面向磁粒子成像的学习型差异重建与基准数据集
Learned Discrepancy Reconstruction and Benchmark Dataset for Magnetic Particle Imaging
AI总结：
针对MPI噪声不符合高斯假设的问题，提出含可逆神经网络的学习型差异重建方法，并构建MPI-MNIST基准，显著提升结构相似性。
摘要：
磁粒子成像（MPI）是一种新兴成像模态，它基于超顺磁性氧化铁纳米颗粒的磁响应，实现高分辨率、实时成像且不产生有害辐射。MPI图像重建任务中的一个关键挑战源于其底层噪声模型，该模型并不满足应用传统重建方法时所隐含的高斯假设。为应对这一挑战，我们提出了Learned Discrepancy Approach，这是一种面向反问题的新型基于学习的重建方法，其中包含一个学习型差异函数。该方法通过引入可逆神经网络来显式建模特定问题的噪声分布，从而增强传统技术。它不依赖隐含的高斯噪声假设，因此特别适合处理MPI中复杂的噪声模型，同时也适用于其他反问题。为进一步推动MPI重建技术，我们推出了MPI-MNIST数据集——这是由手写数字MNIST数据集衍生出的大规模模拟MPI测量集合。该数据集包含由最先进的基于模型的系统矩阵生成的受噪声扰动测量，以及来自一台临床前MPI扫描仪设备的测量，从而为算法测试提供真实且灵活的环境。在MPI-MNIST数据集上的验证表明，与经典重建技术相比，我们的方法在结构相似性方面显著提升了重建质量。
英文摘要：
Magnetic Particle Imaging (MPI) is an emerging imaging modality based on the magnetic response of superparamagnetic iron oxide nanoparticles to achieve high-resolution and real-time imaging without harmful radiation. One key challenge in the MPI image reconstruction task arises from its underlying noise model, which does not fulfill the implicit Gaussian assumptions that are made when applying traditional reconstruction approaches. To address this challenge, we introduce the Learned Discrepancy Approach, a novel learning-based reconstruction method for inverse problems that includes a learned discrepancy function. It enhances traditional techniques by incorporating an invertible neural network to explicitly model problem-specific noise distributions. This approach does not rely on implicit Gaussian noise assumptions, making it especially suited to handle the sophisticated noise model in MPI and also applicable to other inverse problems. To further advance MPI reconstruction techniques, we introduce the MPI-MNIST dataset - a large collection of simulated MPI measurements derived from the MNIST dataset of handwritten digits. The dataset includes noise-perturbed measurements generated from state-of-the-art model-based system matrices and measurements of a preclinical MPI scanner device. This provides a realistic and flexible environment for algorithm testing. Validated against the MPI-MNIST dataset, our method demonstrates significant improvements in reconstruction quality in terms of structural similarity when compared to classical reconstruction techniques.
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