Multi Slice

Multi-slice techniques aim to accelerate data acquisition and analysis across diverse fields, from magnetic resonance imaging (MRI) to 3D reconstruction and tensor analysis. Current research focuses on improving reconstruction algorithms, often employing deep learning models like denoising diffusion probabilistic models and U-Nets, to overcome challenges such as signal interference in SMS-MRI and occlusion in single-view 3D reconstruction. These advancements enhance the speed and efficiency of data processing, leading to improved image quality in medical imaging and more efficient analysis of large datasets in scientific applications like synchrotron micro-CT. The resulting improvements in speed and data handling have significant implications for various scientific disciplines and practical applications.