A 4D Global Respiratory Motion Model of the Thorax Based on CT Images: A Proof of Concept

PurposeRespiratory motion reduces the sensitivity and specificity of medical images especially in the thoracic and abdominal areas. It may affect applications such as cancer diagnostic imaging and/or radiation therapy (RT). Solutions to this issue include modeling of the respiratory motion in order to optimize both diagnostic and therapeutic protocols. Personalized motion modeling required patient‐specific four‐dimensional (4D) imaging which in the case of 4D computed tomography (4D CT) acquisition is associated with an increased dose. The goal of this work was to develop a global respiratory motion model capable of relating external patient surface motion to internal structure motion without the need for a patient‐specific 4D CT acquisition.MethodsThe proposed global model is based on principal component analysis and can be adjusted to a given patient anatomy using only one or two static CT images in conjunction with a respiratory synchronized patient external surface motion. It is based on the relation between the internal motion described using deformation fields obtained by registering 4D CT images and patient surface maps obtained either from optical imaging devices or extracted from CT image‐based patient skin segmentation. 4D CT images of six patients were used to generate the global motion model which was validated by adapting it on four different patients having skin segmented surfaces and two other patients having time of flight camera acquired surfaces. The reproducibility of the proposed model was also assessed on two patients with two 4D CT series acquired within 2 weeks of each other.ResultsProfile comparison shows the efficacy of the global respiratory motion model and an improvement while using two CT images in order to adapt the model. This was confirmed by the correlation coefficient with a mean correlation of 0.9 and 0.95 while using one or two CT images respectively and when comparing acquired to model generated 4D CT images. For the four patients with segmented surfaces, expert validation indicates an error of 2.35 ± 0.26 mm compared to 6.07 ± 0.76 mm when using a simple interpolation between full inspiration (FI) and full expiration (FE) CT only; i.e., without specific modeling of the respiratory motion. For the two patients with acquired surfaces, this error was of 2.48 ± 0.18 mm. In terms of reproducibility, model error changes of 0.12 and 0.17 mm were measured for the two patients concerned.ConclusionsThe framework for the derivation of a global respiratory motion model was developed. A single or two static CT images and associated patient surface motion, as a surrogate measure, are only needed to personalize the model. This model accuracy and reproducibility were assessed by comparing acquired vs model generated 4D CT images. Future work will consist of assessing extensively the proposed model for radiotherapy applications.