Imaging study of pseudo-CT images of superposed ultrasound deformation fields acquired in radiotherapy based on step-by-step local registration

AbstractThe purpose of this study is to create a new pseudo-computed tomography (CT) imaging approach under superposed ultrasound (US) deformation fields based on step-by-step local registration. Scanned CT and US 3D image datasets of three patients with postoperative cervical carcinoma were selected, including CT (CTsim) and US images (USsim) acquired during simulated positioning process and cone beam CT (CBCT) and US images for positioning verification (USpv) acquired after treatment for 10 times. Regions of interest such as urinary bladders were segmented out and accepted local registration to obtain different deformation fields. These deformation fields were successively performed according to their order and then applied to localized CT images to obtain pseudo-CT (CTps). After filtering, we obtained the final correct pseudo-CT (CTpsf). The pseudo-CT based on the mask of the whole imaging region of US images (WCTps) were acquired as control. Then, we compared CTpsf, CTps, WCTps, and CBCT in terms of their similarity in anatomical structure and differences in pseudo-CT and CTsim in terms of dosimetry. Structural similarity degree between CTpsf and CBCT was larger compared with that between CTps and WCTps. Target regions and dosages of endangered organs between CTpsf and CTsim were different under the same calculation conditions based on the Monte Carlo algorithm. Compared with the VMAT plan of CTsim, the pass rate of CTpsf in γ analysis under the standards of 2% dosage difference and 2-mm distance difference was 91.8%. The imaging quality of CTpsf was better compared with WCTps and CTps. It exhibited high similarity with CBCT in anatomical structure and had favorable application prospect in adaptive radiotherapy. Graphical abstractThe local deformation registration is performed between the ultrasound images based on different regions of interest, and then stepwise applied to localized CT images to obtain pseudo-CT. After filtering, the corrected pseudo CT image is obtained.

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