惯性约束聚变靶三维成象数值研究

利用ＣＴ技术，采用乘代数重建法（ＭＡＲＴ），编制出三维图象重建程序（ＣＴ３Ｄ，通过五个方位对“星光Ⅱ”装置的ＩＣＦ靶进行Ｘ光针孔成象，对三维重问题进行数值研究，以便重建出靶等离了体的三维图象。     

一种通用的MRI计算机模拟软件

报道一套基于解释脉冲程序的MRI的计算机模拟软件,可适用于任意脉冲序列成象方法的计算机模拟,该软件包括成象物理模型的建立,成象物理过程的模拟,基于K空间的时域FID信号的重组,多维傅立叶交换重建图象,多维滤波反投影重建图象.图象显示等功能模块;分析了成象的物理过程的基本原理和软件的设计思想.并以GE成象和SEPI快速成象方法为例进行了模拟,实现了图象重建.     

边缘特征增强算法和小波分析在精确聚焦中的应用

在数字成象系统中,如何快速而且有效地实现精确聚焦一直是成象系统研究中的焦点.本文提出了利用增强图象边缘特征来提高精确聚焦能力,并将常见的边缘增强滤波器与以小波变换为基础构建的滤波器进行了比较,实验数据表明,虽然常见滤波器和基于小波变换滤波器都能提高聚焦能力,但后者能更有效地分辨出只有微小的离焦量的图象,有很好的应用前景.     

X－波段电子自旋成象

报道了自行设计研制的Ｘ－波段ＥＳＲ成象装置并用之对典型样品作了ＥＳＲ成象测定；用解卷积和分析图象重建中卷积滤波法对自旋样品体系进行了二维的图象重建，获取了能反映真实自旋分布的二维自旋密度图象．     

X-波段电子自旋成象

报道了自行设计研制的X-波段ESR成象装置并用之对典型样品作了ESR成象测定;用解卷积和分析图象重建中卷积滤波法对自旋样品体系进行了二维的图象重建,获取了能反映真实自旋分布的二维自旋密度图象.     

双树小波变换与小波树稀疏联合的低场CS-MRI算法

压缩感知理论常用在磁共振快速成像上,仅采样少量的K空间数据即可重建出高质量的磁共振图像.压缩感知磁共振成像技术的原理是将磁共振图像重建问题建模成一个包含数据保真项、稀疏先验项和全变分项的线性组合最小化问题,显著减少磁共振扫描时间.稀疏表示是压缩感知理论的一个关键假设,重建结果很大程度上依赖于稀疏变换.本文将双树复小波变换和小波树稀疏联合作为压缩感知磁共振成像中的稀疏变换,提出了基于双树小波变换和小波树稀疏的压缩感知低场磁共振图像重建算法.实验表明,本文所提算法可以在某些磁共振图像客观评价指标中表现出一定的优势.     

电光成象系统中的信息传递——系统等效带宽和信噪比链

根据信息论基本原理,讨论了电光成象系统等效带宽和信噪比链及其在传递图象信息中的作用。列举了若干电光成象器件等效光学带宽与极限分辨力的关系,提出了划分电光成象系统信噪比链各环节的另一种方法,给出了相应的表达式。     

一种应用于X射线成象系统的多CCD图象传感器

我们设计了一种基于多CCD的X射线图象传感器。这种图象传感器由X射线增感屏、光学系统和多个CCD传感器组成。与传统的单CCD图象传感器相比,本传感器不但成象面积大,空间分辨力高,而且图象在信噪比,病灶检测灵敏度等方面都有所改善,并且系统具有较高的效费比。研究了多CCD图象传感器的成象形式、图象几何失真(主要是桶形失真)的矫正方法、亮度矫正方法及图象重建方法。实验系统(增感屏+2×2CCD)的实验结果验证了设计思想的可行性和系统的实用性。     

光学CT图象重建的数值模拟研究─Kosenbrock坐标轮换法

时间分辨光学 ＣＴ技术因其对生物组织体的无损性，在生物成象领域引起了广泛的兴趣和研究，已提出许多方案，意在克服由于生物组织体中的多光散射效应所造成的成象障碍．本文简述了基于扩散方程近似的光学ＣＴ正向问题有限元解法，提出采用直接搜索优化算法一Ｒｏｓｅｎｂｒｏｃｋ坐标轮换法求解时间分辨光学 ＣＴ中的图象重建问题，给出了基于积分光强和光子平均飞行时间及其加权组合的二维图象重建问题的数值模拟结果，证实了该方法的可行性．     

