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Zhong H, Huang Q, Zheng X, Wang Y, Qian Y, Chen X, Wang J, Duan S. Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks. BMC Med Imaging 2024; 24:151. [PMID: 38890572 PMCID: PMC11184875 DOI: 10.1186/s12880-024-01331-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. PURPOSE To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. METHODS Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. RESULTS The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. CONCLUSION CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.
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Affiliation(s)
- Hua Zhong
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China.
| | - Qianwen Huang
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Xiaoli Zheng
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Yong Wang
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Yanan Qian
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Xingbiao Chen
- Clinical Science, Philips Healthcare, Shanghai, China
| | - Jinan Wang
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Shaoyin Duan
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
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Noda Y, Ando T, Kaga T, Yamda N, Seko T, Ishihara T, Kawai N, Miyoshi T, Ito A, Naruse T, Hyodo F, Kato H, Kambadakone AR, Matsuo M. Pancreatic cancer detection with dual-energy CT: diagnostic performance of 40 keV and 70 keV virtual monoenergetic images. LA RADIOLOGIA MEDICA 2024; 129:677-686. [PMID: 38512626 DOI: 10.1007/s11547-024-01806-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/14/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE To compare the diagnostic performance of 40 keV and 70 keV virtual monoenergetic images (VMIs) generated from dual-energy CT in the detection of pancreatic cancer. METHODS This retrospective study included patients who underwent pancreatic protocol dual-energy CT from January 2019 to August 2022. Four radiologists (1-11 years of experience), who were blinded to the final diagnosis, independently and randomly interpreted 40 keV and 70 keV VMIs and graded the presence or absence of pancreatic cancer. For each image set (40 keV and 70 keV VMIs), the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. The diagnostic performance of each image set was compared using generalized estimating equations. RESULTS Overall, 137 patients (median age, 71 years; interquartile range, 63-78 years; 77 men) were included. Among them, 62 patients (45%) had pathologically proven pancreatic cancer. The 40 keV VMIs had higher specificity (75% vs. 67%; P < .001), PPV (76% vs. 71%; P < .001), and accuracy (85% vs. 81%; P = .001) than the 70 keV VMIs. On the contrary, 40 keV VMIs had lower sensitivity (96% vs. 98%; P = .02) and NPV (96% vs. 98%; P = .004) than 70 keV VMIs. However, the diagnostic confidence in patients with (P < .001) and without (P = .001) pancreatic cancer was improved in 40 keV VMIs than in 70 keV VMIs. CONCLUSIONS The 40 keV VMIs showed better diagnostic performance in diagnosing pancreatic cancer than the 70 keV VMIs, along with higher reader confidence.
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Affiliation(s)
- Yoshifumi Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
| | - Tomohiro Ando
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Tetsuro Kaga
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Nao Yamda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Takuya Seko
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Takuma Ishihara
- Innovative and Clinical Research Promotion Center, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Nobuyuki Kawai
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Toshiharu Miyoshi
- Department of Radiology Services, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Akio Ito
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Takuya Naruse
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Fuminori Hyodo
- Center for One Medicine Innovative Translational Research (COMIT), Institute for Advanced Study, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
- Department of Pharmacology, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Hiroki Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Avinash R Kambadakone
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114, USA
| | - Masayuki Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
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Edmund J, Feen Rønjom M, van Overeem Felter M, Maare C, Margrete Juul Dam A, Tsaggari E, Wohlfahrt P. Split-filter dual energy computed tomography radiotherapy: From calibration to image guidance. Phys Imaging Radiat Oncol 2023; 28:100495. [PMID: 37876826 PMCID: PMC10590838 DOI: 10.1016/j.phro.2023.100495] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 10/26/2023] Open
Abstract
Background and purpose Dual-energy computed tomography (DECT) is an emerging technology in radiotherapy (RT). Here, we investigate split-filter DECT throughout the RT treatment chain as compared to single-energy CT (SECT). Materials and methods DECT scans were acquired with a tin-gold split-filter at 140 kV resulting in a low- and high-energy CT reconstruction (recon). Ten cancer patients (four head-and-neck (HN), three rectum, two anal/pelvis and one abdomen) were DECT scanned without and with iodine administered. A cylindrical and an anthropomorphic HN phantom were scanned with DECT and 120 kV SECT. The DECT images generated were: 120 kV SECT-equivalent (CTmix), virtual monoenergetic images (VMIs), iodine map, virtual non-contrast (VNC), effective atomic number (Zeff), and relative electron density (ρe,w). The clinical utility of these recons was investigated for calibration, delineation, dose calculation and image-guided RT (IGRT). Results A calibration curve for 75 keV VMI had a root-mean-square-error (RMSE) of 34 HU in closest agreement with the RSME of SECT calibration. This correlated with a phantom-based dosimetric agreement to SECT of γ1%1mm > 98%. A 40 keV VMI recon was most promising to improve tumor delineation accuracy with an average evaluation score of 1.6 corresponding to "partial improvement". The dosimetric impact of iodine was in general < 2%. For this setup, VNC vs. non-contrast CTmix based dose calculations are considered equivalent. SECT- and DECT-based IGRT was in agreement within the setup uncertainty. Conclusions DECT-based RT could be a feasible alternative to SECT providing additional recons to support the different steps of the RT workflow.
