An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation

doi:10.1109/JBHI.2022.3223106

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	An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation
	Qing Wu1,2 ; Yuwei Li1 ; Yawen Sun 3; Yan Zhou 3; Hongjiang Wei 4; Jingyi Yu1 ; Yuyao Zhang5
	2023-02-01
发表期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (IF:6.7[JCR-2023],7.1[5-Year])
ISSN	2168-2208
卷号	27 期号:2 页码:1-12
发表状态	已发表
DOI	10.1109/JBHI.2022.3223106
摘要	High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3-dimensional (3D) HR image acquisition typically requests long scan time and, results in small spatial coverage and low signal-to-noise ratio (SNR). Recent studies showed that, with deep convolutional neural networks, isotropic HR MR images could be recovered from low-resolution (LR) input via single image super-resolution (SISR) algorithms. However, most existing SISR methods tend to approach scale-specific projection between LR and HR images, thus these methods can only deal with fixed up-sampling rates. In this paper, we propose ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D HR MR images. In the ArSSR model, the LR image and the HR image are represented using the same implicit neural voxel function with different sampling rates. Due to the continuity of the learned implicit function, a single ArSSR model is able to achieve arbitrary and infinite up-sampling rate reconstructions of HR images from any input LR image. Then the SR task is converted to approach the implicit voxel function via deep neural networks from a set of paired HR and LR training examples. The ArSSR model consists of an encoder network and a decoder network. Specifically, the convolutional encoder network is to extract feature maps from the LR input images and the fully-connected decoder network is to approximate the implicit voxel function. Experimental results on three datasets show that the ArSSR model can achieve state-of-the-art SR performance for 3D HR MR image reconstruction while using a single trained model to achieve arbitrary up-sampling scales.
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收录类别	EI ; SCOPUS ; SCI
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/256356
专题	iHuman研究所信息科学与技术学院信息科学与技术学院_PI研究组_虞晶怡组信息科学与技术学院_博士生信息科学与技术学院_PI研究组_张玉瑶组生物医学工程学院
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.Shanghai Advanced Research Institute, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Shanghai, China 3.Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China 4.School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China 5.School of Information Science and Technology and iHuman Institute, ShanghaiTech University, Shanghai, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Qing Wu,Yuwei Li,Yawen Sun,et al. An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2023,27(2):1-12.
APA	Qing Wu.,Yuwei Li.,Yawen Sun.,Yan Zhou.,Hongjiang Wei.,...&Yuyao Zhang.(2023).An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,27(2),1-12.
MLA	Qing Wu,et al."An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 27.2(2023):1-12.