Ms. Zahra Shirzadi Profile

Zahra Shirzadi

at Univ of Western Ontario

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Area of Expertise:

Biomedical Engineering , Medical Imaging , Image Processing , Biomechanical Modeling

Publications (2)

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Proceedings Article | 15 March 2013 Paper

Estimation of lung tissue incompressibility variation throughout respiration for tumor targeting in lung radiotherapy

Zahra Shirzadi, Abbas Samani

Proceedings Volume 8671, 867123 (2013) https://doi.org/10.1117/12.2007049

KEYWORDS: Lung, Tissues, Tumors, Lung cancer, Radiotherapy, Motion models, Optimization (mathematics), Computer simulations, 4D CT imaging, Image segmentation

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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.

Proceedings Article | 17 February 2012 Paper

Lung tumor motion prediction during lung brachytherapy using finite element model

Zahra Shirzadi, Ali Sadeghi Naini, Abbas Samani

Proceedings Volume 8316, 83160I (2012) https://doi.org/10.1117/12.906511

KEYWORDS: Lung, Tumors, Motion models, Tissues, Lung cancer, 3D modeling, Finite element methods, Image segmentation, Computed tomography, Natural surfaces

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A biomechanical model is proposed to predict deflated lung tumor motion caused by diaphragm respiratory motion. This model can be very useful for targeting the tumor in tumor ablative procedures such as lung brachytherapy. To minimize motion within the target lung, these procedures are performed while the lung is deflated. However, significant amount of tissue deformation still occurs during respiration due to the diaphragm contact forces. In the absence of effective realtime image guidance, biomechanical models can be used to estimate tumor motion as a function of diaphragm's position. To develop this model, Finite Element Method (FEM) was employed. To demonstrate the concept, we conducted an animal study of an ex-vivo porcine deflated lung with a tumor phantom. The lung was deformed by compressing a diaphragm mimicking cylinder against it. Before compression, 3D-CT image of this lung was acquired, which was segmented and turned into FE mesh. The lung tissue was modeled as hyperelastic material with a contact loading to calculate the lung deformation and tumor motion during respiration. To validate the results from FE model, the motion of a small area on the surface close to the tumor was tracked while the lung was being loaded by the cylinder. Good agreement was demonstrated between the experiment results and simulation results. Furthermore, the impact of tissue hyperelastic parameters uncertainties in the FE model was investigated. For this purpose, we performed in-silico simulations with different hyperelastic parameters. This study demonstrated that the FEM was accurate and robust for tumor motion prediction.

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