Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction

Lv, Jun; Li, Guangyuan; Tong, Xiangrong; Chen, Weibo; Huang, Jiahao; Wang, Chengyan; Yang, Guang

doi:10.1016/j.compbiomed.2021.104504

Cited by 47 publications

(20 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For future research, it is worth investigating the priori knowledge-driven learning strategies, as well as the other kind of cancers of CT images with a small size of labelled samples [ 27 – 29 ], feature-based approaches [ 30 ], and a transfer learning enhanced GAN [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples

Wang

et al. 2022

Oxidative Medicine and Cellular Longevity

View full text Add to dashboard Cite

The small size of labelled samples is one of the challenging problems in identifying early lung nodules from CT images using deep learning methods. Recent literature on the topic shows that deep convolutional generative adversarial network (DCGAN) has been used in medical data synthesis and gained some success, but does not demonstrate satisfactory results in synthesizing CT images. It primarily suffers from the problem of model convergence and is prone to mode collapse. In this paper, we propose a generative adversarial network (GAN) model with prior knowledge to generate CT images of early lung nodules from a small-size of labelled samples, i.e., SLS-PriGAN. Particularly, a knowledge acquisition network and a sharpening network are designed for priori knowledge learning and acquisition, and then, a GAN model is developed to produce CT images of early lung nodules. To validate our method, a general fast R-CNN network is trained using the CT images generated by SLS-PriGAN. The experiment result shows that it achieved a recognizing accuracy of 91%, a recall rate of 81%, and F 1 score of 0.85 in identifying clinic CT images of early lung nodules. This provides a promising way of identifying early lung nodules from CT images using deep learning with small-size labelled samples.

show abstract

Section: Discussionmentioning

confidence: 99%

An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples

Wang

et al. 2022

Oxidative Medicine and Cellular Longevity

View full text Add to dashboard Cite

show abstract

“…Also note that our current PUERT is restricted to the single coil CS-MRI reconstruction. However, accelerated parallel imaging [62], [81], [82] is remarkably promising to achieve higher degrees of acceleration. And we consider the combination of PUERT with multi-coil imaging as an important area of research.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

PUERT: Probabilistic Under-Sampling and Explicable Reconstruction Network for CS-MRI

Xie

Zhang

et al. 2022

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i.e., where to sample and how to reconstruct. To deal with both problems simultaneously, we propose a novel end-to-end Probabilistic Under-sampling and Explicable Reconstruction neTwork, dubbed PUERT, to jointly optimize the sampling pattern and the reconstruction network. Instead of learning a deterministic mask, the proposed sampling subnet explores an optimal probabilistic sub-sampling pattern, which describes independent Bernoulli random variables at each possible sampling point, thus retaining robustness and stochastics for a more reliable CS reconstruction. A dynamic gradient estimation strategy is further introduced to gradually approximate the binarization function in backward propagation, which efficiently preserves the gradient information and further improves the reconstruction quality. Moreover, in our reconstruction subnet, we adopt a model-based network design scheme with high efficiency and interpretability, which is shown to assist in further exploitation for the sampling subnet. Extensive experiments on two widely used MRI datasets demonstrate that our proposed PUERT not only achieves state-of-the-art results in terms of both quantitative metrics and visual quality but also yields a sub-sampling pattern and a reconstruction model that are both customized to training data. 1

show abstract

“…Augmentation of images from different patients is necessary to further improve the generalization performance. It is possible to validate the proposed method in other organs, e.g., abdomen [42], cardiac [43], knees [17], etc. It is also possible to apply the proposed augmentation method to unsupervised learning-based reconstructions when ground truth images are difficult to obtain.…”

Section: Discussionmentioning

confidence: 99%

MAGnitude-Image-to-Complex K-space (MAGIC-K) Net: A Data Augmentation Network for Image Reconstruction

et al. 2021

Self Cite

View full text Add to dashboard Cite

Deep learning has demonstrated superior performance in image reconstruction compared to most conventional iterative algorithms. However, their effectiveness and generalization capability are highly dependent on the sample size and diversity of the training data. Deep learning-based reconstruction requires multi-coil raw k-space data, which are not collected by routine scans. On the other hand, large amounts of magnitude images are readily available in hospitals. Hence, we proposed the MAGnitude Images to Complex K-space (MAGIC-K) Net to generate multi-coil k-space data from existing magnitude images and a limited number of required raw k-space data to facilitate the reconstruction. Compared to some basic data augmentation methods applying global intensity and displacement transformations to the source images, the MAGIC-K Net can generate more realistic intensity variations and displacements from pairs of anatomical Digital Imaging and Communications in Medicine (DICOM) images. The reconstruction performance was validated in 30 healthy volunteers and 6 patients with different types of tumors. The experimental results demonstrated that the high-resolution Diffusion Weighted Image (DWI) reconstruction benefited from the proposed augmentation method. The MAGIC-K Net enabled the deep learning network to reconstruct images with superior performance in both healthy and tumor patients, qualitatively and quantitatively.

show abstract

Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction

Cited by 47 publications

References 38 publications

An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples

An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples

PUERT: Probabilistic Under-Sampling and Explicable Reconstruction Network for CS-MRI

MAGnitude-Image-to-Complex K-space (MAGIC-K) Net: A Data Augmentation Network for Image Reconstruction

Contact Info

Product

Resources

About