Denoising of MR spectroscopic imaging data using statistical selection of principal components

Objectives

To evaluate a new denoising method for MR spectroscopic imaging (MRSI) data based on selection of signal-related principal components (SSPCs) from principal components analysis (PCA).

Materials and methods

A PCA-based method was implemented for selection of signal-related PCs and denoising achieved by reconstructing the original data set utilizing only these PCs. Performance was evaluated using simulated MRSI data and two volumetric in vivo MRSIs of human brain, from a normal subject and a patient with a brain tumor, using variable signal-to-noise ratios (SNRs), metabolite peak areas, Cramer-Rao bounds (CRBs) of fitted metabolite peak areas and metabolite linewidth.

Results

In simulated data, SSPC determined the correct number of signal-related PCs. For in vivo studies, the SSPC denoising resulted in improved SNRs and reduced metabolite quantification uncertainty compared to the original data and two other methods for denoising. The method also performed very well in preserving the spectral linewidth and peak areas. However, this method performs better for regions that have larger numbers of similar spectra.

Conclusion

The proposed SSPC denoising improved the SNR and metabolite quantification uncertainty in MRSI, with minimal compromise of the spectral information, and can result in increased accuracy.

     

The intravascular contrast agent Clariscan™ (NC 100150 injection) for 3D MR coronary angiography in patients with coronary artery disease

4. Conclusion In our experience Clariscan^TM improves image quality, thus, increasing the visual length and improving the actual visible dimensions of the coronary arteries. Distal segments improved to a higher extent than the proximal parts. In that way sensitivity and specificity for the detection of significant coronary artery disease could be improved. Better SNR and CNR can be used to improve spatial resolution or to reduce scan time by techniques like SENSE or SMASH [14,15].     

High-resolution 3D whole-heart coronary MRA: a study on the combination of data acquisition in multiple breath-holds and 1D residual respiratory motion compensation

Object

To study a scan protocol for coronary magnetic resonance angiography based on multiple breath-holds featuring 1D motion compensation and to compare the resulting image quality to a navigator-gated free-breathing acquisition. Image reconstruction was performed using L1 regularized iterative SENSE.

Materials and methods

The effects of respiratory motion on the Cartesian sampling scheme were minimized by performing data acquisition in multiple breath-holds. During the scan, repetitive readouts through a k-space center were used to detect and correct the respiratory displacement of the heart by exploiting the self-navigation principle in image reconstruction. In vivo experiments were performed in nine healthy volunteers and the resulting image quality was compared to a navigator-gated reference in terms of vessel length and sharpness.

Results

Acquisition in breath-hold is an effective method to reduce the scan time by more than 30 % compared to the navigator-gated reference. Although an equivalent mean image quality with respect to the reference was achieved with the proposed method, the 1D motion compensation did not work equally well in all cases.

Conclusion

In general, the image quality scaled with the robustness of the motion compensation. Nevertheless, the featured setup provides a positive basis for future extension with more advanced motion compensation methods.     

Utilization of MR angiography in perfusion imaging for identifying arterial input function

Magnetic Resonance Materials in Physics, Biology and Medicine - This research utilizes magnetic resonance angiography (MRA) to identify arterial locations during the parametric evaluation of...     

Grid-free interactive and automated data processing for MR chemical shift imaging data

Purpose  

Today’s available chemical shift imaging (CSI) analysis tools are based on Fourier transform of the entire data set prior to interactive display. This strategy is associated with limitations particularly when arbitrary voxel positions within a 3D spatial volume are needed by the user. In this work, we propose and demonstrate a processing-resource-efficient alternative strategy for both interactive and automated CSI data processing up to three spatial dimensions.     

A highly parallelized framework for computationally intensive MR data analysis

Object

The goal of this study was to develop a comprehensive magnetic resonance (MR) data analysis framework for handling very large datasets with user-friendly tools for parallelization and to provide an example implementation.

Materials and methods

Commonly used software packages (AFNI, FSL, SPM) were connected via a framework based on the free software environment R, with the possibility of using Nvidia CUDA GPU processing integrated for high-speed linear algebra operations in R. Three hundred single-subject datasets from the 1,000 Functional Connectomes project were used to demonstrate the capabilities of the framework.

Results

A framework for easy implementation of processing pipelines was developed and an R package for the example implementation of Fully Exploratory Network ICA was compiled. Test runs on data from 300 subjects demonstrated the computational advantages of a processing pipeline developed using the framework compared to non-parallelized processing, reducing computation time by a factor of 15.

