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

• Published in: Physics in medicine & biology Vol. 68; no. 9; pp. 95006 - 95016
• Main Authors: Bauer, Cornelius J, Teske, Hendrik, Walter, Alexandra, Hoegen, Philipp, Adeberg, Sebastian, Debus, Jürgen, Jäkel, Oliver, Giske, Kristina
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 07.05.2023
• Subjects: Algorithms; articulated skeleton; biomechanical model; Bone and Bones; bone and joint kinematics; deformable image registration; head and neck cancer; Head and Neck Neoplasms - diagnostic imaging; Head and Neck Neoplasms - radiotherapy; Humans; Image Processing, Computer-Assisted - methods; interfractional motion; Neck - diagnostic imaging; Radiotherapy Planning, Computer-Assisted; Reproducibility of Results; interfractional motion; deformable image registration; articulated skeleton; head and neck cancer; biomechanical model; bone and joint kinematics
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/acc7f1

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.