Deep-Learning-Based Magnetic Resonance Simultaneous Multislice Imaging Using Holographic Image Decoding
Year: 2024
Type: conference-paper
Abstract: We propose a new magnetic resonance simultaneous multislice imaging (SMS) by developing our previously proposed imaging method based on the principle of holography. Different amplitude modulations for each slice are applied to separate the slice images. The slice image separation is performed using a data-driven science in which U-Nets separates the slice images using the given amplitude modulation as a cue. Simulation experiments using a real-valued and an phase-varied image model revealed the relationship between the coefficients of amplitude modulation, the number of simultaneously excited slices, and image quality. In addition, we performed image separation experiments on the holographic imaging method by approximating the excited volume region as five slice images with no gaps, and showed the feasibility of image separation. This method has the potential to be a new type of SMS.
Authors: Satoshi Ito, Yuki Sato, Naoya Endo, Shohei Ouchi
Institutions: Utsunomiya University, National Institute of Technology
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4402915948