基于注意力的 q 空间深度学习泛化用于加速扩散磁共振成像。

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-10-29 DOI:10.1109/JBHI.2024.3487755
Fangrong Zong, Zaimin Zhu, Jiayi Zhang, Xiaofeng Deng, Zhuangzhuang Li, Chuyang Ye, Yong Liu
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引用次数: 0

摘要

扩散磁共振成像(dMRI)是一种无创方法,通过测量沿多个方向的扩散加权信号来捕捉组织的微观解剖信息,广泛应用于微观结构的量化。获取微观参数需要在 q 空间进行密集采样,因此耗费大量时间。加速 dMRI 采集的最流行方法是对 q 空间数据进行欠采样,同时应用深度学习方法重建定量扩散参数。然而,对预定 q 空间采样策略的依赖往往限制了传统的基于深度学习的重建。本研究提出了一种新型深度学习模型,命名为基于注意力的q空间深度学习(aqDL),以实现可变q空间采样策略的重建。aqDL 通过使用一系列变换器编码器,将不同扫描策略的 dMRI 数据映射到一个共同的特征空间。潜特征通过多层感知器用于重建 dMRI 参数。aqDL 模型的性能是利用人类连接组计划数据集在不同的低采样率下进行评估的。为了验证其通用性,该模型还在另外两个独立数据集上进行了进一步测试。我们的结果表明,无论采用可变还是预定的 q 空间扫描策略,aqDL 都能在不同的取样不足数下始终获得最高的重建准确率。这些研究结果表明,aqDL 有潜力用于一般临床 dMRI 数据集。
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Attention-based q-space Deep Learning Generalized for Accelerated Diffusion Magnetic Resonance Imaging.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method for capturing the microanatomical information of tissues by measuring the diffusion weighted signals along multiple directions, which is widely used in the quantification of microstructures. Obtaining microscopic parameters requires dense sampling in the q space, leading to significant time consumption. The most popular approach to accelerating dMRI acquisition is to undersample the q-space data, along with applying deep learning methods to reconstruct quantitative diffusion parameters. However, the reliance on a predetermined q-space sampling strategy often constrains traditional deep learning-based reconstructions. The present study proposed a novel deep learning model, named attention-based q-space deep learning (aqDL), to implement the reconstruction with variable q-space sampling strategies. The aqDL maps dMRI data from different scanning strategies onto a common feature space by using a series of Transformer encoders. The latent features are employed to reconstruct dMRI parameters via a multilayer perceptron. The performance of the aqDL model was assessed utilizing the Human Connectome Project datasets at varying undersampling numbers. To validate its generalizability, the model was further tested on two additional independent datasets. Our results showed that aqDL consistently achieves the highest reconstruction accuracy at various undersampling numbers, regardless of whether variable or predetermined q-space scanning strategies are employed. These findings suggest that aqDL has the potential to be used on general clinical dMRI datasets.

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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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