Cone Beam Computed Tomography Image-Quality Improvement Using "One-Shot" Super-resolution.
Cone beam computed tomography (CBCT) images are convenient representations for obtaining information about patients' internal organs, but their lower image quality than those of treatment planning CT images constitutes an important shortcoming. Several proposed CBCT image-quality improvement methods...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2120 - 2134 |
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| Autores principales: | , , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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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=187278981&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278981 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278981 187278981 187278981 10.1007/s10278-024-01346-w 187278981 ppf: 2120 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Cone Beam Computed Tomography Image-Quality Improvement Using "One-Shot" Super-resolution. aug: au: Tsuji, Takumasa Yoshida, Soichiro Hommyo, Mitsuki Oyama, Asuka Kumagai, Shinobu Shiraishi, Kenshiro Kotoku, Jun'ichi affil: https://ror.org/01gaw2478 Graduate School of Medical Care and Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, 173-8605, Tokyo, Japan sug: subj: Tomography, X-Ray Computed Methods Image Processing, Computer Assisted Methods Quality Improvement Human Funding Source Japan Prostatic Neoplasms Radiography Prostatic Neoplasms Radiotherapy Radiotherapy, Computer-Assisted Sensitivity and Specificity Pelvis Radiography Descriptive Statistics Cancer Patients Quantitative Studies Deep Learning Data Analysis, Statistical Analysis of Variance Paired T-Tests ab: Cone beam computed tomography (CBCT) images are convenient representations for obtaining information about patients' internal organs, but their lower image quality than those of treatment planning CT images constitutes an important shortcoming. Several proposed CBCT image-quality improvement methods based on deep learning require large amounts of training data. Our newly developed model using a super-resolution method, "one-shot" super-resolution (OSSR) based on the "zero-shot" super-resolution method, requires only small amounts of training data to improve CBCT image quality using only the target CBCT image and the paired treatment planning CT image. For this study, pelvic CBCT images and treatment planning CT images of 30 prostate cancer patients were used. We calculated the root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) to evaluate image-quality improvement and normalized mutual information (NMI) as a quantitative evaluation of positional accuracy. Our proposed method can improve CBCT image quality without requiring large amounts of training data. After applying our proposed method, the resulting RMSE, PSNR, SSIM, and NMI between the CBCT images and the treatment planning CT images were as much as 0.86, 1.05, 1.03, and 1.31 times better than those obtained without using our proposed method. By comparison, CycleGAN exhibited values of 0.91, 1.03, 1.02, and 1.16. The proposed method achieved performance equivalent to that of CycleGAN, which requires images from approximately 30 patients for training. Findings demonstrated improvement of CBCT image quality using only the target CBCT images and the paired treatment planning CT images. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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