论文标题
磁性粒子成像中可靠重建的L1数据拟合：开放MPI数据集的定量评估
L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset
论文作者
论文摘要
磁性粒子成像是一种新兴的定量成像方式，利用超顺磁铁氧化铁纳米颗粒的独特非线性磁化现象来恢复浓度。传统上，重建是通过非神经性约束的惩罚最小二乘问题提出的，然后使用kaczmarz方法的变体解决，该方法通常在少量迭代后早点停止。除了幻影信号外，测量还包括背景信号和噪声信号。为了获得良好的重建，通常会采用频率选择的预处理步骤，以消除噪声的有害影响。在这项工作中，我们通过将高度嘈杂的测量值视为离群值，并采用L1数据拟合，从可靠的统计数据中提出了一种互补的纯变量方法来进行噪声处理。与标准方法相比，可以易于使用可比的计算复杂性实现。使用公共域数据集（即打开的MPI数据集）进行的实验表明，它可以提供准确的重建，并且不太容易容易进行嘈杂的测量，这是通过定量（PSNR / SSIM）和与Kaczmarz方法的定性比较来说明的。我们还研究了kaczmarz方法的性能，以定量地进行小型迭代数字。
Magnetic particle imaging is an emerging quantitative imaging modality, exploiting the unique nonlinear magnetization phenomenon of superparamagnetic iron oxide nanoparticles for recovering the concentration. Traditionally the reconstruction is formulated into a penalized least-squares problem with nonnegativity constraint, and then solved using a variant of Kaczmarz method which is often stopped early after a small number of iterations. Besides the phantom signal, measurements additionally include a background signal and a noise signal. In order to obtain good reconstructions, a preprocessing step of frequency selection to remove the deleterious influences of the noise is often adopted. In this work, we propose a complementary pure variational approach to noise treatment, by viewing highly noisy measurements as outliers, and employing the l1 data fitting, one popular approach from robust statistics. When compared with the standard approach, it is easy to implement with a comparable computational complexity. Experiments with a public domain dataset, i.e., Open MPI dataset, show that it can give accurate reconstructions, and is less prone to noisy measurements, which is illustrated by quantitative (PSNR / SSIM) and qualitative comparisons with the Kaczmarz method. We also investigate the performance of the Kaczmarz method for small iteration numbers quantitatively.
