A Brief Review on: MRI Images Reconstruction using GAN

Shende, Priyanka; Pawar, Mahesh; Kakde, Sandeep

doi:10.1109/iccsp.2019.8698083

Cited by 18 publications

(6 citation statements)

References 18 publications

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“…Generative modeling is a machine learning task that uses unsupervised learning to identify patterns in input data. It has been compared to modern models like Pix2Pix, DECNN, LA-GANs, and MedGAN, demonstrating improved performance in brain MRIs [18].…”

Section: Generative Adversarial Network (Gan) For Super-resolution Of...mentioning

confidence: 99%

Resolution Enhancement of Brain MRI Images Using Deep Learning

Roy,

Upadhyaya,

Rai

et al. 2024

RAiSE-2023

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Section: Generative Adversarial Network (Gan) For Super-resolution Of...mentioning

confidence: 99%

Resolution Enhancement of Brain MRI Images Using Deep Learning

Roy,

Upadhyaya,

Rai

et al. 2024

RAiSE-2023

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“…Convolutional neural networks based on deep learning extract prior knowledge from training data and combine the concept of sparse regularization for MRI reconstruction. Prominent instances included variational autoencoders [8], generative adversarial networks (GANs) [9][10][11][12][13][14], and various other generative models [15,16]. Iterative models constituted another category, where many deep learning methods embraced iterative updates for MRI reconstruction.…”

Section: Related Workmentioning

confidence: 99%

Progressive Feature Reconstruction and Fusion to Accelerate MRI Imaging: Exploring Insights across Low, Mid, and High-Order Dimensions

Wang,

Lian,

Xiong

et al. 2023

Electronics

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Magnetic resonance imaging (MRI) faces ongoing challenges associated with prolonged acquisition times and susceptibility to motion artifacts. Compressed Sensing (CS) principles have emerged as a significant advancement, addressing these issues by subsampling k-space data points and enabling rapid imaging. Nevertheless, the recovery of intricate details from under-sampled data remains a complex endeavor. In this study, we introduce an innovative deep learning approach tailored to the restoration of high-fidelity MRI images from under-sampled k-space data. Our method employs a cascaded reconstruction strategy that progressively restores hierarchical features and fuses them to achieve the final reconstruction. This cascade encompasses low, intermediate, and high orders of reconstruction, which is followed by a return through intermediate and low orders. At distinct reconstruction stages, we introduce a novel reconstruction block to recapture diverse frequency information crucial for image reconstruction. The other core innovation of our proposal lies in a fusion algorithm that harmonizes results from various reconstruction tiers into the final MRI image. Our methodology is validated using two distinct datasets. Notably, our algorithm achieves impressive PSNR values of 32.60 and 31.02 at acceleration factors of 4× and 8× in the FastMRI dataset along with SSIM scores of 0.818 and 0.771, outperforming current state-of-the-art algorithms. Similarly, on the Calgary–Campinas dataset, our algorithm achieves even higher PSNR values, reaching 37.68 and 33.44, which is accompanied by substantial SSIM scores of 0.954 and 0.901. It is essential to highlight that our algorithm achieves these remarkable results with a relatively lower parameter count, underscoring its efficiency. Comparative analyses against analogous methods further emphasize the superior performance of our approach, providing robust evidence of its effectiveness.

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“…The two networks are continuously trained, and finally the discriminator cannot distinguish the generated Image and real image. After continuous development of GAN, different types of versions have been derived [50], such as Vanilla GAN, CGAN, DCGAN, GRAN, LAPGAN, WGAN, etc. Research [51] used GAN for the reconstruction of MRI images.…”

Section: ) Generative Adversarial Network (Gan)mentioning

confidence: 99%