Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model.
While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual mon...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2688 - 2698 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515414&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515414 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515414 181515414 181515414 10.1007/s10278-024-01111-z 181515414 ppf: 2688 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model. aug: au: Koike, Yuhei Ohira, Shingo Kihara, Sayaka Anetai, Yusuke Takegawa, Hideki Nakamura, Satoaki Miyazaki, Masayoshi Konishi, Koji Tanigawa, Noboru affil: https://ror.org/001xjdh50 Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, 573-1010, Hirakata, Osaka, Japan sug: subj: Tomography, X-Ray Computed Methods Deep Learning Convolutional Neural Networks Image Processing, Computer Assisted Technology, Radiologic Human Comparative Studies Record Review Retrospective Design Descriptive Statistics Data Analysis Software Head and Neck Neoplasms Radiotherapy Head and Neck Neoplasms Radiography Artificial Intelligence, Generative Radiographic Image Enhancement Quality Improvement Cancer Patients Inpatients Image Retrieval Picture Archiving and Communication Systems Radiographic Image Interpretation, Computer-Assisted Funding Source ab: While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50 keV (sVMI50keV) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI50keV. The image quality was evaluated, and the results were compared with those of the convolutional neural network-based model, Unet. The mean absolute errors from the true VMI50keV were 36.5 ± 4.9 and 33.0 ± 4.4 Hounsfield units for Unet and SwinUNETR, respectively. SwinUNETR yielded smaller errors in tissue attenuation values compared with those of Unet. The contrast changes in sVMI50keV generated by SwinUNETR from SECT were closer to those of DECT-derived VMI50keV than the contrast changes in Unet-generated sVMI50keV. This study demonstrated the potential of transformer-based models for generating synthetic low-energy VMIs from SECT images, thereby improving the image quality of head and neck cancer imaging. It provides a practical and feasible solution to obtain low-energy VMIs from SECT data that can benefit a large number of facilities and patients without access to DECT technology. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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