A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising
This work addresses the challenge of achieving high image quality in dynamic PET, which is crucial for medical imaging applications, though it appears incremental as it builds on existing model-based and deep learning approaches.
The paper tackled dynamic PET image denoising by proposing a kernel space-based multidimensional sparse model integrated with neural networks, resulting in strong denoising performance that outperformed baseline methods on simulated and real data.
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.