indication for CT scans in the triaging of COVID-19 patients. METHODS: Included were 192 COVID-19 patients who had undergone CT scans and blood tests for triaging. Two radiologists reviewed the CT images and recorded the incidence of lung involvement. The prediction model for lung involvement on CT images using clinico-laboratory variables [age, gender, body mass index, oxygen saturation of the peripheral artery (SpO2), comorbidities, symptoms, and blood data] were developed by multivariate logistic regression with cross-validation. RESULTS: In 120 of the 192 patients (62.5%), CT revealed lung involvement. The patient age (odds ratio [OR]; 4.95, 95% confidence interval [CI]; 0.93-26.49), albumin (OR; 4.66, 95%CI; 1.37-15.84), lactate dehydrogenase (OR; 5.79, 95%CI; 1.43-23.38) and C-reactive protein (OR; 8.93, 95%CI; 4.13-19.29) were selected for the final prediction model for lung involvement on CT images. The cross-validated area under the receiver operating characteristics (ROC) curve was 0.83. CONCLUSIONS: The high incidence of lung involvement (62.5%) was confirmed on CT images. The proposed prediction model that includes the patient age, albumin, lactate dehydrogenase, and C-reactive protein may be useful for predicting lung involvement on CT images and may assist in deciding whether triaged COVID-19 patients should undergo CT.

Dual-energy CT: minimal essentials for radiologists.

Dual-energy CT, the object is scanned at two different energies, makes it possible to identify the characteristics of materials that cannot be evaluated on conventional single-energy CT images. This imaging method can be used to perform material decomposition based on differences in the material-attenuation coefficients at different energies. Dual-energy analyses can be classified as image data-based- and raw data-based analysis. The beam-hardening effect is lower with raw data-based analysis, resulting in more accurate dual-energy analysis. On virtual monochromatic images, the iodine contrast increases as the energy level decreases; this improves visualization of contrast-enhanced lesions. Also, the application of material decomposition, such as iodine- and edema images, increases the detectability of lesions due to diseases encountered in daily clinical practice. In this review, the minimal essentials of dual-energy CT scanning are presented and its usefulness in daily clinical practice is discussed.