CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China; School of Artificial Intelligence, The University of Chinese Academy of Sciences, Beijing, China.
School of Engineering Medicine, Beihang University, Beijing, China; The Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, China.
Comput Biol Med. 2024 Aug;178:108783. doi: 10.1016/j.compbiomed.2024.108783. Epub 2024 Jun 22.
Magnetic particle imaging (MPI) is an emerging non-invasive medical imaging tomography technology based on magnetic particles, with excellent imaging depth penetration, high sensitivity and contrast. Spatial resolution and signal-to-noise ratio (SNR) are key performance metrics for evaluating MPI, which are directly influenced by the gradient of the selection field (SF). Increasing the SF gradient can improve the spatial resolution of MPI, but will lead to a decrease in SNR. Deep learning (DL) methods may enable obtaining high-resolution images from low-resolution images to improve the MPI resolution under low gradient conditions. However, existing DL methods overlook the physical procedures contributing to the blurring of MPI images, resulting in low interpretability and hindering breakthroughs in resolution. To address this issue, we propose a dual-channel end-to-end network with prior knowledge embedding for MPI (DENPK-MPI) to effectively establish a latent mapping between low-gradient and high-gradient images, thus improving MPI resolution without compromising SNR. By seamlessly integrating MPI PSF with DL paradigm, DENPK-MPI leads to a significant improvement in spatial resolution performance. Simulation, phantom, and in vivo MPI experiments have collectively confirmed that our method can improve the resolution of low-gradient MPI images without sacrificing SNR, resulting in a decrease in full width at half maximum by 14.8%-23.8 %, and the accuracy of image reconstruction is 18.2 %-27.3 % higher than other DL methods. In conclusion, we propose a DL method that incorporates MPI prior knowledge, which can improve the spatial resolution of MPI without compromising SNR and possess improved biomedical application.
基于磁颗粒的磁共振成像（MPI）是一种新兴的非侵入性医学成像层析技术，具有优异的成像深度穿透性、高灵敏度和对比度。空间分辨率和信噪比（SNR）是评估 MPI 的关键性能指标，它们直接受到选择场（SF）梯度的影响。增加 SF 梯度可以提高 MPI 的空间分辨率，但会导致 SNR 降低。深度学习（DL）方法可以从低分辨率图像中获取高分辨率图像，从而在低梯度条件下提高 MPI 的分辨率。然而，现有的 DL 方法忽略了导致 MPI 图像模糊的物理过程，导致低可解释性，阻碍了分辨率的突破。为了解决这个问题，我们提出了一种带有先验知识嵌入的 MPI 端到端双通道网络（DENPK-MPI），以有效地在低梯度和高梯度图像之间建立潜在映射，从而在不牺牲 SNR 的情况下提高 MPI 分辨率。通过无缝集成 MPI PSF 与 DL 范例，DENPK-MPI 显著提高了空间分辨率性能。仿真、体模和体内 MPI 实验均证实，我们的方法可以在不牺牲 SNR 的情况下提高低梯度 MPI 图像的分辨率，使全宽半最大值降低 14.8%-23.8%，图像重建的准确性比其他 DL 方法提高 18.2%-27.3%。总之，我们提出了一种包含 MPI 先验知识的 DL 方法，该方法可以在不牺牲 SNR 的情况下提高 MPI 的空间分辨率，具有改进的生物医学应用。
