Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
International Workshop on Statistical Atlases and Computational Models of the Heart(2023)
摘要
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's
function and condition in a non-invasive manner. Undersampling of the k-space
is employed to reduce the scan duration, thus increasing patient comfort and
reducing the risk of motion artefacts, at the cost of reduced image quality. In
this challenge paper, we investigate the use of a convolutional recurrent
neural network (CRNN) architecture to exploit temporal correlations in
supervised cine cardiac MRI reconstruction. This is combined with a
single-image super-resolution refinement module to improve single coil
reconstruction by 4.4% in structural similarity and 3.9% in normalised mean
square error compared to a plain CRNN implementation. We deploy a high-pass
filter to our ℓ_1 loss to allow greater emphasis on high-frequency details
which are missing in the original data. The proposed model demonstrates
considerable enhancements compared to the baseline case and holds promising
potential for further improving cardiac MRI reconstruction.
更多查看译文
关键词
cine cardiac mri reconstruction,convolutional recurrent network
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要