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Thoracic CT image registration is crucial for thoracic image analysis and related downstream clinical diagnosis and treatment. Due to the large-scale deformation of multiple tissues and organs involved in the respiratory process, achieving high-precision thoracic CT image registration is very challenging. It should be noted that this large-scale deformation occurs in a continuous cycle over time and space. Therefore, incorporating spatio-temporal cycle consistency into the registration can aid in achieving high-precision thoracic CT image registration.However, most existing registration methods either focus only on static registration between two typical respiratory stages (usually extreme exhalation and extreme inhalation), or are based on assumptions of specific respiratory motion models that are difficult to generalize.To this end, this paper proposes a spatio-temporal cycle consistency registration method to embed the continuous cyclic changes of respiratory deformation in time and space. Specifically, we first construct continuous sequential CT image registration in two stages: exhale to inhale and inhale to exhale, to introduce spatio-temporal cyclic consistency of global deformation. In addition, spatio-temporal consistency based on the lung mask is employed to deal with the large-scale deformation in the lung region to achieve better local matching. Finally, based on the spatio-temporal consistency of the deformation of the lung vessel mask, the internal deformation of the lung is further refined to improve the registration accuracy.We conducted experiments and evaluations on the 4D-CT DIR and COPD datasets. Experimental results show that our method outperforms advanced methods by incorporating spatio-temporal cycle consistency.
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