Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks

Deora, Puneesh; Vasudeva, Bhavya; Bhattacharya, Sumanta; Pradhan, Pyari Mohan

doi:10.1109/cvprw50498.2020.00269

Cited by 29 publications

(11 citation statements)

References 28 publications

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“…Later, in [108], MSE loss of k-space is added. The authors of [109] proposed to utilize dense connection structure in the bottleneck part of U-Net. The authors of [110] proposed to use adversarial loss function in LSGAN [111].…”

Section: Non-parallel Imagingmentioning

confidence: 99%

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

Xie

2022

Electronics

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Compressed sensing (CS) and its medical applications are active areas of research. In this paper, we review recent works using deep learning method to solve CS problem for images or medical imaging reconstruction including computed tomography (CT), magnetic resonance imaging (MRI) and positron-emission tomography (PET). We propose a novel framework to unify traditional iterative algorithms and deep learning approaches. In short, we define two projection operators toward image prior and data consistency, respectively, and any reconstruction algorithm can be decomposed to the two parts. Though deep learning methods can be divided into several categories, they all satisfies the framework. We built the relationship between different reconstruction methods of deep learning, and connect them to traditional methods through the proposed framework. It also indicates that the key to solve CS problem and its medical applications is how to depict the image prior. Based on the framework, we analyze the current deep learning methods and point out some important directions of research in the future.

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Section: Non-parallel Imagingmentioning

confidence: 99%

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

Xie

2022

Electronics

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“…Deora et al [ 53 ] developed a new generative adversarial network (GAN) framework for reconstructing CS-MRI. It improves the quality by combining the patch-based GAN discriminator and the structural similarity index loss.…”

Section: Soft Computingmentioning

confidence: 99%

A Survey of Soft Computing Approaches in Biomedical Imaging

Devi

Singh

Tiwari

et al. 2021

Journal of Healthcare Engineering

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Medical imaging is an essential technique for the diagnosis and treatment of diseases in modern clinics. Soft computing plays a major role in the recent advances in medical imaging. It handles uncertainties and improves the qualities of an image. Until now, various soft computing approaches have been proposed for medical applications. This paper discusses various medical imaging modalities and presents a short review of soft computing approaches such as fuzzy logic, artificial neural network, genetic algorithm, machine learning, and deep learning. We also studied and compared each approach used for other imaging modalities based on the certain parameter used for the system evaluation. Finally, based on comparative analysis, the possible research strategies for further development are proposed. As far as we know, no previous work examined this issue.

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“…Murugesan et al [96] and Emami et al [97] used context dependent/attention-guided GAN which has a feedback loop back to the generator input providing information focusing on local deviations from tissue. Mardani et al [98] and Deora et al [99] used residual skip connections inside each convolutional block of their generator. It is noteworthy that Mardani suggests the discriminator outputs can be used to focus on sensitive anatomies.…”

Section: Introductionmentioning

confidence: 99%

Improving Signal to Noise Ratio in Ultra High Field Magnetic Resonance Imaging

Tavaf¹

2021

Preprint

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Ultra-High Field (UHF) Magnetic Resonance Imaging (MRI) advantages, including higher image resolution, reduced acquisition time via parallel imaging, and better signal-to-noise ratio (SNR) have opened new opportunities for various clinical and research projects, including functional MRI, brain connectivity mapping, and anatomical imaging. The advancement of these UHF MRI performance metrics, especially SNR, was the primary motivation of this thesis. Unaccelerated SNR depends on receive array sensitivity profile, receiver noise correlation and static magnetic field strength. Various receive array decoupling technologies, including overlap/inductive and preamplifier decoupling, were previously utilized to mitigate noise correlation. In this dissertation, I developed a novel self-decoupling principle to isolate elements of a loop-based receive array and demonstrated, via full-wave electromagnetic/circuit co-simulations validated by bench measurements, that the self-decoupling technique provides inter-element isolation on par with overlap decoupling while self-decoupling improves SNR. I then designed and constructed the first self-decoupled 32 and 64 channel receiver arrays for human brain MR imaging at 10.5T / 447MHz. Experimental comparisons of these receive arrays with the industry’s gold-standard 7T 32 channel receiver resulted in 1.81 times and 3.53 times more average SNR using the 10.5T 32 and 64 channel receivers I built, respectively. To further improve the SNR of accelerated MR images, I developed a novel data-driven model using a customized conditional generative adversarial network (GAN) architecture for parallel MR image reconstruction and demonstrated that, when applied to human brain images subsampled with rate of 4, the GAN model results in a peak signal-to-noise ratio (PSNR) of 37.65 compared to GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA)’s PSNR of 33.88.In summary, the works presented in this dissertation improved the SNR available for human brain imaging and provided the experimental realization of the advantages anticipated at 10.5T MRI. The insights from this thesis inform future efforts to build self-decoupled transmit arrays and high density, 128 channel loop-based receive arrays for human brain MRI especially at ultra-high field as well as future studies to utilize deep learning techniques for reconstruction and post-processing of parallel MR images.

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Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks

Cited by 29 publications

References 28 publications

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

A Survey of Soft Computing Approaches in Biomedical Imaging

Improving Signal to Noise Ratio in Ultra High Field Magnetic Resonance Imaging

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