The evolution and current situation in the application of dual-energy computed tomography: a bibliometric study

Introduction

Dual-energy computed tomography (DECT) has been extensively explored since it was first proposed in 1973 (1); however, DECT was not widely expanded in clinic until the introduction of the first-generation dual-source CT system in 2006 (2). Compared to conventional CT, DECT requires obtaining two datasets at high and low X-ray energies simultaneously through multiple imaging acquisition techniques, including dual-source CT, fast-switching X-ray tubes, and dual-layer detectors (3). It allows for different algorithms and postprocessing analyses, including virtual monoenergetic imaging (VMI), effective atomic number (Zeff), electron density mapping, energy spectrum curve, material decomposition [including virtual noncontrast (VNC) imaging, iodine mapping, virtual noncalcium (VNCa) mapping, and lung ventilation maps], and material differentiation or labeling (including urinary stone component analysis, gout imaging, bone removal, pulmonary vascular analysis, fat and iron quantification, and material prominence) based on two datasets (4).

Currently, VMI and iodine maps are primarily used to optimize image quality (5). Low-energy VMI has high soft tissue contrast and enhances the conspicuity of tumor and vascular lesions, while high-energy VMI can reduce metal artifacts. The electrode density and Zeff obtained with DECT are helpful for dose calculation in proton therapy and for distinguishing tissue components such as renal stones and tumor types. Among a growing list of diagnostic applications, lung ventilation mapping, gout imaging, and VNCa mapping have been increasingly used for pulmonary embolism, gout visualization, and bone marrow edema (6-8). In addition, the multimaterial differentiation or labeling method can be used for the quantitative analysis of specific substances, such as fat and iron (9).

A wide variety postprocessing algorithms have promoted the wider application of DECT. However, despite the substantial effort and many related studies being published in this area, systematic collation and scientific analysis of DECT literature are lacking. Bibliometrics is a reliable method that is capable of evaluating trends in research activity over time based on literature database analysis (10). It provides a scientific statistical basis for understanding the developing trends in a specific field and can rank academic groups and individual researchers according to different data. This study thus aimed to characterize the applications in various systems and identify the trends in DECT research by analyzing articles published over the past few decades.

Methods

Database

The bibliometric analysis data in this study were derived from the Web of Science Core Collection (WoSCC) database, which contains a comprehensive collection of literature. As one of the largest bibliometric databases in the world, the WoSCC database is the most commonly used database for bibliometric analysis (11).

Inclusion criteria and search strategy

On January 22, 2023, we retrieved all literature related to DECT from the WoSCC published prior to August 31, 2022. The search strategy was as follows: TS = (“dual-energy CT” OR “dual-source CT” OR “spectral CT” OR “dual-layer detector spectrum CT” OR “split-filter dual-energy CT” OR “single-source sequence acquisition dual-energy CT” OR “DECT” OR “dual-energy computer tomography”) AND date of publication (DOP) = (January 1, 1982, to August 31, 2022). Original articles and reviews of DECT studies were included, while conference papers and papers related to engineering were excluded. The language used was limited to English. Ultimately, 4,720 studies were retrieved. Records for all data, including title, authors, nationalities, affiliations, journal name, keywords, abstracts, and references, were downloaded in plain-text format.

Data visualization

This study analyzed the number of articles published annually, countries and institutions, highly cited and co-cited articles, authors, journals, and keywords. Excel 2019 (Microsoft Corp., Redmond, WA, USA) was used to display the trends in the number of articles published by year. CiteSpace and VOSviewer were used for data visualization, both of which have been widely used in bibliometric analyses. Visualization analysis of the co-occurrence of institutions and countries, authors, references, cocitations, and keywords was completed using CiteSpace. In the visual map, different countries, institutions, and authors were simultaneously depicted in their respective networks. In these networks, the size of nodes represents the number of publications or cocitations, and the thickness of the links represents the strength of the association or frequency of the cooperation between countries or institutions, commonly known as total link strength (TLS). Betweenness centrality (BC) was used as an indicator to measure the likelihood of a given node being located in the center of the network to assess the importance of the node. Generally, nodes with a BC ≥0.1 occupy a pivotal position and are usually indicated by purple rings (12). In the cocitation network, modularity value (Q) is an index used to assess the extent of the modularity of networks. A Q>0.3 indicates that a cluster structure is significant. Another similar evaluation indicator for the quality of networks, mean silhouette value (S), is often used to assess the homogeneity of networks. S>0.7 indicates that the clustering result is reliable and satisfactory (13). The R-bibliometrix software package in R-Studio was used to analyze the number of publications per journal. The h-index was used to evaluate the quantity and level of academic output of researchers and journals. VOSviewer was used to analyze the number of articles published by each country and the keyword co-occurrence. A high output rate of studies indicates that the influence of a country in the field is large. Additionally, in an overlay visualization map of keywords, the size of nodes represents the number of keyword occurrences, and future research hot spots can be reflected by keyword occurrence frequency (14).

