2018
DOI: 10.1109/tmi.2017.2785879
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DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Abstract: Abstract-Compressed Sensing Magnetic Resonance Imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging based fast MRI, which utilises multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstr… Show more

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Cited by 886 publications
(684 citation statements)
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“…It should be noted that the AF used in the latest brain MRI reconstruction is even up to 10 . Nevertheless, the image size is 256 × 256, and the number of training data is 16,095 in that work (approximately 21 times larger than that used in this work).…”
Section: Discussionmentioning
confidence: 93%
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“…It should be noted that the AF used in the latest brain MRI reconstruction is even up to 10 . Nevertheless, the image size is 256 × 256, and the number of training data is 16,095 in that work (approximately 21 times larger than that used in this work).…”
Section: Discussionmentioning
confidence: 93%
“…However, conventional CS‐MRI had some limitations: (1) The sparse transforms (e.g., total variation or discrete wavelet transform) could be inadequate to describe complex image contents, especially for biological tissues. This could lead to artifacts and loss of detailed structures in reconstructed results . (2) CS used iterative strategies to optimize some objective functions, which was typically time‐consuming.…”
Section: Introductionmentioning
confidence: 99%
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“…When the acceleration factor was 8, both outputs from X‐net and Y‐net were slightly blurred. It has been demonstrated that this type of blurring effect can be offset by utilizing an adversarial network …”
Section: Discussionmentioning
confidence: 99%
“…There have been many published techniques on undersampled image reconstruction, and image reconstruction can also be interpreted as an image artifact correction problem, suggesting feasibility for deblurring off‐resonance. Some convolutional neural network techniques operate entirely in the image domain and enhance image quality with supervised, perceptual, and adversarial losses . Still operating primarily in the image domain but drawing from a SENSE‐based reconstruction, there are techniques that use deep variational networks to learn a deep model prior to replacing the sparsity regularizers in the compressed‐sensing reconstruction equation .…”
Section: Introductionmentioning
confidence: 99%