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Low-KeV Virtual Monoenergetic Dual-Energy CT with Deep Learning Reconstruction for Assessing Hepatocellular Carcinoma

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Abstract

Purpose

To evaluate the diagnostic performance of low-keV virtual monoenergetic imaging (VMI) using dual-energy CT (DECT) with deep learning image reconstruction (DLIR) in patients with hepatocellular carcinoma (HCC).

Methods

This retrospective study included patients with HCC undergoing DECT scans between February 2019 and March 2022. VMI was reconstructed with hybrid iterative reconstruction (HIR) at 70-keV (HIR70keV) and 40-keV (HIR40keV) and DLIR at 40-keV (DLIR40keV). Two radiologists calculated the contrast-to-noise ratio (CNR) of the HCC. The possible presence of HCC was assessed by two additional radiologists. CNR was compared using Friedman’s test. Diagnostic performance was compared between three groups using Cochran’s Q test and jackknife alternative free-response receiver operating characteristic analysis.

Results

Thirty-two patients (mean age 73.19 ± 11.86, 23 males) with 36 HCCs were enrolled. The CNR of DLIR40keV was significantly higher than HIR70keV and HIR40keV (p < 0.001 and 0.001). The sensitivities for the detection of HCC were HIR70keV, 63.9%; HIR40keV, 72.2%; DLIR40 keV, 83.3%, and HIR70keV, 52.8%; HIR40keV, 61.1%; DLIR40 keV, 77.8% for observers 1 and 2, respectively. DLIR40keV sensitivity was significantly higher than HIR70keV on both readers (p = 0.020 and 0.013). The figures of merit (FOM) were HIR70keV, 0.86; HIR40keV, 0.92; DLIR40 keV, 0.96, and HIR70keV, 0.84; HIR40keV, 0.90; and DLIR40 keV, 0.94 for observers 1 and 2, respectively. For both observers, DLIR40keV FOM was significantly higher than HIR70keV (p = 0.013 and 0.012).

Conclusion

DLIR40keV achieved the best CNR among the three groups. HCC detectability was significantly improved at DLIR40keV compared to HIR70keV.

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References

  1. Chernyak, V., Fowler, K. J., Kamaya, A., Kielar, A. Z., Elsayes, K. M., Bashir, M. R., Kono, Y., Do, R. K., Mitchell, D. G., Singal, A. G., Tang, A., & Sirlin, C. B. (2018). Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology, 289(3), 816–830. https://doi.org/10.1148/radiol.2018181494. Liver Imaging Reporting and Data System (LI-RADS).

  2. Kim, J. H., Joo, I., & Lee, J. M. (2019). Atypical Appearance of Hepatocellular Carcinoma and its mimickers: How to solve challenging cases using Gadoxetic Acid-enhanced liver magnetic resonance imaging. Korean Journal of Radiology, 20(7), 1019–1041. https://doi.org/10.3348/kjr.2018.0636.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Grazzini, G., Cozzi, D., Flammia, F., Grassi, R., Agostini, A., Belfiore, M. P., Borgheresi, A., Mazzei, M. A., Floridi, C., Carrafiello, G., Giovagnoni, A., Pradella, S., & Miele, V. (2020). Hepatic tumors: Pitfall in diagnostic imaging. Acta Bio-Medica : Atenei Parmensis, 91(8-s), 9–17. https://doi.org/10.23750/abm.v91i8-S.9969.

    Article  PubMed  Google Scholar 

  4. Candita, G., Rossi, S., Cwiklinska, K., Fanni, S. C., Cioni, D., Lencioni, R., & Neri, E. (2023). Imaging Diagnosis of Hepatocellular Carcinoma: A state-of-the-art review. Diagnostics (Basel), 13(4). https://doi.org/10.3390/diagnostics13040625.

  5. McCollough, C. H., Leng, S., Yu, L., & Fletcher, J. G. (2015). Dual- and multi-energy CT: Principles, Technical Approaches, and clinical applications. Radiology, 276(3), 637–653. https://doi.org/10.1148/radiol.2015142631.

    Article  PubMed  Google Scholar 

  6. Albrecht, M. H., Vogl, T. J., Martin, S. S., Nance, J. W., Duguay, T. M., Wichmann, J. L., De Cecco, C. N., Varga-Szemes, A., van Assen, M., Tesche, C., & Schoepf, U. J. (2019). Review of clinical applications for virtual Monoenergetic Dual-Energy CT. Radiology, 293(2), 260–271. https://doi.org/10.1148/radiol.2019182297.

