弥散张量成像和高角分辨率弥散成像数据的鲁棒估计和有效平滑
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摘要
弥散张量成像(Diffusion Tensor Imaging, DTI)是一种可以非入侵的观察大脑内部水分子微观运动的成像技术,在研究大脑疾病方面有着很好的辅助作用。高角分辨率弥散成像(High Angular Resolution Diffusion Imaging, HARDI)是DTI的进一步发展,这种成像模式利用更多的成像时间来获取更精细的空间角度信息。DTI和HARDI都是从弥散加权成像(Diffusion Weighted Imaging, DWI)中估计出水分子弥散的方向信息,从而判断大脑内部神经纤维的走向,所以准确的估计弥散方向信息对于准确的了解大脑的微观神经结构是至关重要的。而DTI和HARDI信号在获取过程中,会受到噪声和系统误差的影响,从而影响弥散方向信息的准确估计。
我们用局部加权线性回归(locally weighted linear least squares, LWLLS)来估计DWI信号中水分子弥散的方向信息。局部加权线性回归是一个在线性的方程里面,整合了线性回归和局部邻域平滑的估计方法。这个方法在线性回归的方程中,加入了局部邻域的相关性,这种相关性通过一个邻域的权重函数来表示。我们采用了各向异性的双边滤波器作为权重函数,使得利用局部加权线性回归在估计弥散方向信息的同时,也可以利用体素的邻域信息来平滑DWI信号。
HARDI信号由于采用了更多的信号采集时间,并且信噪比较低,所以需要额外的平滑处理。我们提出一个基于惯性矩的相似性测度来分析弥散方向信息。然后,利用这个相似性测度来平滑弥散方向信息。
使用模拟数据和真实数据的大量实验以及与其他常用方法的比较证明,我们的方法能够很好的估计和平滑DTI和HARDI信号。
分割和配准在医学图像分析中有着重要的作用,我们也发展了基于偏微分方程的医学图像分割算法和基于微分同胚Demons算法的医学图像配准算法。这些算法也将应用于DTI图像的分割和配准。
Diffusion Tensor Imaging (DTI) is a non-invasive and in vivo imaging technique which measures the motion of water molecules in the microstructures of living tissues. It is a powerful tool for studying tissue microstructures in vivo. High Angular Resolution Diffusion Imaging (HARDI) is a further development based on DTI and requires relatively more imaging time and finer spatial resolution. Both based on Diffusion Weighted Imaging (DWI) data, DTI and HARDI can be used for estimating the orientational information of the motion of water molecules, which in theory is supposed to coincide with the running direction of the underlying fiber tracts. The DWI data can thus be used to reconstruct the fiber pathway using either the DTI or the HARDI model. However, DWI data usually contain severe artifacts that are caused by thermal noise, motion and eddy current. These artifacts can seriously bias the correct estimation of the mentioned orientional information, thereby affecting the reconstruction of fiber pathways.
We use a locally weighted linear least squares (LWLLS) method to estimate the diffusion orientation information from DWI data. The method combines the linear least squares (LLS) and local neighborhood smoothing through adding the correlate information within the local neighborhood in linear least square. It incorporates into the LLS framework a bilateral filter which assigns different weights to neighbor voxels. This method efficiently smoothes the DWI data and estimates optimal diffusion orientation simultaneously.
Because HARDI data acquired at high b-values are usually with low signal-to-noise ratios, estimating the orientation distribution function (ODF) from such data is therefore a challenging task. We proposed a similarity measure for ODF based on moment of interia, combining a similarity measure and an anisotropic diffusion filter to smooth the ODF fields.
Extensive experiments and comparisons with other alternative methods using both simulated and real-world datasets demonstrated that our methods perform excellent on DTI and HARDI data.
Segmentation and registration are two interesting and challenging topics in many applications of medical imaging. We developed a segmentation method based on partial differential equation and a registration method based on diffeomorphic demons algorithm for medical images processing. These methods are applicable to DTI data analysis.
我们用局部加权线性回归(locally weighted linear least squares, LWLLS)来估计DWI信号中水分子弥散的方向信息。局部加权线性回归是一个在线性的方程里面,整合了线性回归和局部邻域平滑的估计方法。这个方法在线性回归的方程中,加入了局部邻域的相关性,这种相关性通过一个邻域的权重函数来表示。我们采用了各向异性的双边滤波器作为权重函数,使得利用局部加权线性回归在估计弥散方向信息的同时,也可以利用体素的邻域信息来平滑DWI信号。
HARDI信号由于采用了更多的信号采集时间,并且信噪比较低,所以需要额外的平滑处理。我们提出一个基于惯性矩的相似性测度来分析弥散方向信息。然后,利用这个相似性测度来平滑弥散方向信息。
使用模拟数据和真实数据的大量实验以及与其他常用方法的比较证明,我们的方法能够很好的估计和平滑DTI和HARDI信号。
分割和配准在医学图像分析中有着重要的作用,我们也发展了基于偏微分方程的医学图像分割算法和基于微分同胚Demons算法的医学图像配准算法。这些算法也将应用于DTI图像的分割和配准。
Diffusion Tensor Imaging (DTI) is a non-invasive and in vivo imaging technique which measures the motion of water molecules in the microstructures of living tissues. It is a powerful tool for studying tissue microstructures in vivo. High Angular Resolution Diffusion Imaging (HARDI) is a further development based on DTI and requires relatively more imaging time and finer spatial resolution. Both based on Diffusion Weighted Imaging (DWI) data, DTI and HARDI can be used for estimating the orientational information of the motion of water molecules, which in theory is supposed to coincide with the running direction of the underlying fiber tracts. The DWI data can thus be used to reconstruct the fiber pathway using either the DTI or the HARDI model. However, DWI data usually contain severe artifacts that are caused by thermal noise, motion and eddy current. These artifacts can seriously bias the correct estimation of the mentioned orientional information, thereby affecting the reconstruction of fiber pathways.
We use a locally weighted linear least squares (LWLLS) method to estimate the diffusion orientation information from DWI data. The method combines the linear least squares (LLS) and local neighborhood smoothing through adding the correlate information within the local neighborhood in linear least square. It incorporates into the LLS framework a bilateral filter which assigns different weights to neighbor voxels. This method efficiently smoothes the DWI data and estimates optimal diffusion orientation simultaneously.
Because HARDI data acquired at high b-values are usually with low signal-to-noise ratios, estimating the orientation distribution function (ODF) from such data is therefore a challenging task. We proposed a similarity measure for ODF based on moment of interia, combining a similarity measure and an anisotropic diffusion filter to smooth the ODF fields.
Extensive experiments and comparisons with other alternative methods using both simulated and real-world datasets demonstrated that our methods perform excellent on DTI and HARDI data.
Segmentation and registration are two interesting and challenging topics in many applications of medical imaging. We developed a segmentation method based on partial differential equation and a registration method based on diffeomorphic demons algorithm for medical images processing. These methods are applicable to DTI data analysis.
引文
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