Synonym substitution of keywords

Any synonyms, such as “dual-energy CT” and “dual-energy computed tomography”, “dual-source CT” and “dual source CT”, and “CT angiography” and “angiography”, that could possibly lead to a bias in the results were substituted using VOSviewer before data analysis.

Results

Publication year

The first paper on DECT was published in 1982. However, annual publications did not increase, even gradually, until after 2006. The number of published articles has exhibited a general upward trend in recent years, reaching 504 by 2021 (Figure 1).

Figure 1 Trend of annual publications in the application of DECT. DECT, dual-energy computed tomography.

Analysis of the most productive countries and institutions

A total of 64 countries have published articles on DECT. The top 10 countries with the most publications are listed in Table 1. The highest contributing countries in terms of published articles were the USA (n=1,662), Germany (n=1,171), and China (n=867), with the USA also being the country with the most citations (n=51,565). The co-occurrence map of countries (Figure 2A) and collaboration map among countries (Figure 2B) display the co-occurrence links and cooperative relationships between countries, respectively. In Figure 2A, the “USA” node has a distinct purple ring, indicating its significant impact. The USA has collaborated with many countries internationally, with Germany and China being the most common partners. More than 3,000 institutions have published articles on DECT. The top 10 most productive institutions are listed in Table 1, with the top 3 being the Mayo Clinic (178 articles), Massachusetts General Hospital (158 articles), and Harvard Medical School (133 articles). Massachusetts General Hospital (BC =0.11) occupied a key position in the co-occurrence networks, as illustrated in Figure 2C.

Figure 2 The visualization map of countries and institutions. (A) Map of country co-occurrence. (B) Visualization of the countries or regions. The thickness of the line represents the frequency of collaboration. (C) Co-occurrence of institutions.

Analysis of the top cited references and cocited references

A high citation analysis can help us understand the influence of articles, authors, and institutions in relevant fields (15). The most cited article, found according to the total citations among the publications, was “Material differentiation by dual energy CT: initial experience”, published by Johnson TRC et al. The co-citation analysis of references implies that 2 references or authors are cited together in other articles. Analysis of cocitations may contribute to understanding influential references or authors in the certain field and the association between them. Table 2 displays the 15 most co-cited references; the paper “Dual- and multi-energy CT: principles, technical approaches, and clinical applications”, published by McCollough CH et al., ranked first with 281 citations, followed by the papers by Johnson TRC et al. and Flohr TG et al.

Analysis of authors and co-cited authors

A total of 16,838 authors were included in this study. The top 10 most highly productive authors and the top 10 most co-cited authors are listed in Table 3. The most productive author was Schoepf UJ, who published 155 articles. This was followed by Vogl TJ and Alkadhi H, with 113 and 100 articles, respectively. Johnson TRC was the author with the highest number of cocitations and was cited 1,104 times in total. However, the BC value of Johnson TRC was not the highest.

Analysis of published journals

The top 15 journals in terms of publication volume are listed in Table 4. European Radiology (n=411) had the most number of articles, followed by the European Journal of Radiology (n=334) and the American Journal of Roentgenology (n=240), which accounted for 15.85%, 12.88%, and 9.25% of the total number of publications in the top 15 journals, respectively. Meanwhile, the top 5 journals based on h-index were Radiology (h-index =58), European Radiology (h-index =56), American Journal of Roentgenology (h-index =53), Investigative Radiology (h-index =49), and Medical Physics (h-index =47).

