User-Centric Biomedical Imaging Platform for Realistic 3D Rendering

The increasing complexity of medical procedures requires the adoption of advanced biomedical software to optimize data management and enhance user experience. This research proposes a User-Centric Biomedical Imaging Platform, which integrates various imaging modalities and rendering techniques to support physicians during the planning and execution of interventions. The platform integrates an innovative Advanced Realistic Rendering Technique (AR2T), offering detailed and realistic visualization of volumetric data acquired through computed tomography and magnetic resonance imaging. A multi-target voxelization method, combined with a novel 3D fractal dimension based approach, introduces significant rendering performance improvement. Deep learning-based denoising, powered by a cutting edge algorithm-agnostic blending technique, further enhances the quality of biomedical 3D imaging. Supported by multiple clinical assessments, this industrial PhD research contributes not only to technological advancement but also to more effective and informed patient care.