Rethinking the Fourier Transform: Frequency Split-Enhance Network for Fast System Matrix Calibration in Magnetic Particle Image

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Weixin Xu;Penghua Zhai;Zhongwei Bian;Yao Fu;Yukun Wu;Chaojuan Yang;Jie Tian;Wei Mu
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Abstract

Magnetic particle imaging (MPI) is an emerging molecular tomographic technique known for its high sensitivity and spatiotemporal resolution. Typically, high-quality images are obtained using the system matrix (SM)-based reconstruction method. Unlike other tomographic methods, SM calibration in MPI requires a time-consuming process to measure voxel-level responses across the MPI scanner’s field of view. Since the image resolution is directly affected by the size of the SM, the need for full-size SM calibration presents challenges for practical applications. This issue is further compounded by the necessity for repeated recalibration when changes occur in the tracer’s characteristics or the magnetic field environment. Consequently, efficient and rapid SM calibration is crucial. Existing calibration approaches often assume that each voxel in the SM is independent, overlooking the intrinsic relationships between voxels and their frequency-domain sparsity. To address this, we propose a novel framework dubbed frequency split-enhance network (FSE-Net), wherein the Fourier transform driven feature modulation block, the frequency split-enhance module (FSEM), is introduced to simultaneously split and enhance high- and low frequency features in distinct ways. By effectively capturing and utilizing frequency-domain features from a low-resolution (LR) SM obtained through fast sparse sampling, FSE-Net bridges the gap between LR and high-resolution (HR) volumetric images, achieving HR images with accurate shapes and refined textures. Extensive experiments on widely used OpenMPI public benchmark and simulation datasets demonstrate that our FSE-Net outperforms existing methods, achieving state-of-the-art performance in SM calibration tasks. Furthermore, FSE-Net significantly improves the resolution of an in-house field-free point (FFP) MPI system without requiring time-consuming full-size SM calibration, providing an efficient and practical solution for real-world applications.
对傅里叶变换的再思考:分频增强网络在磁粒子图像中快速标定系统矩阵
磁颗粒成像(MPI)是一种新兴的分子层析成像技术,以其高灵敏度和高时空分辨率而闻名。通常,使用基于系统矩阵(SM)的重建方法获得高质量的图像。与其他层析成像方法不同,MPI中的SM校准需要一个耗时的过程来测量MPI扫描仪视野中的体素级响应。由于图像分辨率直接受到SM尺寸的影响,因此对全尺寸SM校准的需求对实际应用提出了挑战。当示踪剂的特性或磁场环境发生变化时,需要反复重新校准,这进一步加剧了这个问题。因此,高效、快速的SM校准至关重要。现有的校准方法通常假设SM中的每个体素是独立的,忽略了体素与其频域稀疏度之间的内在关系。为了解决这个问题,我们提出了一种新的框架,称为频分增强网络(FSE-Net),其中引入了傅里叶变换驱动的特征调制块,即频分增强模块(FSEM),以不同的方式同时分割和增强高频和低频特征。FSE-Net通过快速稀疏采样获得的低分辨率(LR) SM有效捕获和利用频域特征,弥补了LR和高分辨率(HR)体积图像之间的差距,实现了具有精确形状和精细纹理的HR图像。在广泛使用的OpenMPI公共基准和模拟数据集上进行的大量实验表明,我们的FSE-Net优于现有方法,在SM校准任务中实现了最先进的性能。此外,FSE-Net显著提高了内部无场点(FFP) MPI系统的分辨率,而无需耗时的全尺寸SM校准,为实际应用提供了高效实用的解决方案。
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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