Multiple Acquisition

Multiple acquisition techniques aim to efficiently and effectively gather diverse data types or from varied acquisition parameters, improving data quality and reducing limitations of single-acquisition methods. Current research focuses on developing adaptable models, such as unrolled convolutional neural networks and hybrid physics-based/deep learning approaches, that can handle multiple acquisition conditions simultaneously, often using dynamic weight prediction or feature scaling to adjust to different settings. This research is significant because it allows for higher-quality data acquisition with reduced cost and time, impacting fields like medical imaging (MRI), robotics (musculoskeletal modeling), and geophysics (seismic data processing) by enabling more robust and efficient data analysis.