一种基于时空变换和压缩感知的磁共振螺旋采样的图像重建算法

螺旋采样磁共振快速成像在功能性成像、并行成像和动态成像等领域发挥着越来越重要的作用.螺旋采样图像重建的传统算法是用核函数将螺旋状分布的k空间数据插值到均匀网格中,再利用傅里叶变换和最小二乘法进行重建.但是基于网格化的算法对核函数过于依赖,在网格化过程中产生难以避免的误差.该文提出了基于时空变换和压缩感知的l1范数的最优化模型和重建算法.时空变换矩阵描述了空间上的磁共振图像与采集到的时域信号间的关系,使得算法直接使用采集到的数据作为保真约束项,避免了网格化过程产生的误差.此外,基于图像处理单元的并行计算被用来提高时空变换矩阵的运算速度,使得算法具有较强的应用价值.     

A novel acquisition-reconstruction algorithm for surface magnetic resonance imaging

In U-shaped, hand-size magnetic resonance surface scanners, imaging is performed along only one spatial direction, with the application of just one gradient (one-dimensional imaging). Lateral spatial resolution can be obtained by magnet displacement, but, in this case, resolution is very poor (on the order of some millimeters) and cannot be useful for high-resolution imaging applications. In this article, an innovative technique for acquisition and reconstruction of images produced by U-shaped, hand-size MRI surface scanners is presented. The proposed method is based on the acquisition of overlapping strips and an analytical reconstruction technique; it is capable of arbitrarily improving spatial lateral resolution without either using a second magnetic field gradient or making any assumptions about the imaged sample extension. Numerical simulations on synthetic images are reported demonstrating the method functionalities. The presented method also makes it possible to use U-shaped, hand-size MRI surface scanners for high-resolution biomedical applications, such as the imaging of skin lesions.     

Undersampled MRI reconstruction with patch-based directional wavelets

Compressed sensing has shown great potential in reducing data acquisition time in magnetic resonance imaging (MRI). In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a preconstructed basis or dictionary. In this paper, patch-based directional wavelets are proposed to reconstruct images from undersampled k-space data. A parameter of patch-based directional wavelets, indicating the geometric direction of each patch, is trained from the reconstructed image using conventional compressed sensing MRI methods and incorporated into the sparsifying transform to provide the sparse representation for the image to be reconstructed. A reconstruction formulation is proposed and solved via an efficient alternating direction algorithm. Simulation results on phantom and in vivo data indicate that the proposed method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise. Besides, the proposed method is not sensitive to the initial image when training directions.     

MR imaging of flow with locally high spatial resolution.

Locally focused magnetic resonance imaging (LF MRI) allows imaging with variable spatial resolution within the field of view (FOV). Because LF MRI uses a priori information to provide locally high resolution in regions with rapid spatial variations in intensity (e.g., blood/tissue interface), it allows accurate reproduction of intense sharp edges in the specimen without blurring and truncation artifacts. This study employs LF MRI for 3D imaging of stationary and pulsatile flow. In the implemented version of LF MRI analytically defined basis functions are used to determine image intensity in regions depicted with low or high resolution. It is demonstrated that LF MRI of flow allows a significant (i.e. 3-4 times) reduction in scan time as compared to conventional FT MRI. It is also shown that LF images of pulsatile flow have a decreased appearance of ghosting artifacts as compared to the images reconstructed by using the conventional method.     

High-resolution imaging using Hadamard encoding

High-resolution imaging techniques using noninvasive modalities such as magnetic resonance (MR) imaging are being pursued as in vivo cancer screening techniques in an attempt to eliminate the invasive nature of surgical biopsy. When acquiring high-resolution MR images for tissue screening, image fields of view have in the past been limited by the matrix sizes available in conventional MR scanners. We present here a technique that uses aliasing to produce high resolution images with larger matrix sizes than are currently available. The image is allowed to alias in both the frequency encoding and phase encoding dimensions, and the individual, aliased fields of view are recovered by Hadamard encoding methods. These fields may then be tiled to obtain a composite image with high spatial resolution and a large field of view. The technique is demonstrated using two-dimensional and three-dimensional in vivo imaging of the human brain and breast.     

