MFCA-MICNN:采用多尺度快速通道关注和多分支不规则卷积的卷积神经网络,用于 dMRI 中的噪声去除。

IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Physics in medicine and biology Pub Date : 2024-10-16 DOI:10.1088/1361-6560/ad8294
Lingmei Ai, Yunfan Shi, Ruoxia Yao, Liangfu Li
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引用次数: 0

摘要

扩散磁共振成像(dMRI)是目前量化脑组织微观结构和重建白质纤维通路的最重要的无创方法。然而,dMRI 中水分子固有的自由扩散运动会导致信号衰减,从而降低信噪比(SNR),并对微观结构数据的准确性和精确性产生不利影响。为了应对这一挑战,我们提出了一种用于 dMRI 图像去噪的新方法,即多尺度快速注意-多分支不规则卷积神经网络。在这项工作中,我们引入了多尺度快速通道注意力,这是一种高效多尺度特征提取的新方法,可跨特征通道计算注意力权重。这增强了模型捕捉复杂特征的能力,并提高了整体性能。此外,我们还提出了一种多分支不规则卷积架构,可有效破坏空间噪声相关性并捕捉噪声特征,从而进一步提高模型的去噪性能。最后,我们设计了一种新颖的损失函数,它能确保在边缘和平坦区域都有出色的表现。实验结果表明,在 dMRI 图像去噪方面,所提出的方法在定量和定性方面都优于其他最先进的深度学习去噪方法,而且参数更少,运行速度更快。
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MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI.

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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