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Thesis Format

Integrated Article

Degree

Master of Science

Program

Biomedical Engineering

Supervisor

Samani, Abbas

2nd Supervisor

Sadeghi-Naini, Ali

Affiliation

York University

Joint Supervisor

Abstract

Radiation therapy is a main component of treatment for many lung cancer patients. However, the respiratory motion can cause inaccuracies in radiation delivery that can lead to treatment complications. In addition, the radiation-induced damage to healthy tissue limits the effectiveness of radiation treatment. Motion management methods have been developed to increase the accuracy of radiation delivery, and functional avoidance treatment planning has emerged to help reduce the chances of radiation-induced toxicity. In this work, we have developed biomechanical model-based techniques for tumor motion estimation, as well as lung functional imaging. The proposed biomechanical model accurately estimates lung and tumor motion/deformation by mimicking the physiology of respiration, while accounting for heterogeneous changes in the lung mechanics caused by COPD, a common lung cancer comorbidity. A biomechanics-based image registration algorithm is developed and is combined with an air segmentation algorithm to develop a 4DCT-based ventilation imaging technique, with potential applications in functional avoidance therapies.

Summary for Lay Audience

Lung cancer is the leading cause of cancer-related death in Canada. Radiation therapy is a main component of treatment for many lung cancer patients, in which high-energy beams are planned to target the tumor cells and destroy them.

Both the lung and the tumor can move and deform during radiation therapy as the patient breathes. If not accounted for, the motion of the tumor can considerably reduce the effectiveness of treatment. An ideal solution is to develop a system that can track the tumor in real-time and guide the radiation delivery accordingly. This tracking system requires an accurate estimation of the tumor’s location and shape. Currently, four-dimensional computed tomography (4DCT) scans of the patients are used to plan the radiation delivery. We have developed a technique for estimating tumor motion/deformation by modeling the breathing mechanics using 4DCT scans. Our biomechanical model mimics lung physiology and is driven by the pressure and diaphragm motion present during a breathing cycle. Chronic obstructive pulmonary disease (COPD), is a progressive lung disease that commonly coexists with lung cancer and affects lung mechanics in a heterogeneous manner. By incorporating these heterogeneous changes in lung mechanics into our model, we achieved high accuracies for tumor motion/deformation estimation.

Radiation treatment planning aims at maximizing the tumor dose while minimizing the healthy lung tissue exposure to radiation. Considering the heterogeneity in the lung function present in cancer patients (due to COPD and/or smoking), the outcome of radiation therapy can be improved by planning the radiation beam to deliberately avoid the well-functioning regions in the lung and instead pass through the already poorly-functioning regions. This method of planning is called functional avoidance and requires regional information on lung function that is usually obtained using magnetic resonance (MR) or nuclear medicine imaging. We have developed a 4DCT-based technique for imaging the regional ventilation, as a surrogate for lung function. 4DCT is a cost-effective and accessible alternative to the current modalities. Our ventilation imaging method utilizes our accurate lung biomechanical model to find the regional change in the air volume, i.e. ventilation. Results show good agreement with MR-based functional images.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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