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15 March 2013 Estimation of lung tissue incompressibility variation throughout respiration for tumor targeting in lung radiotherapy
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A novel technique is proposed to characterize lung tissue incompressibility variation during respiration. Lung tissue incompressibility variation stems from significant air content variation in the tissue throughout respiration. Estimating lung tissue incompressibility and its variation is critical for computer assisted tumor motion tracking. Continuous tumor motion during respiration is a major challenge in lung cancer treatment by external beam radiotherapy. If not accounted for, this motion leads to areas of radiation over dosage for the lung normal tissues. Since no effective imaging modality is available for real-time lung tumor tracking, computer based modeling which has the capability for accurate tissue deformation estimation can be a good alternative. Lung tissue deformation estimation can be made using the lung Finite Element (FE) model where its accuracy depends on input tissue biomechanical properties including incompressibility parameter. In this research, an optimization algorithm is proposed to estimate the incompressibility parameter function in terms of respiration cycle time. In this algorithm, the incompressibility parameter and lung pressure values are varied systematically until optimal values, which result in maximum similarity between acquired and simulated 4D CT images of the lung, are achieved for each respiration time point. The simulated images are constructed using a reference image in conjunction with the deformation field obtained from the lung’s FE model in each respiration time increment. We demonstrated that utilizing the calculated function along with respiratory system FE modeling leads to accurate tumor targeting, hence potentially improving lung radiotherapy outcome.
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Zahra Shirzadi and Abbas Samani "Estimation of lung tissue incompressibility variation throughout respiration for tumor targeting in lung radiotherapy", Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 867123 (15 March 2013);

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