Analysis of keywords

A total of 10,173 keywords, including author keywords and keyword plus, were included in this study. We set the keyword frequency to no less than 25 times using VOSviewer, and 323 keywords were identified. After merging the keyword synonyms, the top 10 keywords in terms of frequency were “CT” (2,296 times), “dual-energy CT” (2,293 times), “CT angiography” (1,065 times), “dual-source CT” (803 times), “image quality” (642 times), “multidetector CT” (470 times), “diagnosis” (373 times), “accuracy” (371 times), “spectral CT” (334 times), and “diagnostic accuracy” (291 times). “Spectral CT”, “iodine”, “head”, and “cancer” appeared frequently in recent years as can be seen from the overlay visualization map of keywords in Figure 3.

Figure 3 Overlay visualization map of keywords on VOSviewer. The size of the node represents the occurrence number of keywords. The color represents the average time of occurrence, and a color closer to yellow indicates a time closer to the present.

Citation burst refers to the rapid increase in the value of a variable (such as a keyword) in a short period of time. It indicates that a variable has become a hot spot during a given period and has been intensely examined and studied by the academic community (16). The top 25 keywords with the strongest citation bursts are listed in Figure 4. “Dual-source CT”, “spiral computed tomography”, “coronary angiography”, “initial experience”, and “heart rate” were the top 5 most highly cited keywords. And the citation bursts of the above keywords mainly occurred before 2013. Figure 4 shows the keywords “X-ray computed”, “iodine quantification”, “principal”, “head”, “deep learning”, and “bone marrow edema” have been frequently cited since 2018.

Figure 4 Top 25 keywords with the strongest citation bursts. A green line represents the time period from 1982 to 2022. A light green line indicates that a keyword has not yet appeared. A dark green line represents the period during which a keyword was studied. A red line represents the time span of a burst. CT, computed tomography.

A timeline map of reference cocitation is depicted in Figure 5, which displays 12 clusters (Q=0.85, S=0.93). As seen in Figure 5, the clusters arranged from top to bottom is “radiomics”, “dual-energy CT”, “cholangiography”, “virtual monoenergetic imaging”, “radiation dose”, “myocardial perfusion”, “gout”, “pulmonary embolism”, “proton therapy”, “metal artifact reduction”, “material decomposition”, and “bone marrow edema”, respectively. The earliest cluster was focalized around radiation dose. Clusters focused around radiomics, gout, proton therapy, material decomposition, and bone marrow edema have persisted until the present period.

Figure 5 Timeline of reference co-citation on the application of DECT. DECT, dual-energy computed tomography. CT, computed tomography.

Discussion

The number of publications on DECT has grown rapidly since 2006, indicating that DECT application remains an intense area of research. Compared with conventional reviews, bibliometric methods can help scholars grasp the developmental characteristics of a given field and guide future research by analyzing the characteristics of publications, including year of publication, authors, journals, countries, and keywords (17). Our study analyzed 4,720 reviews and articles on DECT using visualization software, including VOSviewer and CiteSpace, to summarize the status of research and hot spots.

The country collaboration analysis demonstrated that the USA published the most literature and was home to 6 of the top 10 institutions in terms of publication volume. Moreover, the USA occupied a critical position in the collaboration network, indicating that it has assumed a leading position in DECT. Germany ranked as the country with the second most publications, hosting 3 of the top 10 institutions. Although China ranked third in the number of publications, it has published far fewer articles and has much fewer citations than do the 2 countries ranked above it. China also has no top 10 institutions, suggesting a large gap between developing and developed countries. Visual analysis of the authors indicated that the author with the most publications did not also have the most citations. Among the top 10 most productive authors, only McCollough CH and Achenbach S appeared in the top 10 authors with the most citations, suggesting that prolific authors should consider not only the number of articles but also their quality. Among the top 15 most co-cited papers, 5 were published in European Radiology and 4 in Radiology. Meanwhile, the top 2 journals ranked by h-index were Radiology (h-index =58) and European Radiology (h-index =56). European Radiology received up to 14,984 citations, a far greater number than that of other journals, reflecting its high influence and academic status in the DECT field (14). A total of 2,593 articles were published in the top 15 productive journals, accounting for approximately 55% of the 4,720 articles. Predictably, high-quality articles with major breakthroughs are published in these journals. Cocitation analysis demonstrated that the most co-cited article, which mainly discussed the principles, acquisition techniques, and clinical applications of DECT, was published by McCollough in Radiology in 2015 (18).

