Deep learning for undersampled MRI reconstruction

By Chang Min Hyun, Hwa Pyung Kim, Sung Min Lee, SungChull Lee, J. K. Seo. Physics in Medicine and Biology, 2017

506 citations recorded by the source corpus.

This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, a small number of low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of the Fourier transforms of the subsampled and fully sampled k-space data. Our experiments show the remarkable performance of the proposed method; only 29 of the k-space data can generate images of high quality as effectively as standard MRI reconstruction with the fully sampled data.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/67ebfc6a6013fcc4b64035994ad80ddfbdd6bc5e

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In Computational Mechanics

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Diffusion models in medical imaging: A comprehensive survey (2022, background): “Accordingly, their radiation exposure reduces from the standard dose, or the imaging process is done in an under-sampled or sparse-view manner [88, 89, 90, 91].”
Transformers in Medical Imaging: A Survey (2022, background): “Reducing the number of MRI measurements can result in faster scan times and a reduction in artifacts due to patients movement at the expense of aliasing artifacts in the image [295].”
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018, methodology): “U-Net models and their variants have successfully been used for many image-to-image prediction tasks including MRI reconstruction [17, 12] and image segmentation [29].”