    Article  PubMed  Google Scholar 

  7. Albrecht, M. H., Scholtz, J. E., Hüsers, K., Beeres, M., Bucher, A. M., Kaup, M., Martin, S. S., Fischer, S., Bodelle, B., Bauer, R. W., Lehnert, T., Vogl, T. J., & Wichmann, J. L. (2016). Advanced image-based virtual monoenergetic dual-energy CT angiography of the abdomen: Optimization of kiloelectron volt settings to improve image contrast. European Radiology, 26(6), 1863–1870. https://doi.org/10.1007/s00330-015-3970-2.

    Article  PubMed  Google Scholar 

  8. Yoo, J., Lee, J. M., Yoon, J. H., Joo, I., Lee, E. S., Jeon, S. K., & Jang, S. (2021). Comparison of low kVp CT and dual-energy CT for the evaluation of hypervascular hepatocellular carcinoma. Abdom Radiol (NY), 46(7), 3217–3226. https://doi.org/10.1007/s00261-020-02888-7.

    Article  PubMed  Google Scholar 

  9. Cecco, C. N. D., Caruso, D., Schoepf, U. J., Santis, D. D., Muscogiuri, G., Albrecht, M. H., Meinel, F. G., Wichmann, J. L., Burchett, P. F., Varga-Szemes, A., Sheafor, D. H., & Hardie, A. D. (2018). A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions. European Radiology, 28(8), 3393–3404. https://doi.org/10.1007/s00330-018-5313-6.

    Article  PubMed  Google Scholar 

  10. Große Hokamp, N., Höink, A. J., Doerner, J., Jordan, D. W., Pahn, G., Persigehl, T., Maintz, D., & Haneder, S. (2018). Assessment of arterially hyper-enhancing liver lesions using virtual monoenergetic images from spectral detector CT: Phantom and patient experience. Abdom Radiol (NY), 43(8), 2066–2074. https://doi.org/10.1007/s00261-017-1411-1.

    Article  PubMed  Google Scholar 

  11. Agrawal, M. D., Oliveira, G. R., Kalva, S. P., Pinho, D. F., Arellano, R. S., & Sahani, D. V. (2016). Prospective comparison of reduced-iodine-dose virtual monochromatic imaging dataset from dual-energy CT angiography. https://doi.org/10.2214/ajr.15.15814.

  12. Koetzier, L. R., Mastrodicasa, D., Szczykutowicz, T. P., van der Werf, N. R., Wang, A. S., Sandfort, V., van der Molen, A. J., Fleischmann, D., & Willemink, M. J. (2023). Deep Learning Image Reconstruction for CT: Technical principles and clinical prospects. Radiology, 306(3), e221257. https://doi.org/10.1148/radiol.221257.

    Article  PubMed  Google Scholar 

  13. Nakamoto, A., Kim, T., Hori, M., Onishi, H., Tsuboyama, T., Sakane, M., Tatsumi, M., & Tomiyama, N. (2015). Clinical evaluation of image quality and radiation dose reduction in upper abdominal computed tomography using model-based iterative reconstruction; comparison with filtered back projection and adaptive statistical iterative reconstruction. European Journal of Radiology, 84(9), 1715–1723. https://doi.org/10.1016/j.ejrad.2015.05.027.

    Article  PubMed  Google Scholar 

  14. Raza, A., & Sood, G. K. (2014). Hepatocellular carcinoma review: Current treatment, and evidence-based medicine. World Journal of Gastroenterology, 20(15), 4115–4127. https://doi.org/10.3748/wjg.v20.i15.4115.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bhosale, P., Le, O., Balachandran, A., Fox, P., Paulson, E., & Tamm, E. (2015). Quantitative and qualitative comparison of single-source dual-energy computed Tomography and 120-kVp computed Tomography for the Assessment of Pancreatic Ductal Adenocarcinoma. Journal of Computer Assisted Tomography, 39(6), 907–913. https://doi.org/10.1097/rct.0000000000000295.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Koo, T. K., & Li, M. Y. (2016). A Guideline of selecting and reporting Intraclass correlation coefficients for Reliability Research. Journal of Chiropractic Medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Alkhalaf, Z. S. A., Yakar, D., de Groot, J. C., Dierckx, R., & Kwee, T. C. (2021). Medical knowledge and clinical productivity: Independently correlated metrics during radiology residency. European Radiology, 31(7), 5344–5350. https://doi.org/10.1007/s00330-020-07646-3.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Sato, M., Ichikawa, Y., Domae, K., Yoshikawa, K., Kanii, Y., Yamazaki, A., Nagasawa, N., Nagata, M., Ishida, M., & Sakuma, H. (2022). Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen. European Radiology. https://doi.org/10.1007/s00330-022-08647-0.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Okimoto, N., Yasaka, K., Kaiume, M., Kanemaru, N., Suzuki, Y., & Abe, O. (2023). Improving detection performance of hepatocellular carcinoma and interobserver agreement for liver imaging reporting and data system on CT using deep learning reconstruction. Abdom Radiol (NY), 48(4), 1280–1289. https://doi.org/10.1007/s00261-023-03834-z.