Analysis of keywords indicated that the top 10 keywords with the highest frequency from high to low were “CT”, “dual-energy CT”, “CT angiography”, “dual-source CT”, “image quality”, “multidetector CT”, “diagnosis”, “accuracy”, “spectral CT”, and “diagnostic accuracy”, respectively. Dual-source and spectral CT are imaging acquisition approaches that are widely used in clinical practice (19). The dual-source CT system is equipped with two X-ray tubes and two detectors that are positioned orthogonal to each other for dual-energy scanning (20). Spectral CT includes a two-layer detector and fast-switching X-ray tubes. This method has been frequently examined in scientific research in recent years. Regardless of which approach is used, the purpose of DECT is to increase diagnostic accuracy by improving image quality or obtaining more information compared with conventional CT. For example, image quality can be improved by reducing metal artifacts with high-kiloelectron volt VMI and increasing soft tissue contrast with low-kiloelectron volt VMI (5). DECT angiography has attracted increased attention and has been widely used in diagnosing vascular diseases. DECT can be used with iodine mapping or virtual VMI to reduce the dose of contrast agent, with VMI to remove metal hardening artifacts, and with VNCa technology to remove bone or calcification in the image to disclose the lesion might be covered (21).

Timeline view and keyword burst analyses can effectively characterize the current research hot spots and emerging trends within a given timeframe. In our study, these analyses demonstrated that research on DECT can be categorized into two periods. During the first period prior to 2015, research efforts were primarily focused on disease diagnosis, with a particular emphasis on cardiovascular diseases. Atherosclerotic plaques are the predominant type of coronary artery disease. The plaque can be classified into two distinct categories of calcified and noncalcified plaques, the latter of which encompasses lipid and fibrous plaques, according to composition (22). In comparison to fibrous plaque, the lipid plaque has lower stability, and thus the accurate differentiation of these types is necessary for patient treatment. However, the conventional approach of use multidetector CT (MDCT) has difficulty in accurately discerning lipid plaque due the substantial overlap of Hounsfield values between these types (23). In contrast, DECT can accurately discriminate lipid and fibrous plaques through use of element decomposition based on quantitative parameters (e.g., Zeff), thereby facilitating individualized precision therapy (24,25).

Furthermore, DECT is also used for cardiac imaging evaluation, particularly in the assessment of infarcted myocardial tissue. Compared to myocardial perfusion imaging and single-energy CT, DECT has the ability to provide more information, such as that concerning myocardial blood supply, anatomy and function of the coronary artery, and the extent of myocardial fibrosis, without an elevated radiation dose (26-28). Of note, during the latter years of the first period, a considerable number of researchers began to pay attention to the use of DECT for imaging renal masses. This could be attributed to its imaging reconstruction benefits derived from iodine subtraction, which has enabled the acquisition of both the VNC and the enhancement of images in a single scan with a lower radiation dose (29,30). The appearance of the “renal masses” burst may represent a transition in DECT-related research focus from cardiovascular diseases to other fields.

During the second period from 2015 to 2022, the scope of research on DECT was enlarged beyond the confines of cardiovascular diseases. This period witnessed the successive emergence of keyword bursts including “iodine quantification”, “deep learning”, and “bone marrow edema”. Iodine quantification is a typical application of material decomposition techniques, enabling the quantification of lesion enhancement degree and the illustration of the iodine distribution in blood vessels or tissues. Due to this advantage, DECT has been applied broadly in the treatment of various diseases, including both nonneoplastic and neoplastic types (31-34). Deep learning is a branch of artificial intelligence which has propelled the movement of the studies on DECT. In terms of image reconstruction, some studies have found that low-kiloelectron volt VMI reconstruction via deep learning can improve image quality in the evaluation of hypoenhanced hepatic metastasis and other liver diseases (35-37). Furthermore, deep learning can automatically segment brain tumors and surrounding healthy tissues to assist in the diagnosis of multiple myeloma (38,39). It can even predict head and neck lymph node metastasis of papillary thyroid carcinoma on DECT images (40). Furthermore, bone marrow edema is also detectable via VNCa imaging, with DECT being able to discern bone marrow edema caused by osteonecrosis, inflammatory arthritis, multiple myeloma, and bone metastases (41-43).

Limitations

Our study has some limitations. First, all documents were retrieved and downloaded from WoSCC. Although WoSCC is the most frequently used database, the literature included in this study may not represent all articles in the DECT field. Second, all papers analyzed were restricted to original articles and reviews, and conference papers were not considered. In addition, the language was limited to English. Finally, factors such as self-citation, which may have a subtle impact on bibliometrics, were not specifically considered in this study.

