Radiation therapy (RT) is one of the most common treatments for lung cancer. Atelectasis is a common co-pathology of lung cancer and is the condition when one or more regions of the lung collapse. Over the course of radiation treatment, regions of the lung that were previously collapsed may inflate with air again due the shrinking size of the tumor or for some other reason. The lung often has significant anatomical shape differences when comparing a CT image of the lung with collapse to a CT image of the same lung after the collapsed region inflates with air. Patients with large anatomical changes due to parts of the lung that are no longer collapsed may benefit from adaptive radiation therapy (ART). ART refers to the procedure of updating the RT treatment plan over the course of treatment to accommodate anatomical changes in the planning CT. The purpose of ART is to better treat the tumor while reducing the damage to the surrounding healthy lung tissue. Image registration may be used to update the radiation treatment plan by deforming the original RT plan to accommodate for the anatomical shape changes. Image registration of the CT scans of the lungs with large shape changes is challenging. Traditional registration algorithms can not handle large anatomical changes well, and often the pre-knowledge of the corresponding anatomical structures is required to achieve reasonable alignment of the features in the lung. In this research, we studied how varifold-based image registration could be adapted to register lung images with large deformation. The pipeline developed in the dissertation for varifold-based image registration approach is named the pulmonary vessel and surface varifold-based registration algorithm (PVSV). We found that PVSV was able to handle lung shapes with substantial differences, and is robust to missing information. The PVSV method achieved the best performance compared with previous image registration methods applied to lung images with a higher success rate.
The second focus of this dissertation was to determine which image registration algorithm performed best for registering pulmonary CT scans of patients with chronic obstructive pulmonary disease (COPD). This project is part of the “SubPopulations and InteRmediate Outcome Measures In COPD Study” (SPIROMICS). SPIROMICS is an ongoing prospective cohort study to identify subpopulations and intermediate outcome measures in patients with COPD. The purpose of this project is to identify biomechanics biomarkers and track disease progression of COPD patients. CT images were collected at baseline, one-year, three-year, and five-year follow-ups for almost 3000 COPD subjects from 14 university-based clinical centers across the United States. In our work, we compared and contrasted the registration algorithms performance of four state-of-the-art image registration algorithms. Biomechanical features were extracted from the transformations, and a statistical analysis was performed. Results show statistically significant increasing or decreasing trends in the mean, standard deviation and entropy of the Jacobian determinant; the mean of the anisotropic deformation index (ADI); and the energy of the slab-rod index (SRI) as function of GOLD stage globally, and on a lobe-by-lobe basis. Furthermore, these trends held for all four registration algorithms suggesting the robustness of biomechanical properties extracted by image registration, and the authenticity of the detected trends. In the future, we hope that this work may be used to recognize abnormal pulmonary behavior resulting from COPD and predict COPD progression.
|Advisor:||Christensen, Gary E.|
|Commitee:||Durumeric, Oguz C., Reinhardt, Joseph M., Hugo, Geoffrey D., Saha, Punam K.|
|School:||The University of Iowa|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- Iowa|
|Source:||DAI-B 82/8(E), Dissertation Abstracts International|
|Subjects:||Computer Engineering, Biomedical engineering, Electrical engineering|
|Keywords:||Biomechanical Analysis, COPD, Large Anatomical Deformation, Lung Cancer, Medical Image Registration, Varifold|
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