Edge Preserving and Noise Reducing Reconstruction for Magnetic Particle Imaging
Edge Preserving and Noise Reducing Reconstruction for Magnetic Particle Imaging
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DOI：
10.1109/tmi.2016.2593954
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发表时间：
2017
影响因子：
10.6
通讯作者：
M. Storath;C. Brandt;M. Hofmann;T. Knopp;J. Salamon;A. Weber;A. Weinmann
中科院分区：
文献类型：
--
作者：
M. Storath;C. Brandt;M. Hofmann;T. Knopp;J. Salamon;A. Weber;A. Weinmann
磁性粒子成像（MPI）是一种新兴的医学成像模式，其基于磁性纳米粒子对施加的磁场的非线性响应。MPI的一个重要功能是可以为3D体积捕获快速动态过程。高时间分辨率又导致必须有效处理的大量数据。但由于MPI的系统矩阵是非稀疏的，图像重建的计算要求很高。因此，目前仅使用基本的图像重建方法，例如Tikhonov正则化。然而，已知Tikhonov正则化过度平滑重构图像中的边缘，并且仅具有有限的降噪效果。在这项工作中，我们开发了一个有效的边缘保持和降低噪声的MPI重建方法。作为正则化模型，我们建议使用非负融合套索模型，我们设计了一个离散化，适合在这项工作中考虑的临床前MPI扫描仪的采集几何形状。我们开发了一个定制的求解器的基础上，广义的向前向后的计划，这是特别适合于在MPI的密集和结构不好的系统矩阵。已经有一个非优化的原型实现在几秒钟内处理3D体积，以便每秒处理几帧似乎是可行的。我们证明了重建质量的改善，在一个实验性的医疗设置在狭窄的体外血管成形术的国家的最先进的方法。
Magnetic particle imaging (MPI) is an emerging medical imaging modality which is based on the non-linear response of magnetic nanoparticles to an applied magnetic field. It is an important feature of MPI that even fast dynamic processes can be captured for 3D volumes. The high temporal resolution in turn leads to large amounts of data which have to be handled efficiently. But as the system matrix of MPI is non-sparse, the image reconstruction gets computationally demanding. Therefore, currently only basic image reconstruction methods such as Tikhonov regularization are used. However, Tikhonov regularization is known to oversmooth edges in the reconstructed image and to have only a limited noise reducing effect. In this work, we develop an efficient edge preserving and noise reducing reconstruction method for MPI. As regularization model, we propose to use the nonnegative fused lasso model, and we devise a discretization that is adapted to the acquisition geometry of the preclinical MPI scanner considered in this work. We develop a customized solver based on a generalized forward-backward scheme which is particularly suitable for the dense and not well-structured system matrices in MPI. Already a non-optimized prototype implementation processes a 3D volume within a few seconds so that processing several frames per second seems amenable. We demonstrate the improvement in reconstruction quality over the state-of-the-art method in an experimental medical setup for an in-vitro angioplasty of a stenosis.