Conclusions

DECT has been widely used in research and clinical practice since the application of the first-generation dual-source CT system. The USA is the leading country in the field and has made a significant contribution. Prior studies in DECT were primarily concentrated on cardiovascular diseases, whereas contemporary hot spots of research have expanded to other fields, such as iodine quantification, deep learning, and bone marrow edema. With further research on postprocessing algorithms, DECT is expected to make an even greater contribution to the field of clinical imaging.

Acknowledgments

Funding: This study received funding from the Science and Technology Research Project of Education Department of Jiangxi Province (No. GJJ2200121).

Footnote

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-467/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All data were obtained through literature retrieval based on a canonical database. No medical institutions or patients were included, and thus ethical approval or informed consent was not applicable.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Hounsfield GN. Computerized transverse axial scanning (tomography). 1. Description of system. Br J Radiol 1973;46:1016-22.
2. Johnson TR, Krauss B, Sedlmair M, Grasruck M, Bruder H, Morhard D, Fink C, Weckbach S, Lenhard M, Schmidt B, Flohr T, Reiser MF, Becker CR. Material differentiation by dual energy CT: initial experience. Eur Radiol 2007;17:1510-7. [Crossref] [PubMed]
3. Hamid S, Nasir MU, So A, Andrews G, Nicolaou S, Qamar SR. Clinical Applications of Dual-Energy CT. Korean J Radiol 2021;22:970-82. [Crossref] [PubMed]
4. Mansouri M, Aran S, Singh A, Kambadakone AR, Sahani DV, Lev MH, Abujudeh HH. Dual-Energy Computed Tomography Characterization of Urinary Calculi: Basic Principles, Applications and Concerns. Curr Probl Diagn Radiol 2015;44:496-500. [Crossref] [PubMed]
5. Zeng Y, Geng D, Zhang J. Noise-optimized virtual monoenergetic imaging technology of the third-generation dual-source computed tomography and its clinical applications. Quant Imaging Med Surg 2021;11:4627-43. [Crossref] [PubMed]
6. Booz C, Nöske J, Lenga L, Martin SS, Yel I, Eichler K, Gruber-Rouh T, Huizinga N, Albrecht MH, Vogl TJ, Wichmann JL. Color-coded virtual non-calcium dual-energy CT for the depiction of bone marrow edema in patients with acute knee trauma: a multireader diagnostic accuracy study. Eur Radiol 2020;30:141-50. [Crossref] [PubMed]
7. Svensson E, Aurell Y, Jacobsson LTH, Landgren A, Sigurdardottir V, Dehlin M. Dual energy CT findings in gout with rapid kilovoltage-switching source with gemstone scintillator detector. BMC Rheumatol 2020;4:7. [Crossref] [PubMed]
8. Weidman EK, Plodkowski AJ, Halpenny DF, Hayes SA, Perez-Johnston R, Zheng J, Moskowitz C, Ginsberg MS, Dual-Energy CT. Angiography for Detection of Pulmonary Emboli: Incremental Benefit of Iodine Maps. Radiology 2018;289:546-53. [Crossref] [PubMed]
9. Peng Y, Ye J, Liu C, Jia H, Sun J, Ling J, Prince M, Li C, Luo X. Simultaneous hepatic iron and fat quantification with dual-energy CT in a rabbit model of coexisting iron and fat. Quant Imaging Med Surg 2021;11:2001-12. [Crossref] [PubMed]
10. Rodrigues Sousa E, Zoni E, Karkampouna S, La Manna F, Gray PC, De Menna M, Kruithof-de Julio M. A Multidisciplinary Review of the Roles of Cripto in the Scientific Literature Through a Bibliometric Analysis of its Biological Roles. Cancers (Basel) 2020;12:1480. [Crossref] [PubMed]
11. Yan S, Zhang H, Wang J. Trends and hot topics in radiology, nuclear medicine and medical imaging from 2011-2021: a bibliometric analysis of highly cited papers. Jpn J Radiol 2022;40:847-56. [Crossref] [PubMed]
