Evaluation and application of 3D lung warping and registration model using HRCT images

Image-based study of structure-function relationships is a challenging problem in that the structure or region of interest may vary in position and shape on images captured over time. Such variation may be caused by the change in body posture or the motion of breathing and heart beating. Therefore, the structure or region of interest should be registered before any further regional study can be carried out. In this paper, we propose a novel approach to study the structure-function relationship of ventilation using a previously developed 3D lung warping and registration model. First, we evaluate the effectiveness of the lung warping and registration model using a set of criteria, including apparent lung motion patterns and ground truths. Then, we study the ventilation by integrating the warping model with air content calibration. The warping model is applied to three CT lung data sets, obtained under volume control of FRC, 40% and 75% vital capacity (VC). Dense displacement fields are obtained to represent deformation between different lung volume steps. For any specific region of interest, we first register it between images over time using the dense displacement, and then estimate the corresponding regional inspired air content. Assessments include change of regional volume during inspiration, change of regional air content, and the distribution of regional ventilation. This is the first time that 3D warping of lung images is applied to assess clinically significant pulmonary functions.