迭代重建
快速傅里叶变换
计算机科学
图像质量
笛卡尔坐标系
k-空间
计算
迭代法
医学影像学
人工智能
图像(数学)
采样(信号处理)
算法
计算机视觉
傅里叶变换
数学
滤波器(信号处理)
数学分析
几何学
标识
DOI:10.1109/msp.2010.936726
摘要
Magnetic resonance imaging (MRI) is a sophisticated and versatile medical imaging modality. Traditionally, MR images are reconstructed from the raw measurements by a simple inverse 2D or 3D fast Fourier transform (FFT). However, there are a growing number of MRI applications where a simple inverse FFT is inadequate, e.g., due to non-Cartesian sampling patterns, non-Fourier physical effects, nonlinear magnetic fields, or deliberate under-sampling to reduce scan times. Such considerations have led to increasing interest in methods for model-based image reconstruction in MRI.
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