Improved lung tumor autocontouring algorithm for intrafractional tumor tracking using 0.5 T linac-MR

To add an intelligent parameter optimization capability to our autocontouring algorithm, and evaluate its performance using in-vivo data. Methods An autocontouring algorithm for intrafractional lung-tumor tracking using linac-MR was previously developed based on pulse-coupled neural networks. The algorithm's contouring performance is dependent on eight parameters (including four integer parameters). Previously, the parameters were optimized using a time-consuming, exhaustive method. To avoid this inefficiency, adaptive particle swarm optimization (APSO) was adopted in this study, which is a stochastic, non-gradient based optimization algorithm that can handle integer variables. For this study, six non-small cell lung cancer patients were imaged with 3T MRI at similar to 4 frames per second (2D sagittal plane, free breathing). For each patient, an expert delineated a gold standard contour (ROIstd) of the lung tumor in 130 consecutive images. The first 30 ROIstd were used for parameter optimization, and the rest 100 ROIstd were used to validate autocontours (ROIauto). In each image, Dice similarity index, Hausdorff distance, and centroid position difference (Delta dcentroid) were calculated between ROIstd and ROIauto to measure their similarity. Results&Conclusion An efficient, fully automatic parameter optimization was added to our autocontouring algorithm. Using the six patients data, approximately 1/24 time reduction was achieved in parameter optimization (63-125 hrs to 2-4 hrs per patient), while maintaining the same or slightly improved performance.