    Article  PubMed  Google Scholar 

  20. Marin, D., Ramirez-Giraldo, J. C., Gupta, S., Fu, W., Stinnett, S. S., Mileto, A., Bellini, D., Patel, B., Samei, E., & Nelson, R. C. (2016). Effect of a noise-optimized second-generation monoenergetic algorithm on image noise and conspicuity of Hypervascular Liver tumors: An in Vitro and in vivo study. Ajr. American Journal of Roentgenology, 206(6), 1222–1232. https://doi.org/10.2214/ajr.15.15512.

    Article  PubMed  Google Scholar 

  21. Shuman, W. P., Green, D. E., Busey, J. M., Mitsumori, L. M., Choi, E., Koprowicz, K. M., & Kanal, K. M. (2014). Dual-energy liver CT: Effect of monochromatic imaging on lesion detection, conspicuity, and contrast-to-noise ratio of hypervascular lesions on late arterial phase. Ajr. American Journal of Roentgenology, 203(3), 601–606. https://doi.org/10.2214/ajr.13.11337.

    Article  PubMed  Google Scholar 

  22. Mileto, A., Nelson, R. C., Samei, E., Choudhury, K. R., Jaffe, T. A., Wilson, J. M., & Marin, D. (2014). Dual-energy MDCT in hypervascular liver tumors: Effect of body size on selection of the optimal monochromatic energy level. Ajr. American Journal of Roentgenology, 203(6), 1257–1264. https://doi.org/10.2214/ajr.13.12229.

    Article  PubMed  Google Scholar 

  23. Yamashita, Y., Komohara, Y., Takahashi, M., Uchida, M., Hayabuchi, N., Shimizu, T., & Narabayashi, I. (2000). Abdominal helical CT: Evaluation of optimal doses of intravenous contrast material–a prospective randomized study. Radiology, 216(3), 718–723. https://doi.org/10.1148/radiology.216.3.r00se26718.

    Article  CAS  PubMed  Google Scholar 

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Funding

We have not received any funding in this study.

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Authors and Affiliations

  1. Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, D1, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan

    Takashi Ota, Atsushi Nakamoto, Hiromitsu Onishi, Takahiro Tsuboyama, Shohei Matsumoto, Hideyuki Fukui, Koki Kaketaka, Toru Honda, Kengo Kiso, Mitsuaki Tatsumi & Noriyuki Tomiyama

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  1. Takashi Ota
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Takashi Ota, Hiromitsu Onishi, Shohei Matsumoto, Atsushi Nakamoto, and Koki Kaketaka. The role each played in this study is shown in the table below. The first draft of the manuscript was written by Takashi Ota and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Authors and their Roles:

Hiromitsu Onishi, Atsushi Nakamoto, Takashi Ota, Shohei Matsumoto, Koki Kaketaka: Qualitative image quality assessment; Hiromitsu Onishi, Shohei Matsumoto: Evaluation of HCC detection capability; Atsushi Nakamoto, Takashi Ota: Quantitative image analysis; Takashi Ota: Main author of the article; Atsushi Nakamoto: Correcting papers (most contributors).

Corresponding author

Correspondence to Takashi Ota.

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Ethical Approval

We obtained approval for this study from the Osaka University Hospital Institutional Review Board.

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Informed consent was waived in this study because it was a retrospective, non-interventional study.

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All data and images used in this study have been anonymized.

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We have no conflicts of interest in this study.

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Ota, T., Nakamoto, A., Onishi, H. et al. Low-KeV Virtual Monoenergetic Dual-Energy CT with Deep Learning Reconstruction for Assessing Hepatocellular Carcinoma. J. Med. Biol. Eng. (2024). https://doi.org/10.1007/s40846-024-00855-x

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