Improving Cone-Beam Computed Tomography Based Adaptive Radiation Therapy with Deep Learning

During radiation therapy, patient anatomical changes may compromise treatment quality if the treatment plan, prepared prior to therapy, remains unchanged throughout the course. Adaptive radiation therapy (ART) has been developed to address this issue by adapting the treatment plan based on up-to-date patient anatomy. The widespread availability of cone-beam computed tomography (CBCT) and its capability for the 3D imaging of patient anatomy has made CBCT-based ART an emerging and increasingly popular technology in the field of radiation oncology. However, numerous challenges persist in the current workflow. One challenge involves generating synthetic CT (sCT) images that retain CBCT anatomy while maintaining CT image quality. Clinically used sCT is typically obtained through deformable image registration (DIR) between pre-planning CT (pCT) and CBCT; however, this method often inadequately preserves CBCT anatomy due to DIR errors. Another challenge stems from the truncation issue in CBCT images, resulting from size limitations of imaging panels, which consequently leads to inaccuracies in CBCT-based dose calculations. Additionally, auto-segmentation of CBCT is impeded by low image quality and a lack of training labels. Although addressing this issue is difficult, enhancing auto-segmentation is essential, as manual segmentation is highly time-consuming. This dissertation aims to improve CBCT-based ART by leveraging deep learning (DL) technologies. First, an unsupervised DL model is proposed to directly convert CBCT images into sCT images with reduced artifacts and more accurate Hounsfield Unit values, as found in CT scans. Second, the model's generalizability is investigated and discussed, along with potential solutions to address the generalizability problem. Third, two DL models are designed to extract and combine information from pCT and CBCT, in order to inpaint axial and longitudinal truncations in CBCT images. Through these three studies, the predicted truncation-free sCT images hold the potential to enhance ART workflows, enabling more accurate dose calculations compared to DIR-generated sCT. The dissertation then shifts its focus to CBCT auto-segmentation. Initially, an unsupervised DL-based DIR model is proposed to predict the deformation vector field between pCT and CBCT, enabling pCT structure propagation as the segmentation on CBCT. To further improve segmentation accuracy, a DL-based direct segmentation model assisted by DIR is proposed, which outperforms state-of-the-art DIR-based segmentation results. The contributions of this thesis are expected to enhance the accuracy and efficiency of sCT generation and CBCT auto-segmentation in the CBCT-based ART workflow.

Author dc:creator

• Liang, Xiao

Contributors dc:contributor

• Nguyen, Dan
• Jiang, Steve B.
• Lin, Mu-Han
• Wang, Jing
• Lu, Weiguo

• Deep Learning
• Image Processing, Computer-Assisted
• Radiotherapy Planning, Computer-Assisted
• Radiotherapy, Image-Guided
• Cone-Beam Computed Tomography

Language dc:language

en

Identifier

1381370399

OAI identifier oai:identifier

oai:utswmed-ir.tdl.org:2152.5/10073