论文标题
3D磁性粒子成像的深图像：开放MPI数据集上正则化技术的定量比较
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset
论文作者
论文摘要
磁性粒子成像（MPI）是一种成像模态，利用了（超）顺磁性纳米颗粒的非线性磁化行为，以获得由这些纳米颗粒组成的示踪剂的空间且通常还依赖于时间依赖的浓度。 MPI的潜在医疗应用数量不断增加。在这些应用程序中成功性能的一种先决条件是对图像重建问题的正确解决方案。来自反问题理论的更多经典方法以及机器学习领域的新方法具有在MPI中提供高质量重建的潜力。我们根据先验的深度图像研究了一种新颖的重建方法，该方法基于深度神经网络代表解决方案。新颖的方法以及变异和迭代正则化技术在公开可用的开放MPI数据集上的峰值信噪比和结构相似性指数进行了定量比较。
Magnetic particle imaging (MPI) is an imaging modality exploiting the nonlinear magnetization behavior of (super-)paramagnetic nanoparticles to obtain a space- and often also time-dependent concentration of a tracer consisting of these nanoparticles. MPI has a continuously increasing number of potential medical applications. One prerequisite for successful performance in these applications is a proper solution to the image reconstruction problem. More classical methods from inverse problems theory, as well as novel approaches from the field of machine learning, have the potential to deliver high-quality reconstructions in MPI. We investigate a novel reconstruction approach based on a deep image prior, which builds on representing the solution by a deep neural network. Novel approaches, as well as variational and iterative regularization techniques, are compared quantitatively in terms of peak signal-to-noise ratios and structural similarity indices on the publicly available Open MPI dataset.
