{"title":"利用深度学习重建的低 keV 虚拟单能双能量 CT 评估肝细胞癌","authors":"Takashi Ota, Atsushi Nakamoto, Hiromitsu Onishi, Takahiro Tsuboyama, Shohei Matsumoto, Hideyuki Fukui, Koki Kaketaka, Toru Honda, Kengo Kiso, Mitsuaki Tatsumi, Noriyuki Tomiyama","doi":"10.1007/s40846-024-00855-x","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>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).</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>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 (<i>p</i> < 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 (<i>p</i> = 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 (<i>p</i> = 0.013 and 0.012).</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>DLIR40keV achieved the best CNR among the three groups. HCC detectability was significantly improved at DLIR40keV compared to HIR70keV.</p>","PeriodicalId":50133,"journal":{"name":"Journal of Medical and Biological Engineering","volume":"283 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Low-KeV Virtual Monoenergetic Dual-Energy CT with Deep Learning Reconstruction for Assessing Hepatocellular Carcinoma\",\"authors\":\"Takashi Ota, Atsushi Nakamoto, Hiromitsu Onishi, Takahiro Tsuboyama, Shohei Matsumoto, Hideyuki Fukui, Koki Kaketaka, Toru Honda, Kengo Kiso, Mitsuaki Tatsumi, Noriyuki Tomiyama\",\"doi\":\"10.1007/s40846-024-00855-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Purpose</h3><p>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).</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>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 (<i>p</i> < 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 (<i>p</i> = 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 (<i>p</i> = 0.013 and 0.012).</p><h3 data-test=\\\"abstract-sub-heading\\\">Conclusion</h3><p>DLIR40keV achieved the best CNR among the three groups. HCC detectability was significantly improved at DLIR40keV compared to HIR70keV.</p>\",\"PeriodicalId\":50133,\"journal\":{\"name\":\"Journal of Medical and Biological Engineering\",\"volume\":\"283 1\",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-03-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical and Biological Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s40846-024-00855-x\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical and Biological Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40846-024-00855-x","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Low-KeV Virtual Monoenergetic Dual-Energy CT with Deep Learning Reconstruction for Assessing Hepatocellular Carcinoma
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.
期刊介绍:
The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.