Referenceless PRFS MR Thermometry Using Partial Separability Model

Many areas of magnetic resonance (MR)-guided thermal therapy research would benefit from temperature maps with high spatial and temporal resolution. Conventional thermometry relies on the subtraction of baseline images, which makes it sensitive to tissue motion and frequency drift during the course of treatment. For another case is the limit of magnetic resonance imaging sampling speed, it is hard to accurately achieve MR thermometry with high spatiotemporal resolution especially for dynamic organs. To address these issues, a novel method for MR thermometry is presented by exploiting the data redundancy based on partial separability (PS) model and the referenceless thermometry. The PS model highly sparse sample two datasets in the (k–t) space for image reconstruction, which respectively determine the spatial and temporal resolutions. After the phase information is extracted from the images reconstructed by the PS model, the background phase outside the heated region from each acquired phase image through a polynomial fitting is estimated. Extrapolation of the polynomial to the heated region serves as the background phase estimate, which is then subtracted from the actual phase. The thermometry results showed that this method could accurately capture the dynamic change of MR thermometric images with 1.5 mm × 1.5 mm spatial resolution and 250 ms temporal resolution, respectively. The in vivo experiment of MR-guided high intensity focused ultrasound research and the cardiac dynamic MR thermometry are shown to demonstrate the benefits of the proposed method in high spatiotemporal resolution MR thermometry.     

基于空域非均匀傅里叶变换的傅里叶望远镜

为了在稀疏发射阵列下清晰重构目标图像,提出了一种基于空域非均匀傅里叶变换(NDFT)的傅里叶望远镜信号处理方法。依据傅里叶望远镜的发射器位置与抽取的目标空间频率关系,结合MATLAB程序特点,完成了空域非均匀傅里叶逆变换,重构了目标图像。稀疏发射阵列配置方式为:T型阵列单臂放置11个发射望远镜,连续抽取目标的8个低频信息,再抽取3个高频分量。选择不同形状和灰度分布的4个卫星作为成像目标。与补零均匀快速傅里叶变换(FFT)方法重构的图像对比发现:信噪比为100 dB时,相比补零均匀FFT方法, NDFT方法重构图像的Strehl比都有所提升,最高提升了0.159 8。     

Boundary normal pressure-based electrical conductivity reconstruction for magneto–acoustic tomography with magnetic induction

As a kind of multi-physics imaging approach integrating the advantages of electrical impedance tomography and ultrasound imaging with the improved spatial resolution and image contrast, magneto–acoustic tomography with magnetic induction(MAT-MI) is demonstrated to have the capability of electrical impedance contrast imaging for biological tissues with conductivity differences. By being detected with a strong directional transducer, abrupt pressure change is proved to be generated by the gradient of the induced Lorentz force along the force direction at conductivity boundary. A simplified boundary normal pressure(BNP)-based conductivity reconstruction algorithm is proposed and the formula for conductivity distribution inside the object with the clear physical meaning of pressure derivative, is derived. Numerical simulations of acoustic pressure and conductivity reconstruction are conducted based on a 2-layer eccentric cylindrical phantom model using Hilbert transform. The reconstructed two-dimensional conductivity images accord well with the model, thus successfully making up the deficiency of only imaging conductivity boundary in traditional MAT-MI. The proposed method is also demonstrated to have a spatial resolution of one wavelength. This study provides a new method of reconstructing accurate electrical conductivity and suggests the potential applications of MAT-MI in imaging biological tissues with conductivity difference.     

一种多光谱与高分辨率全色图像融合新算法

提出了一种新的基于小波变换的多光谱与高分辨率图像融合方法.该方法通过强度因子有效地将高分辨率图像经小波分解的低频分量信息融合到多光谱图像经小波分解的低频分量中去,使得经过小波反变换的融合图像较大程度地保留了多光谱图像的光谱信息和高分辨率图像的空间分辨率.给出了该方法的融合结果,并与小波变换法(WT方法)进行了比较,证明了该图像融合方法的正确和有效性.