Lung abnormalities at multimodality imaging after radiation therapy for non-small cell lung cancer.
This paper proposes a novel framework for the identification of the radiation-induced lung injury (RILI) after radiation therapy (RT) using 4D computed tomography (CT) scans. The proposed methodology consists of four components: (i) elastic image registration; (ii) segmentation of the lung fields; (iii) extraction of functional and texture features; and (iv) classification of the lung tissues. The registration step locally aligns the consecutive phases of the respiratory cycle using an elastic image registration approach based on descent minimization of the sum of squared difference similarity metric. Secondly, lung fields are segmented using a hybrid framework that integrates an adaptive shape prior model, a first-order intensity model, and a second order homogeneity descriptor of the lung tissues. Next, regional features that describe both the texture features using the novel 7<sup>th</sup>-order Markov-Gibbs random field (MGRF) model in addition to the lung functionality features (e.g., ventilation and elasticity) are estimated from a segmented lungs. Finally, a random forest classifier (RF) is applied to distinguish between injured and normal lung tissues. To evaluate the proposed framework, we used data sets that have been collected from 13 patients who had underwent RT treatment. Experimental results demonstrate the promise of the proposed framework for the identification of the injured lung region, and thus hold the promise as a valuable tool for early detection of RILI.