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Updated: Jul 14, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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基于深度学习的超高分辨率重建的可行性研究,使用大脑扩散加权图像中的k空间数据进行重建

Kensei Matsuo1, Takeshi Nakaura2, Kosuke Morita1

  • 1Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan.

Neuroradiology
|September 6, 2023
PubMed
概括

基于超分辨率深度学习的重建 (SR-DLR) 通过提高信号与噪音和对比与噪音的比率来提高脑MRI扩散加权图像质量. 然而,这种先进的技术并没有显著改变明显扩散系数的量化.

关键词:
深度学习是一种深度学习.扩散扩散是一种扩散.音响平面成像 - 图像成像磁共振成像技术 磁共振成像技术追溯研究是回顾性研究.

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相关概念视频

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.