Low-Dose CT Reconstruction Via Optimization-Inspired GAN

Jiang, Jian; Feng, Yuchao; Xu, Honghui; Zheng, Jianwei

doi:10.1109/icassp49357.2023.10095096

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…Furthermore, MGDUN (Yang et al 2022) contributes significantly to the field of image super-resolution, which introduces an endto-end trainable model that harnesses the power of multi-ple contrasts. Additionally, PLA-GAN (Jiang et al 2023) presents a pioneering ADMM technique for CT denoising, which merges the flexibility of model-based methodologies with the benefits of generative adversarial networks.…”

Section: Deep Unfolding Networkmentioning

confidence: 99%

Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction

Chen,

Jiang,

et al. 2024

AAAI

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Consistency and interpretability have long been the critical issues in MRI reconstruction. While interpretability has been dramatically improved with the employment of deep unfolding networks (DUNs), current methods still suffer from inconsistencies and generate inferior anatomical structure. Especially in multi-contrast scenes, different imaging protocols often exacerbate the concerned issue. In this paper, we propose a range-null decomposition-assisted DUN architecture to ensure consistency while still providing desirable interpretability. Given the input decomposed, we argue that the inconsistency could be analytically relieved by feeding solely the null-space component into proximal mapping, while leaving the range-space counterpart fixed. More importantly, a correlation decoupling scheme is further proposed to narrow the information gap for multi-contrast fusion, which dynamically borrows isotropic features from the opponent while maintaining the modality-specific ones. Specifically, the two features are attached to different frequencies and learned individually by the newly designed isotropy encoder and anisotropy encoder. The former strives for the contrast-shared information, while the latter serves to capture the contrast-specific features. The quantitative and qualitative results show that our proposal outperforms most cutting-edge methods by a large margin. Codes will be released on https://github.com/chenjiachengzzz/RNU.

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Section: Deep Unfolding Networkmentioning

confidence: 99%

Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction

Chen,

Jiang,

et al. 2024

AAAI

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A review of deep learning and Generative Adversarial Networks applications in medical image analysis

Sindhura,

Pai,

Bhat

et al. 2024

Multimedia Systems

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Nowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, identification of patterns, detection, etc. Deep learning models learn feature representations from images rather than handcrafted features. Hence, deep learning models are quickly becoming the state-of-the-art method to achieve good performances in different computer-aided decision-support systems in medical applications. Similarly, deep learning-based generative models called Generative Adversarial Networks (GANs) have recently been developed as a novel method to produce realistic-looking synthetic data. GANs are used in different domains, including medical imaging generation. The common problems, like class imbalance and a small dataset, in healthcare are well addressed by GANs, and it is a leading area of research. Segmentation, reconstruction, detection, denoising, registration, etc. are the important applications of GANs. So in this work, the successes of deep learning methods in segmentation, classification, cell structure and fracture detection, computer-aided identification, and GANs in synthetic medical image generation, segmentation, reconstruction, detection, denoising, and registration in recent times are reviewed. Lately, the review article concludes by raising research directions for DL models and GANs in medical applications.

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Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications, and Challenges

Islam,

Aziz,

Nabil

et al. 2024

IEEE Access

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Generative Adversarial Networks are a class of artificial intelligence algorithms that consist of a generator and a discriminator trained simultaneously through adversarial training. GANs have found crucial applications in various fields, including medical imaging. In healthcare, GANs contribute by generating synthetic medical images, enhancing data quality, and aiding in image segmentation, disease detection, and medical image synthesis. Their importance lies in their ability to generate realistic images, facilitating improved diagnostics, research, and training for medical professionals. Understanding its applications, algorithms, current advancements, and challenges is imperative for further advancement in the medical imaging domain. However, no study explores the recent state-of-the-art development of GANs in medical imaging. To overcome this research gap, in this extensive study, we began by exploring the vast array of applications of GANs in medical imaging, scrutinizing them within recent research. We then dive into the prevalent datasets and pre-processing techniques to enhance comprehension. Subsequently, an in-depth discussion of the GAN algorithms, elucidating their respective strengths and limitations, is provided. After that, we meticulously analyzed the results and experimental details of some recent cutting-edge research to obtain a more comprehensive understanding of the current development of GANs in medical imaging. Lastly, we discussed the diverse challenges encountered and future research directions to mitigate these concerns. This systematic review offers a complete overview of GANs in medical imaging, encompassing their application domains, models, state-of-the-art results analysis, challenges, and research directions, serving as a valuable resource for multidisciplinary studies.

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Low-Dose CT Reconstruction Via Optimization-Inspired GAN

Cited by 6 publications

References 19 publications

Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction

Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction

A review of deep learning and Generative Adversarial Networks applications in medical image analysis

Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications, and Challenges

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