DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

By Guang Yang, Simiao Yu, Hao Dong, G. Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Robert Arridge, J. Keegan, Yike Guo and 1 more. 2018

985 citations recorded by the source corpus.

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 utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist–Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

Subfields: Radiation, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiation

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Generative Adversarial Network in Medical Imaging: A Review (2018, methodology): “Besides ensuring image domain data ﬁdelity, frequency domain data ﬁdelity is also imposed when raw K-space data is available in MR reconstruction (Quan et al., 2018; Mardani et al., 2017; Yang et al., 2018a).”
MedGAN: Medical Image Translation using GANs (2018, methodology): “The eﬃciency of the prediction was ﬁrst improved by local descriptors learned through a supervised descriptor learning (SDL) algorithm (Zhong et al., 2016) and more recently through the combination of feature matching with learned non-linear local descriptors (Yang et al., 2018d).”
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, methodology): “Comparable approaches were used in the context of MRI reconstruction [73–78].”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, methodology): “han a physician manually converting the image. Adversarial U-nets have seen a wide spectrum of applications including uantitative susceptibility mappingq of the brain , detection of brain [79] tumors [80], [81] and breast cancer [82], segmentation of retinal vessels [38], segmentation of cardiac str”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, background): “…tasks, such as tumor segmentation [22, 23, 24, 25, 26, 27], brain lesion segmentation [28, 29, 30, 31, 32], MR image reconstruction [33, 34, 35, 36, 37, 38] or prediction of brain related diseases and their progression [39, 40, 41, 42, 43] full brain segmentation into more than 25 classes has - so…”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “A real-time method to reconstruct compressed sensed MRI using generative adversarial networks (GAN) has also been proposed.(99) In another approach,(100) in order to synthesize brain MRIs based on other MRI sequences in the same patient, convolutional encoders were built to generate a latent represe”
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training (2017, methodology): “GANs have also been used for segmentation [31], [32], detection [33], reconstruction [34], [35] and classification [36].”
Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk (2017, background): “These more sophisticated priors are recently emerging in empirical applications of many fields of imaging, such as medical imaging [19]–[32], microscopy [33], inpainting [34], [35], superresolution [36]–[38], compressed sensing [39]–[42], image manipulation [43], and many more.”
Distribution Matching Losses Can Hallucinate Features in Medical Image Translation (2018, methodology): “Applications include MR to CT [7, 8], CS-MRI [9, 10], CT to PET [11], and automatic H&E staining [12].”
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing (2020, methodology): “Recently, deep generative adversarial networks have been proposed for CS medical imaging in [51], [52], [53].”