用神经网络优化动态PET去噪模型，提升图像质量。
A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising
- 基于帧间相关性与帧内结构一致性构建稀疏模型
- 在模拟与真实数据上优于已有方法，显著降低噪声
- 适合需要高时空分辨率的动态PET研究者使用
动态正电子发射断层扫描（dynamic PET）中，由于短时间帧的统计量有限，实现高质量时序图像极具挑战。近年来深度学习在医学图像去噪任务中展现出潜力。本文提出一种模型驱动的神经网络用于动态PET去噪。利用帧间的空间相关性和帧内的结构一致性，建立基于核空间的多维稀疏（KMDS）模型，并用神经网络替代传统参数估计形式，实现自适应参数优化，形成端到端的神经KMDS-Net。在模拟和真实数据上的大量实验表明，该方法在动态PET去噪方面表现优异，优于多个基线方法。所提方法可有效提升动态PET的时空分辨率。源代码已公开于 https://github.com/Kuangxd/Neural-KMDS-Net/tree/main。
原文摘要 · Abstract (English)
Achieving high image quality for temporal frames in dynamic positron emission tomography (PET) is challenging due to the limited statistic especially for the short frames. Recent studies have shown that deep learning (DL) is useful in a wide range of medical image denoising tasks. In this paper, we propose a model-based neural network for dynamic PET image denoising. The inter-frame spatial correlation and intra-frame structural consistency in dynamic PET are used to establish the kernel space-based multidimensional sparse (KMDS) model. We then substitute the inherent forms of the parameter estimation with neural networks to enable adaptive parameters optimization, forming the end-to-end neural KMDS-Net. Extensive experimental results from simulated and real data demonstrate that the neural KMDS-Net exhibits strong denoising performance for dynamic PET, outperforming previous baseline methods. The proposed method may be used to effectively achieve high temporal and spatial resolution for dynamic PET. Our source code is available at https://github.com/Kuangxd/Neural-KMDS-Net/tree/main.
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