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Affiliation(s)
- Jens Edmund
- Radiotherapy Research Unit, Department of Oncology, Herlev & Gentofte Hospital, Herlev, Denmark
- Niels Bohr Institute, Copenhagen University, Denmark
| | - Marianne Feen Rønjom
- Radiotherapy Research Unit, Department of Oncology, Herlev & Gentofte Hospital, Herlev, Denmark
| | | | - Christian Maare
- Radiotherapy Research Unit, Department of Oncology, Herlev & Gentofte Hospital, Herlev, Denmark
| | | | - Eirini Tsaggari
- Radiotherapy Research Unit, Department of Oncology, Herlev & Gentofte Hospital, Herlev, Denmark
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Yue W, Yang W, peng H, Zhong Feng N, Hong Jie H. Comparative study of the image quality of twin beam dual energy and single energy carotid CT angiography. Eur J Radiol 2022; 148:110160. [DOI: 10.1016/j.ejrad.2022.110160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/18/2021] [Accepted: 01/12/2022] [Indexed: 12/14/2022]
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Chen X, Yang B, Li J, Zhu J, Ma X, Chen D, Hu Z, Men K, Dai J. A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma. Phys Med Biol 2021; 66. [PMID: 34700300 DOI: 10.1088/1361-6560/ac3345] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022]
Abstract
Objective:Megavoltage computed tomography (MV-CT) is used for setup verification and adaptive radiotherapy in tomotherapy. However, its low contrast and high noise lead to poor image quality. This study aimed to develop a deep-learning-based method to generate synthetic kilovoltage CT (skV-CT) and then evaluate its ability to improve image quality and tumor segmentation.Approach:The planning kV-CT and MV-CT images of 270 patients with nasopharyngeal carcinoma (NPC) treated on an Accuray TomoHD system were used. An improved cycle-consistent adversarial network which used residual blocks as its generator was adopted to learn the mapping between MV-CT and kV-CT and then generate skV-CT from MV-CT. A Catphan 700 phantom and 30 patients with NPC were used to evaluate image quality. The quantitative indices included contrast-to-noise ratio (CNR), uniformity and signal-to-noise ratio (SNR) for the phantom and the structural similarity index measure (SSIM), mean absolute error (MAE), and peak signal-to-noise ratio (PSNR) for patients. Next, we trained three models for segmentation of the clinical target volume (CTV): MV-CT, skV-CT, and MV-CT combined with skV-CT. The segmentation accuracy was compared with indices of the dice similarity coefficient (DSC) and mean distance agreement (MDA).Mainresults:Compared with MV-CT, skV-CT showed significant improvement in CNR (184.0%), image uniformity (34.7%), and SNR (199.0%) in the phantom study and improved SSIM (1.7%), MAE (24.7%), and PSNR (7.5%) in the patient study. For CTV segmentation with only MV-CT, only skV-CT, and MV-CT combined with skV-CT, the DSCs were 0.75 ± 0.04, 0.78 ± 0.04, and 0.79 ± 0.03, respectively, and the MDAs (in mm) were 3.69 ± 0.81, 3.14 ± 0.80, and 2.90 ± 0.62, respectively.Significance:The proposed method improved the image quality of MV-CT and thus tumor segmentation in helical tomotherapy. The method potentially can benefit adaptive radiotherapy.
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Affiliation(s)
- Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Jingwen Li
- Cloud Computing and Big Data Research Institute, China Academy of Information and Communications Technology, People's Republic of China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Xiangyu Ma
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Deqi Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Zhihui Hu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
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