磁共振成像实时运动校正方法与弥散成像相关方法研究
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摘要
磁共振成像(MRI)是一项重要的医学影像技术,凭借其对人体无损,组织对比度高的特点已经广泛应用到医学和认知神经科学等领域。然而MRI仍然是个较新的技术,技术本身尚不成熟,还有许多技术问题需要解决。另一方面,MRI在医学研究中的应用也在日新月异地发展,有许多新方法有着重要的临床意义。但由于发展太快,其技术方面存在一些问题,阻碍了在实际研究中广泛应用。本文针对这两方面问题展开研究,具体内容如下：
第一部分,基于光学追踪系统的实时运动校正方法的研究。该部分首先介绍了MRI成像原理以及运动对成像质量的影响,并总结了现有的运动校正方法,然后就基于光学追踪的MRI运动校正方法进行了研究。我们开发了一个基于双摄像机的光学运动追踪和监控系统,然后将其应用到Siemens3T MR成像系统上,实现了实时的头动校正的功能,达到了较好的校正精度和更新速度。研究表明使用光学方法校正运动问题是可行的,且具有彻底解决MRI运动伪影的潜力。
第二部分,MRI应用方法研究。弥散成像技术是MRI的一个重要临床应用,可以用于检测生物组织内水分子的运动特性,而弥散峰度成像(DKI)是一种近年来新提出的弥散模型,能提供新的生物体结构信息,具有重要临床意义。该部分首先介绍了弥散成像的基本原理和各种弥散模型,并重点描述了DKI模型的特点,然后就针对DKI模型的几个问题进行研究：
1)快速DKI数据采集方案。该章首先总结了前人研究中使用的采集方案,然后使用模拟方法对大量的采集方案进行了定性和定量比较,寻找高效采集方案的规律并给出了最优化的采集方案。随后,进一步使用真实的中风病人DKI数据检验最优化采集方案的效果,采集方案的效果通过最常用的基于体素(VBA)分析和感兴趣区(ROI)分析进行了比较。研究发现优化方案与常规采集方案在VBA和RO1分析中的结果基本一致,但优化方案只需要不到一半的采集时间。因此,快速采集方案大大提高了DKI数据采集效率,促进了其在临床中的应用。
2)快速DKI参数估计算法。该章提出了一种循环迭代方法,通过迭代的方式重复计算两个DKI参数,即表观弥散系数和表观峰度系数。该方法计算效率高,收敛速度快,将原本需要数十分钟甚至数小时的计算速度提高到只需要一到两分钟,完全可以满足临床应用的需要。同时,该方法还可在迭代过程中加入公认的物理和生物限制条件,以及平滑处理步骤,有效提高了DKI参数估计的准确性,并改善了参数图像的精细结构可视性。
3)自动化弥散数据处理方法。该章首先介绍了常规的弥散数据处理流程,然后通过Linux和Matlab脚本方式实现了处理流程的完全自动化的。自动化的方法可以在脚本中预先设定处理步骤和参数,修改方便,且过程中不需要人工干预,有效提高了数据处理的效率。
第三部分,运动校正方法在DKI中的应用研究。该部分尝试将本文第一部分的光学运动校正和监控模型应用到了DKI数据采集过程中,从原理上证明该方法可以准确的校正运动对弥散图像和弥散梯度方向的影响。
Magnetic Resonance Imaging (MRI) has become an important technique in medical imaging, and it has been widely used in many fields including medicine and cognitive neuroscience research, because it is non-invasive, safe, and provides high tissue contrast which are distinctive and are not available in other imaging modalities for living tissues. While MRI is a relatively young technique, many aspects of it need to be and can be improved. Meanwhile, clinical application of MRI is also fast evolving, and investigators are developing various novel approaches to solve practical problems that they are facing in daily clinical studies. However, the excessive speed of fast development of these solutions naturally brings about a series of technical difficulties that cannot be solved properly in time This dissertation aims to make some contributions along these two axes.
In Part1, we study the topic of real-time motion correction using an optical tracking system. This part first introduces the principle of MRI, describes how motion affects imaging quality, and reviews the methods of motion correction that are currently available. We then study in detail our proposed method for motion correction using an optical tracking. We have developed a tracking system for motion in3D space based on a two-camera system, and have applied it to data acquisition on a Siemens3T MR platform, which has been designed for acquiring MR imaging data that are free of motion from moving objects using a technique called "real time correction of head motion". The study shows that solving the motion problem using the optical system is feasible, and this method has the potential to completely avoid motion artifacts in MRI.
