Development of a framework for the generation of patient-specific lung cancer geometric model using 4D-MRI image data.

Abstract

Being an effective treatment option, radiation therapy aims to eradicate or control malignant cells while minimizing adjacent healthy tissue and organs injury. As lung is a multidimensional and highly patient-specific structure with unique spatial and temporal motion pattern, it is a challenge for surgeons to deliver radiation doses conforming only to the tumor. Intense efforts have been made in recent years to asses breathing motion of lung cancer models using 4D-CT images. However, although 4D-CT generates high spatial and temporal resolution images, one major downside with CT is the utilization of ionizing radiation and the accompanied increased cancer risk. Hence, in this project, a framework for reconstructing patient-specific lung cancer model was developed using 4D-MRI data to investigate if 4D-MRI can be use as an alternative technology for lung cancer modeling. Mimics software was used to generate 3D surface mesh and imported into MeshLab for reconstruction. Taubin smoothing and quadric edge collapse decimation was found to be effective strategies in preserving the geometric features of the lung and reducing the mesh density respectively. Analysis has showed that the reconstructed 4D-MRI derived models, especially tumor, did not resemble 4D-CT derived models fully for Patient 1 and 2. Due to the lack of patient datasets, a thorough assessment was not achieved. Hence, it is recommended that more patient datasets of better resolution should be acquired for more elaborate analysis.