Medical image reconstruction and medical simulation are active research areas in the field of medical image processing and have received a great deal of attention over the past ten years. Medical image reconstruction allows a physician to visualize the interior organs and tissues of patient's body in a nondestructive or minimally invasive way for improved diagnosis and better treatment selection. Medical simulation can model the biochemical nature, metabolic characteristics, and geometric arrangement of human organs such that the change in these properties can be studied and predicted when the environmental parameters are altered. This dissertation covers two distinct but related areas of work: (1) a maximum a posterior (MAP) tomosynthetic reconstruction for X-ray imaging, and (2) real-time deformation modeling of soft organs and tissues utilizing an adaptive mass-spring model, which is the basis of a prototype for a virtual surgery simulation system. The proposed tomosynthetic reconstruction algorithm described in Chapter 2 is based on Bayes' theorem and reconstructs a scanned object by optimizing an objective function in a multi-resolution framework such that both image quality and algorithmic efficiency are improved. In Chapter 3, a virtual surgery simulation system is developed based on an adaptive mass-spring deformable graphical model proposed for soft tissue and organ modeling. The presented deformable model is able to generate visually realistic real-time deformations for visual feedback. Coupled with the haptic feedback provided by a PHANToM device, the presented simulation system allows users to interactively manipulate virtual organs and tissues to simulate routine clinical procedures such as bronchoscopies or kidney biopsies.
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