Project Team
Students
Divya Verma
Computer Science
Penn State Harrisburg
Faculty Mentors
Bimal Ghimire, Ph.D
Penn State Harrisburg
Assistant Professor of Computer Science, School of Science, Engineering, and Technology
Project
Project Video
Project Abstract
The advent of Neural Radiance Fields (NeRFs) have gained industry-wide attention for their capability in 3D novel view synthesis from sparse 2D data, outperforming traditional methods in rendering continuous 3D scenes with high accuracy. As advancements in artificial intelligence continue, its important we analyze how their applications may be leveraged in different industries. This paper explores the application of NeRFs in the medical field, specifically in medical imaging and surgery simulation. We analyze the benefits and limitations of integrating NeRFs with X-rays, CT scans, and ultrasounds, highlighting their potential to enhance image resolution and reduce radiation exposure. Models like MedNeRF, ACNeRF, and Oral-3Dv2 demonstrate the versatility of NeRFs in reconstructing detailed 3D views from minimal inputs. Furthermore, EndoscopeNeRF shows promise in surgical simulation by leveraging endoscopic data to create realistic 3D representations of internal cavities. Despite their potential, challenges such as data acquisition, computational requirements, and adaptation to variable lighting remain. This paper proposes enhancements in NeRF frameworks to address these challenges, aiming to improve the accessibility and efficiency of medical NeRF applications. By refining NeRF architectures and integrating advanced imaging techniques, we can significantly advance the accuracy and utility of 3D medical imaging and simulation, ultimately contributing to better patient outcomes and innovative medical practices.
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