论文标题
基于多个测量矢量模型的多频电磁断层扫描的图像重建
Image Reconstruction for Multi-frequency Electromagnetic Tomography based on Multiple Measurement Vector Model
论文作者
论文摘要
成像生物样品的生物阻抗分布可以提供对样品的电性能的理解，这是生理状态的重要指标。本文介绍了生物医学成像的多频电磁层析成像（MFEMT）技术。该系统由具有可调灵敏度和激发频率的层计线圈的8个通道组成。为了利用每个测量值之间的频率相关性，我们基于多重测量向量（MMV）模型同时重建多个频率数据。通过使用稀疏的贝叶斯学习方法来解决MMV问题，该方法特别有效地分布稀疏。已经进行了模拟和实验来验证该方法的性能。结果表明，通过利用多个测量值，与常用的单个测量矢量模型相比，提出的方法对不足问题的嘈杂数据更为强大。
Imaging the bio-impedance distribution of a biological sample can provide understandings about the sample's electrical properties which is an important indicator of physiological status. This paper presents a multi-frequency electromagnetic tomography (mfEMT) technique for biomedical imaging. The system consists of 8 channels of gradiometer coils with adjustable sensitivity and excitation frequency. To exploit the frequency correlation among each measurement, we reconstruct multiple frequency data simultaneously based on the Multiple Measurement Vector (MMV) model. The MMV problem is solved by using a sparse Bayesian learning method that is especially effective for sparse distribution. Both simulations and experiments have been conducted to verify the performance of the method. Results show that by taking advantage of multiple measurements, the proposed method is more robust to noisy data for ill-posed problems compared to the commonly used single measurement vector model.
