A hybrid patient‐specific biomechanical model based image registration method for the motion estimation of lungs

HighlightsA hybrid image registration approach for lung motion estimation is proposed.Biomechanical models estimate lung motion with compensation from image registration.The method allows more accurate motion estimations on lung surface regions.Displacement compensation analysis can help optimising biomechanical models. Graphical abstract Figure. No Caption available. ABSTRACT This paper presents a new hybrid biomechanical model‐based non‐rigid image registration method for lung motion estimation. In the proposed method, a patient‐specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non‐rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity‐based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity‐based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non‐rigid intensity‐based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson’s ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated.

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