FusionScan: A Novel AI-Based Multi-Modal Imaging Technique for Enhanced Medical Diagnostics, Inspired by Stanford's Mini-Fellowship Program in Molecular Imaging
Keywords: Molecular Imaging, Magnetic Resonance Imaging (MRI), X-Ray Computed Tomography (CT), AI, Medical, Oncology, Diagnostics, Deep Learning, Fusion.
TL;DR: AI for Multi-Modal Imaging for Enhanced Medical Diagnostics
Abstract: Modern medical diagnostics rely heavily on imaging technologies, each characterized by unique advantages and inherent limitations. Inspired by the insights from Stanford's Mini-Fellowship Program in Molecular Imaging Techniques, this research proposes FusionScan, an innovative technique that integrates Magnetic Resonance Imaging (MRI) and X-Ray Computed Tomography (CT) as imaging modalities along with an AI-based system. The research demonstrates how the fused system can provide unparalleled depth, resolution, and functional data by synthesizing the strengths of these two techniques, offering a robust solution for complex medical challenges. The goal is to fuse the images of the two modalities and analyze them using AI models, hence enhancing the resolution and specificity of the molecular images. Simulation results demonstrate the value of AI in enhancing medical images.
Submission Number: 119
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