Defense Date


Document Type


Degree Name

Doctor of Philosophy



First Advisor

Yuichi Motai


Identification of accurate tumor location and shape is highly important in lung cancer radiotherapy, to improve the treatment quality by reducing dose delivery errors. Because a lung tumor moves with the patient's respiration, breathing motion should be correctly analyzed and predicted during the treatment for prevention of tumor miss or undesirable treatment toxicity. Besides, in Image-Guided Radiation Therapy (IGRT), the tumor motion causes difficulties not only in delivering accurate dose, but also in assuring superior quality of imaging techniques such as four-dimensional (4D) Cone Beam Computed Tomography (CBCT) and 4D Magnetic Resonance Imaging (MRI). Specifically, 4D CBCT used in CBCT IGRT requires precise respiratory signal extraction to avoid burry edges, inaccurate tumor shape, and motion-induced artifacts on the reconstructed CBCT image. 4D MRIs used in MRI-guided radiation therapy typically have low resolution as a tradeoff with field of view, image acquisition time, and image quality. To predict the tumor motion and guarantee the superior quality of the imaging techniques, the dissertation is divided into three parts. The first part describes a new prediction method for respiration-related tumor movements, called Intra- and Inter-fractional variation prediction using Fuzzy Deep Learning (IIFDL). IIFDL clusters the respiratory movements based on breathing similarities, and estimates patients' breathing motion using the proposed predictor, called fuzzy deep learning. The second part of the dissertation includes a novel marker-less binning method for 4D CBCT projections, called Image Registration-based Projection Binning (IRPB), which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing periodicity of tissue feature point trajectories. The third part the dissertation explains a novel Super-Resolution (SR) method for 4D MRI, called Recurrent Deep Learning-based SR (RDLS), comprised of feature extraction, recurrent nonlinear mapping, and reconstruction. RDLS estimates high-resolution MRIs from low-resolution MRIs according to a specified magnification power.


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