Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy

• Verfasst von: Eckl, Miriam [VerfasserIn]
• Verfasst von: Hoppen, Lea [VerfasserIn]
• Verfasst von: Sarria, Gustavo R. [VerfasserIn]
• Verfasst von: Boda-Heggemann, Judit [VerfasserIn]
• Verfasst von: Simeonova-Chergou, Anna [VerfasserIn]
• Verfasst von: Steil, Volker [VerfasserIn]
• Verfasst von: Giordano, Frank Anton [VerfasserIn]
• Verfasst von: Fleckenstein, Jens [VerfasserIn]
• Titel: Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy
• Verf.angabe: Miriam Eckl, Lea Hoppen, Gustavo R. Sarria, Judit Boda-Heggemann, Anna Simeonova-Chergou, Volker Steil, Frank A. Giordano, Jens Fleckenstein
• E-Jahr: 2020
• Jahr: 24 November 2020
• Umfang: 9 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 15.07.2024
• Titel Quelle: Enthalten in: Physica medica
• Ort Quelle: Amsterdam : Elsevier, 1996
• Jahr Quelle: 2020
• Band/Heft Quelle: 80(2020) vom: Dez., Seite 308-316
• ISSN Quelle: 1724-191X
• DOI: doi:10.1016/j.ejmp.2020.11.007
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Adaptive radiation therapy
• Sach-SW: Cone-beam CT
• Sach-SW: Cycle-generative adversarial network-based image correction
• Sach-SW: Synthetic CT
• K10plus-PPN: 1895437024

Abstract

Purpose - Image-guided radiation therapy could benefit from implementing adaptive radiation therapy (ART) techniques. A cycle-generative adversarial network (cycle-GAN)-based cone-beam computed tomography (CBCT)-to-synthetic CT (sCT) conversion algorithm was evaluated regarding image quality, image segmentation and dosimetric accuracy for head and neck (H&N), thoracic and pelvic body regions. - Methods - Using a cycle-GAN, three body site-specific models were priorly trained with independent paired CT and CBCT datasets of a kV imaging system (XVI, Elekta). sCT were generated based on first-fraction CBCT for 15 patients of each body region. Mean errors (ME) and mean absolute errors (MAE) were analyzed for the sCT. On the sCT, manually delineated structures were compared to deformed structures from the planning CT (pCT) and evaluated with standard segmentation metrics. Treatment plans were recalculated on sCT. A comparison of clinically relevant dose-volume parameters (D98, D50 and D2 of the target volume) and 3D-gamma (3%/3mm) analysis were performed. - Results - The mean ME and MAE were 1.4, 29.6, 5.4 Hounsfield units (HU) and 77.2, 94.2, 41.8 HU for H&N, thoracic and pelvic region, respectively. Dice similarity coefficients varied between 66.7 ± 8.3% (seminal vesicles) and 94.9 ± 2.0% (lungs). Maximum mean surface distances were 6.3 mm (heart), followed by 3.5 mm (brainstem). The mean dosimetric differences of the target volumes did not exceed 1.7%. Mean 3D gamma pass rates greater than 97.8% were achieved in all cases. - Conclusions - The presented method generates sCT images with a quality close to pCT and yielded clinically acceptable dosimetric deviations. Thus, an important prerequisite towards clinical implementation of CBCT-based ART is fulfilled.