Conclusion

The feasibility of computationally intensive exploratory analyses allows broader access to the tools for discovery science.     

MR image reconstruction algorithms for sparse k-space data: a Java-based integration

We have worked on multi-dimensional magnetic resonance imaging (MRI) data acquisition and related image reconstruction methods that aim at reducing the MRI scan time. To achieve this scan-time reduction we have combined the approach of ’increasing the speed’ ofk-space acquisition with that of ‘deliberately omitting’ acquisition ofk-space trajectories (sparse sampling). Today we have a whole range of (sparse) sampling distributions and related reconstruction methods. In the context of a European Union Training and Mobility of Researchers project we have decided to integrate all methods into one coordinating software system. This system meets the requirements that it is highly structured in an object-oriented manner using the Unified Modeling Language and the Java programming environment, that it uses the client-server approach, that it allows multi-client communication sessions with facilities for sharing data and that it is a true distributed computing system with guaranteed reliability using core activities of the Java Jini package.     

A framework for constraining image SNR loss due to MR raw data compression

Magnetic Resonance Materials in Physics, Biology and Medicine - Computationally intensive image reconstruction algorithms can be used online during MRI exams by streaming data to remote...     

一种基于改进VPGA优化Elman神经网络的电力线通信数据处理算法

为了提高宽带电力线通信系统的通信质量,基于宽带电力线通信系统的基本原理,构建了宽带电力线通信系统的仿真模型。以广东云浮某小区用户电表的实际采集数据作为原始数据,在500 m的四径信道模型下,分别引入了BP神经网络和Elman神经网络进行了通信质量的仿真测试。针对神经网络算法普遍存在的抗噪声性能差的缺点,提出一种基于改进VPGA优化的Elman神经网络用于电力线通信系统解映射模块的数据处理,并进行了仿真测试。实验结果表明,该算法不占用宝贵的频谱资源且实现方便,并且除去信号被噪声淹没等极端恶劣的信道环境以外,均可以显著提高宽带电力线通信系统的通信质量,降低误码率。     

BP神经网络预估锂离子电池SOC训练数据选择

采用BP神经网络对电动汽车用动力锂离子电池荷电状态(SOC)预估进行研究,分析了BP神经网络的模型原理及锂离子电池极化现象。对比采用恒流实验数据训练BP神经网络,提出改进BP神经网络训练数据选择方法,以适应变电流的实际循环中,锂离子电池因极化现象而产生的动态非线性,并进行了电池SOC值的预估。实验表明,采用改进训练数据训练的BP神经网络,在电流剧烈变化的实际工况环境下具有更高的SOC预估精度。     

Development and validation of an algorithm for registration of serial 3D MR breast data sets

We report on the development of an algorithm to improve the registration of serial 3D MR breast images using combined global translation and rotation with locally varying parameters as geometric transformations. Several phantom and volunteer data sets were acquired and registered using mutual information as a similarity measure of the matching process. After applying a global translation by using a rigid matcher, optimum horizontal and vertical rotation angles were determined. In case of the phantom measurements, angle optimization was performed for each slice of the 3D data set of the phantom, which was deliberately shifted and rotated around different axes. In case of registration of volunteer data, optimum rotation parameters were calculated for preselected equidistant slices of the data set to speed up the calculation time. For slices located between and outside these support slices, the rotation angles were calculated by linear interpolation and extrapolation of the slope of the regression determined by the optimized angles of the support slices. The algorithm improves the registration of serial 3D MR data sets and represents a compromise between a rigid and an elastic 3D matching procedure.     

基于相似日选择的调度计划安全校核潮流数据生成

为了提高调度计划安全校核的准确度和精细度,提出了一种基于相似日选择的调度计划安全校核潮流数据生成方法。根据候选基准方式与计划方式综合相似度最高原则从相似日历史数据中筛选出基准方式,在此基础上生成内网潮流数据的拓扑信息和无功电压数据。通过在潮流计算迭代过程中自动投退容抗器,将计划方式母线电压控制在基准方式实际电压值附近,形成收敛的合理的内网潮流数据。根据联络线一致且联络线潮流最接近原则从外网潮流数据中筛选出待拼接的外网方式,在此基础上将内网潮流数据和外网方式数据进行拼接,通过调整外网潮流满足边界节点电压和联络线有功约束,生成满足调度计划安全校核的全网潮流数据。通过实际电网应用,验证了该方法的实用性和有效性。