Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data

Wang, Shanshan; Xiao, Taohui; Liu, Qiegen; Zheng, Hairong

doi:10.1016/j.bspc.2021.102579

Cited by 54 publications

(38 citation statements)

References 131 publications

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“…Representative machine learning algorithms include compressed-sensing methods and DL-based methods. 119,120,130 MR image super-resolution involves generating high-resolution MR images from low-resolution images. 131,132 Superresolution is similar to reconstruction in the sense that both attempt to generate better MR images.…”

Section: Different Types Of Multiparametric Mri-related Tasks Address...mentioning

confidence: 99%

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Liu

et al. 2022

Medical Physics

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Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availabilities of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting.However,the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI.

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Section: Different Types Of Multiparametric Mri-related Tasks Address...mentioning

confidence: 99%

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Liu

et al. 2022

Medical Physics

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“…Based on the reliance on the fully sampled dataset or not, existing methods for dynamic MR imaging can be roughly classified into two types [ 8 , 9 , 10 ]: fully-supervised methods and unsupervised methods. For the fully-supervised methods, data pairs are needed for the training of the neural networks between the corrupted/ undersampled data and the ground truth/fully sampled data [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

SelfCoLearn: Self-Supervised Collaborative Learning for Accelerating Dynamic MR Imaging

Zou

Jia

et al. 2022

Bioengineering

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Lately, deep learning technology has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, the current approaches may have limited abilities in recovering fine details or structures. To address this challenge, this paper proposes a self-supervised collaborative learning framework (SelfCoLearn) for accurate dynamic MR image reconstruction from undersampled k-space data directly. The proposed SelfCoLearn is equipped with three important components, namely, dual-network collaborative learning, reunderampling data augmentation and a special-designed co-training loss. The framework is flexible and can be integrated into various model-based iterative un-rolled networks. The proposed method has been evaluated on an in vivo dataset and was compared to four state-of-the-art methods. The results show that the proposed method possesses strong capabilities in capturing essential and inherent representations for direct reconstructions from the undersampled k-space data and thus enables high-quality and fast dynamic MR imaging.

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“…There are many recent works concerning IR problems [10,11], such as positron microscopy [12], lensless imaging [13], MR imaging [14,15], low-dose CT [16], photoacoustic [17,18] and ultrasound [19], and compressed sensing [20].…”

Section: Introductionmentioning

confidence: 99%

Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network

et al. 2022

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A new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First, the image was transformed using the DWT to provide multi-resolution wavelet analysis. Then, the multiscale maxima points were treated as interest points and extracted in the DWT domain. The generator was a U-net structure to reconstruct the original image from a very coarse version of the image obtained from the inverse DWT of the interest points. The discriminator network was a fully convolutional network, which was used to distinguish the restored image from the real one. The experimental results on three public datasets showed that the proposed IR-DWT-CGAN model had an average increase of 2.9% in the mean structural similarity, an average decrease of 39.6% in the relative dimensionless global error in synthesis, and an average decrease of 48% in the root-mean-square error compared with several other state-of-the-art methods. Therefore, the proposed IR-DWT-CGAN model is feasible and effective for image reconstruction with multiscale interest points.

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Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data

Cited by 54 publications

References 131 publications

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Artificial intelligence in multiparametric magnetic resonance imaging: A review

SelfCoLearn: Self-Supervised Collaborative Learning for Accelerating Dynamic MR Imaging

Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network

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