基于扩散的任意尺度磁共振图像超分辨率渐进式k空间重构与去噪

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jiazhen Wang, Zhihao Shi, Xiang Gu, Yan Yang, Jian Sun
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引用次数: 0

摘要

由于硬件限制和采集时间等限制,获取高分辨率磁共振(MR)图像具有挑战性。超分辨率(SR)技术为在不改变磁共振成像(MRI)硬件的情况下提高MRI图像质量提供了一种潜在的解决方案。然而,典型的SR方法是为固定的上采样尺度而设计的,并且经常产生过度平滑的图像,缺乏精细的纹理和边缘细节。为了解决这些问题,我们提出了一个统一的基于扩散的框架,用于任意尺度平面内MR图像SR,称为渐进式重建和去噪扩散模型(PRDDiff)。具体来说,PRDDiff的前向扩散过程逐渐掩盖高频成分,并加入高斯噪声来模拟MRI中的下采样过程。为了扭转这一过程,我们提出了一种自适应分辨率恢复网络(ARRNet),该网络引入了一个对应于输入MR图像分辨率的当前步长和一个对应于目标分辨率的结束步长。该设计指导ARRNet从输入的MR图像中恢复目标分辨率的干净MR图像。SR过程从初始分辨率的MR图像开始,基于ARRNet恢复的MR图像,通过逐步重建高频成分并去除噪声,逐渐增强到更高的分辨率。此外,我们设计了一种多阶段SR策略,通过多个顺序阶段逐步提高分辨率,进一步提高恢复精度。每个阶段利用PRDDiff的一组采样步骤,在特定结束步骤的指导下,恢复与预定义的中间分辨率相关的细节。我们在fastMRI膝关节数据集、fastMRI脑部数据集、我们实际收集的LR-HR脑部数据集和临床小儿脑瘫(CP)数据集上进行了广泛的实验,包括脑部的t1加权和t2加权图像以及膝关节的质子密度加权图像。结果表明,PRDDiff在重建精度、泛化、下游病灶分割精度和CP分类性能方面优于以往的MR图像超分辨率方法。该代码可在https://github.com/Jiazhen-Wang/PRDDiff-main上公开获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diffusion-based arbitrary-scale magnetic resonance image super-resolution via progressive k-space reconstruction and denoising
Acquiring high-resolution Magnetic resonance (MR) images is challenging due to constraints such as hardware limitations and acquisition times. Super-resolution (SR) techniques offer a potential solution to enhance MR image quality without changing the magnetic resonance imaging (MRI) hardware. However, typical SR methods are designed for fixed upsampling scales and often produce over-smoothed images that lack fine textures and edge details. To address these issues, we propose a unified diffusion-based framework for arbitrary-scale in-plane MR image SR, dubbed Progressive Reconstruction and Denoising Diffusion Model (PRDDiff). Specifically, the forward diffusion process of PRDDiff gradually masks out high-frequency components and adds Gaussian noise to simulate the downsampling process in MRI. To reverse this process, we propose an Adaptive Resolution Restoration Network (ARRNet), which introduces a current step corresponding to the resolution of input MR image and an ending step corresponding to the target resolution. This design guide the ARRNet to recovering the clean MR image at the target resolution from input MR image. The SR process starts from an MR image at the initial resolution and gradually enhances them to higher resolution by progressively reconstructing high-frequency components and removing the noise based on the recovered MR image from ARRNet. Furthermore, we design a multi-stage SR strategy that incrementally enhances resolution through multiple sequential stages to further improve recovery accuracy. Each stage utilizes a set number of sampling steps from PRDDiff, guided by a specific ending step, to recover details pertinent to the predefined intermediate resolution. We conduct extensive experiments on fastMRI knee dataset, fastMRI brain dataset, our real-collected LR-HR brain dataset, and clinical pediatric cerebral palsy (CP) dataset, including T1-weighted and T2-weighted images for the brain and proton density-weighted images for the knee. The results demonstrate that PRDDiff outperforms previous MR image super-resolution methods in term of reconstruction accuracy, generalization, and downstream lesion segmentation accuracy and CP classification performance. The code is publicly available at https://github.com/Jiazhen-Wang/PRDDiff-main.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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