12. Wu H, Wang Y, Tong L, Yan H, Sun Z. The Global Research Trends and Hotspots on Developmental Dysplasia of the Hip: A Bibliometric and Visualized Study. Front Surg 2021;8:671403. [Crossref] [PubMed]
13. Tagorti G, Kaya B. Publication trends of somatic mutation and recombination tests research: a bibliometric analysis (1984-2020). Genomics Inform 2022;20:e10. [Crossref] [PubMed]
14. Shen Z, Wu H, Chen Z, Hu J, Pan J, Kong J, Lin T. The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis. Front Oncol 2022;12:843735. [Crossref] [PubMed]
15. Valderrama-Zurián JC, Castelló-Cogollos L, Aleixandre-Benavent R. Trends in scientific research in Insights into Imaging: a bibliometric review. Insights Imaging 2019;10:79.
16. Xu P, Lv T, Dong S, Cui Z, Luo X, Jia B, Jeon CO, Zhang J. Association between intestinal microbiome and inflammatory bowel disease: Insights from bibliometric analysis. Comput Struct Biotechnol J 2022;20:1716-25. [Crossref] [PubMed]
17. Oo AM, Chu T TS. Bibliometric analysis of the top 100 cited articles in head and neck radiology. Acta Radiol Open 2021;10:20584601211001815. [Crossref] [PubMed]
18. McCollough CH, Leng S, Yu L, Fletcher JG. Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications. Radiology 2015;276:637-53. [Crossref] [PubMed]
19. Wu L, Cao G, Zhao L, Tang K, Lin J, Miao S, Lin T, Sun J, Zheng X, Spectral CT. Analysis of Solitary Pulmonary Nodules for Differentiating Malignancy from Benignancy: The Value of Iodine Concentration Spatial Distribution Difference. Biomed Res Int 2018;2018:4830659. [Crossref] [PubMed]
20. Petersilka M, Bruder H, Krauss B, Stierstorfer K, Flohr TG. Technical principles of dual source CT. Eur J Radiol 2008;68:362-8. [Crossref] [PubMed]
21. Machida H, Tanaka I, Fukui R, Shen Y, Ishikawa T, Tate E, Ueno E. Dual-Energy Spectral CT: Various Clinical Vascular Applications. Radiographics 2016;36:1215-32. [Crossref] [PubMed]
22. Zhou C, Chan HP, Chughtai A, Kuriakose J, Agarwal P, Kazerooni EA, Hadjiiski LM, Patel S, Wei J. Computerized analysis of coronary artery disease: performance evaluation of segmentation and tracking of coronary arteries in CT angiograms. Med Phys 2014;41:081912. [Crossref] [PubMed]
23. Stary HC, Chandler AB, Dinsmore RE, Fuster V, Glagov S, Insull W Jr, Rosenfeld ME, Schwartz CJ, Wagner WD, Wissler RW. A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association. Circulation 1995;92:1355-74. [Crossref] [PubMed]
24. Haghighi RR, Chatterjee S, Tabin M, Sharma S, Jagia P, Ray R, Singh RP, Yadav R, Sharma M, Krishna K, Vani VC, Lakshmi R, Mandal SR, Kumar P, Arava S. DECT evaluation of noncalcified coronary artery plaque. Med Phys 2015;42:5945-54. [Crossref] [PubMed]
25. Landry G, Seco J, Gaudreault M, Verhaegen F. Deriving effective atomic numbers from DECT based on a parameterization of the ratio of high and low linear attenuation coefficients. Phys Med Biol 2013;58:6851-66. [Crossref] [PubMed]
26. Arnoldi E, Lee YS, Ruzsics B, Weininger M, Spears JR, Rowley CP, Chiaramida SA, Costello P, Reiser MF, Schoepf UJ. CT detection of myocardial blood volume deficits: dual-energy CT compared with single-energy CT spectra. J Cardiovasc Comput Tomogr 2011;5:421-9. [Crossref] [PubMed]
27. Deseive S, Bauer RW, Lehmann R, Kettner M, Kaiser C, Korkusuz H, Tandi C, Theisen A, Schächinger V, Schoepf UJ, Vogl TJ, Kerl JM. Dual-energy computed tomography for the detection of late enhancement in reperfused chronic infarction: a comparison to magnetic resonance imaging and histopathology in a porcine model. Invest Radiol 2011;46:450-6. [Crossref] [PubMed]
