Biofidelic image registration for head and neck region utilizing an in-silico articulated skeleton as a transformation model

• Verfasst von: Bauer, Cornelius Jonathan [VerfasserIn]
• Verfasst von: Teske, Hendrik [VerfasserIn]
• Verfasst von: Walter, Alexandra [VerfasserIn]
• Verfasst von: Hoegen, Philipp [VerfasserIn]
• Verfasst von: Adeberg, Sebastian [VerfasserIn]
• Verfasst von: Debus, Jürgen [VerfasserIn]
• Verfasst von: Jäkel, Oliver [VerfasserIn]
• Verfasst von: Giske, Kristina [VerfasserIn]
• Titel: Biofidelic image registration for head and neck region utilizing an in-silico articulated skeleton as a transformation model
• Verf.angabe: Cornelius J. Bauer, Hendrik Teske, Alexandra Walter, Philipp Hoegen, Sebastian Adeberg, Jürgen Debus, Oliver Jäkel and Kristina Giske
• E-Jahr: 2023
• Jahr: 19 April 2023
• Umfang: 12 S.
• Fussnoten: Gesehen am 14.06.2023
• Titel Quelle: Enthalten in: Physics in medicine and biology
• Ort Quelle: Bristol : IOP Publ., 1956
• Jahr Quelle: 2023
• Band/Heft Quelle: 68(2023), 9 vom: Apr., Artikel-ID 095006, Seite 1-12
• ISSN Quelle: 1361-6560
• DOI: doi:10.1088/1361-6560/acc7f1
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1848961634

Abstract

Objective. We propose an integration scheme for a biomechanical motion model into a deformable image registration. We demonstrate its accuracy and reproducibility for adaptive radiation therapy in the head and neck region. Approach. The novel registration scheme for the bony structures in the head and neck regions is based on a previously developed articulated kinematic skeleton model. The realized iterative single-bone optimization process directly triggers posture changes of the articulated skeleton, exchanging the transformation model within the deformable image registration process. Accuracy in terms of target registration errors in the bones is evaluated for 18 vector fields of three patients between each planning CT and six fraction CT scans distributed along the treatment course. Main results. The median of target registration error distribution of the landmark pairs is 1.4 ± 0.3 mm. This is sufficient accuracy for adaptive radiation therapy. The registration performs equally well for all three patients and no degradation of the registration accuracy can be observed throughout the treatment. Significance. Deformable image registration, despite its known residual uncertainties, is until now the tool of choice towards online re-planning automation. By introducing a biofidelic motion model into the optimization, we provide a viable way towards an in-build quality assurance.