论文标题
使用频率约束稀疏贝叶斯学习的急性中风检测的多频电磁断层扫描
Multi-frequency Electromagnetic Tomography for Acute Stroke Detection Using Frequency Constrained Sparse Bayesian Learning
论文作者
论文摘要
成像大脑的生物阻抗分布可以提供急性中风的初始诊断。本文提出了紧凑的非辐射断层扫描方式，即多频电磁断层扫描（MFEMT），用于初步诊断急性中风。 MFEMT系统由具有可调灵敏度和激发频率的梯度计线圈的12个通道组成。为了解决MFEMT的图像重建问题，我们提出了增强的频率约束稀疏贝叶斯学习（FC-SBL），以同时重建所有频率的电导率分布。基于稀疏贝叶斯学习（SBL）框架中的多个测量向量（MMV）模型，FC-SBL可以通过利用频率约束信息来恢复多个图像之间的基本电导率分布模式。建立了一个现实的3D头模型，以模拟中风检测方案，显示了MFEMT穿透高度电阻的头骨和使用FC-SBL提高图像质量的能力。模拟和实验都表明，与单个测量矢量模型相比，所提出的FC-SBL方法对于MFEMT的图像重建问题对于嘈杂的数据是可靠的，该模型有望在大脑区域检测具有增强的空间分辨率和基线无基线方式的急性中风。
Imaging the bio-impedance distribution of the brain can provide initial diagnosis of acute stroke. This paper presents a compact and non-radiative tomographic modality, i.e. multi-frequency Electromagnetic Tomography (mfEMT), for the initial diagnosis of acute stroke. The mfEMT system consists of 12 channels of gradiometer coils with adjustable sensitivity and excitation frequency. To solve the image reconstruction problem of mfEMT, we propose an enhanced Frequency-Constrained Sparse Bayesian Learning (FC-SBL) to simultaneously reconstruct the conductivity distribution at all frequencies. Based on the Multiple Measurement Vector (MMV) model in the Sparse Bayesian Learning (SBL) framework, FC-SBL can recover the underlying distribution pattern of conductivity among multiple images by exploiting the frequency constraint information. A realistic 3D head model was established to simulate stroke detection scenarios, showing the capability of mfEMT to penetrate the highly resistive skull and improved image quality with FC-SBL. Both simulations and experiments showed that the proposed FC-SBL method is robust to noisy data for image reconstruction problems of mfEMT compared to the single measurement vector model, which is promising to detect acute strokes in the brain region with enhanced spatial resolution and in a baseline-free manner.