28. Vliegenthart R, Pelgrim GJ, Ebersberger U, Rowe GW, Oudkerk M, Schoepf UJ. Dual-energy CT of the heart. AJR Am J Roentgenol 2012;199:S54-63. [Crossref] [PubMed]
29. Sheth S, Scatarige JC, Horton KM, Corl FM, Fishman EK. Current concepts in the diagnosis and management of renal cell carcinoma: role of multidetector ct and three-dimensional CT. Radiographics 2001;21:S237-54. [Crossref] [PubMed]
30. Toepker M, Moritz T, Krauss B, Weber M, Euller G, Mang T, Wolf F, Herold CJ, Ringl H. Virtual non-contrast in second-generation, dual-energy computed tomography: reliability of attenuation values. Eur J Radiol 2012;81:e398-405. [Crossref] [PubMed]
31. Dane B, O'Donnell T, Ream J, Chang S, Megibow A. Novel Dual-Energy Computed Tomography Enterography Iodine Density Maps Provide Unique Depiction of Crohn Disease Activity. J Comput Assist Tomogr 2020;44:772-9. [Crossref] [PubMed]
32. Deniffel D, Sauter A, Dangelmaier J, Fingerle A, Rummeny EJ, Pfeiffer D. Differentiating intrapulmonary metastases from different primary tumors via quantitative dual-energy CT based iodine concentration and conventional CT attenuation. Eur J Radiol 2019;111:6-13. [Crossref] [PubMed]
33. Martini K, Blüthgen C, Eberhard M, Schönenberger ALN, De Martini I, Huber FA, Barth BK, Euler A, Frauenfelder T. Impact of Vessel Suppressed-CT on Diagnostic Accuracy in Detection of Pulmonary Metastasis and Reading Time. Acad Radiol 2021;28:988-94. [Crossref] [PubMed]
34. Monti CB, Zanardo M, Cozzi A, Schiaffino S, Spagnolo P, Secchi F, De Cecco CN, Sardanelli F. Dual-energy CT performance in acute pulmonary embolism: a meta-analysis. Eur Radiol 2021;31:6248-58. [Crossref] [PubMed]
35. Lee T, Lee JM, Yoon JH, Joo I, Bae JS, Yoo J, Kim JH, Ahn C, Kim JH. Deep learning-based image reconstruction of 40-keV virtual monoenergetic images of dual-energy CT for the assessment of hypoenhancing hepatic metastasis. Eur Radiol 2022;32:6407-17. [Crossref] [PubMed]
36. Seo JY, Joo I, Yoon JH, Kang HJ, Kim S, Kim JH, Ahn C, Lee JM. 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. Eur J Radiol 2022;154:110390. [Crossref] [PubMed]
37. Xu JJ, Lönn L, Budtz-Jørgensen E, Hansen KL, Ulriksen PS. Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT. Eur Radiol 2022;32:7098-107. [Crossref] [PubMed]
38. Gong H, Baffour FI, Glazebrook KN, Rhodes NG, Tiegs-Heiden CA, Thorne JE, Cook JM, Kumar S, Fletcher JG, McCollough CH, Leng S. Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT. Med Phys 2022;49:6346-58. [Crossref] [PubMed]
39. van der Heyden B, Wohlfahrt P, Eekers DBP, Richter C, Terhaag K, Troost EGC, Verhaegen F. Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach. Sci Rep 2019;9:4126. [Crossref] [PubMed]
40. Jin D, Ni X, Zhang X, Yin H, Zhang H, Xu L, Wang R, Fan G. Multiphase Dual-Energy Spectral CT-Based Deep Learning Method for the Noninvasive Prediction of Head and Neck Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer. Front Oncol 2022;12:869895. [Crossref] [PubMed]
41. Chen M, Herregods N, Jaremko JL, Carron P, Elewaut D, Van den Bosch F, Jans L. Bone marrow edema in sacroiliitis: detection with dual-energy CT. Eur Radiol 2020;30:3393-400. [Crossref] [PubMed]
42. Kaup M, Wichmann JL, Scholtz JE, Beeres M, Kromen W, Albrecht MH, Lehnert T, Boettcher M, Vogl TJ, Bauer RW. Dual-Energy CT-based Display of Bone Marrow Edema in Osteoporotic Vertebral Compression Fractures: Impact on Diagnostic Accuracy of Radiologists with Varying Levels of Experience in Correlation to MR Imaging. Radiology 2016;280:510-9. [Crossref] [PubMed]
43. Son W, Park C, Jeong HS, Song YS, Lee IS. Bone marrow edema in non-traumatic hip: high accuracy of dual-energy CT with water-hydroxyapatite decomposition imaging. Eur Radiol 2020;30:2191-8. [Crossref] [PubMed]