Estimation of Lung's Air Volume and Its Variations Throughout Respiratory CT Image Sequences

Abstract

A respiratory image-sequence-segmentation technique is introduced based on a novel image-sequence analysis. The proposed technique is capable of segmenting the lung's air and its soft tissues followed by estimating the lung's air volume and its variations throughout the image sequence. Accurate estimation of these two parameters is very important in many applications related to lung disease diagnosis and treatment systems (e.g., brachytherapy), where the parameters are either the variables of interest themselves or are dependent/independent variables. The concept of the proposed technique involves using the image sequence's combined histogram to obtain a reasonable initial guess for the lung's air segmentation thresholds. This is followed by an optimization process to find the optimum threshold values that best satisfy the lung's air mass conservation and tissue incompressibility principles. These threshold values are consequently applied to estimate the lung's air volume and its variations throughout respiratory Computed Tomography (CT) image sequences. Ex vivo experiments were conducted on porcine left lungs in order to demonstrate the performance of the proposed technique. The proposed method was initially validated using a breath-hold CT image sequence with known air volumes inside the lung, where results show that the proposed technique outperforms single-histogram-based methods. This was followed by demonstrating the proposed technique's application in a 4-D-CT respiratory sequence, where the air volume inside the lung was unknown. Consistency of the obtained results in the latter experiment with tissue near incompressibility principle was validated. The results indicate a very good ability of the proposed method for estimating the lung's air volume and its variations in a respiratory image sequence.