In Part2, our study focuses on the applications of MRI. In particular, we study a novel diffusion imaging technique, called "Diffusion Kurtosis Imaging (DKI)". Diffusion imaging is an important clinical application of MRI, and it can measure the motion property of water molecules in biological tissue. DKI is a diffusion model proposed in recent years, which can provide new insight on structural information of biological tissues as to the compartment complexity in living tissue. This part first introduces the principle and various models of diffusion imaging, and describes the technical details of the DKI model. Subsequently, we study the following issues pertaining to the DKI model:
1) Fast schemas for acquisition of DKI data. This section first reviews various acquisition schemas adopted in previous studies, and qualitatively and quantitatively evaluates a great number of acquisition schemas (varying the involved numbers of b-value and gradient direction) using simulated datasets. We search for schemas of high performance and consequently make recommendations of optimized acquisition schemas. We subsequently test the performance of the optimized schema based on DKI datasets from patients of a stroke study. The conventional analysis methods, such as voxel-based analysis (VBA) and region-of-interest (ROI) analysis, are applied to examine the performance of the recommended schemas. The study shows that the optimized schema yields very similar results in VBA and ROI analysis, compared to those from the conventional schemas. However, the optimized schema reduces the acquisition time by more than a half. It therefore leads to a conclusion that the fast acquisition schema can significantly accelerate the data acquisition process of DKI, which is a desired feature for clinical applications in practice.
2) A Fast algorithm for estimation of DKI parameters. In this section, we propose an alternatively iterative method, which adopts an iterative framework to calculate the two parameters of the DKI model, namely apparent diffusion coefficient (ADC) and apparent kurtosis coefficient (AKC), alternatively one after the other. The proposed method converges extremely quickly with great accuracy and precision. It reduces the computational time for a typical DKI dataset from tens of minutes or even several hours to merely one or two minutes, which is a feature particularly suitable for clinical applications. In addition, the algorithm incorporates biological and physical constraints that are popularly agreed, and it also incorporates a smoothing procedure into the iterative framework, which effectively improves the estimation accuracy of DKI parameters, and enhances the visibility of delicate structure in AKC and ADC maps.
3) Automatic processing of diffusion data. This section first introduces conventional processing pipeline of diffusion data, and then creates Linux and Matlab scripts to automatically run the processing pipeline. It allows to prescribe all the processing procedures and related parameters in one script program, and to run the program without extra human intervention. Using such a script for data processing facilitates the processing of large amount of imaging data, because the script typically logs all the actions taken on the data, and therefore is easy for modification and future maintenance of the data.
In Part3, combining our work in Part1and2, we study the application of our motion correction method to DKI data acquisition. This part attempts to incorporate the optical system for motion correction that we have developed in Part1into the data acquisition process of DKI. The study verifies that in principle the optical system can accurately correct the errors induced by motion in diffusion images and gradient directions of diffusion.
第一部分,基于光学追踪系统的实时运动校正方法的研究。该部分首先介绍了MRI成像原理以及运动对成像质量的影响,并总结了现有的运动校正方法,然后就基于光学追踪的MRI运动校正方法进行了研究。我们开发了一个基于双摄像机的光学运动追踪和监控系统,然后将其应用到Siemens3T MR成像系统上,实现了实时的头动校正的功能,达到了较好的校正精度和更新速度。研究表明使用光学方法校正运动问题是可行的,且具有彻底解决MRI运动伪影的潜力。
第二部分,MRI应用方法研究。弥散成像技术是MRI的一个重要临床应用,可以用于检测生物组织内水分子的运动特性,而弥散峰度成像(DKI)是一种近年来新提出的弥散模型,能提供新的生物体结构信息,具有重要临床意义。该部分首先介绍了弥散成像的基本原理和各种弥散模型,并重点描述了DKI模型的特点,然后就针对DKI模型的几个问题进行研究：
1)快速DKI数据采集方案。该章首先总结了前人研究中使用的采集方案,然后使用模拟方法对大量的采集方案进行了定性和定量比较,寻找高效采集方案的规律并给出了最优化的采集方案。随后,进一步使用真实的中风病人DKI数据检验最优化采集方案的效果,采集方案的效果通过最常用的基于体素(VBA)分析和感兴趣区(ROI)分析进行了比较。研究发现优化方案与常规采集方案在VBA和RO1分析中的结果基本一致,但优化方案只需要不到一半的采集时间。因此,快速采集方案大大提高了DKI数据采集效率,促进了其在临床中的应用。
2)快速DKI参数估计算法。该章提出了一种循环迭代方法,通过迭代的方式重复计算两个DKI参数,即表观弥散系数和表观峰度系数。该方法计算效率高,收敛速度快,将原本需要数十分钟甚至数小时的计算速度提高到只需要一到两分钟,完全可以满足临床应用的需要。同时,该方法还可在迭代过程中加入公认的物理和生物限制条件,以及平滑处理步骤,有效提高了DKI参数估计的准确性,并改善了参数图像的精细结构可视性。
3)自动化弥散数据处理方法。该章首先介绍了常规的弥散数据处理流程,然后通过Linux和Matlab脚本方式实现了处理流程的完全自动化的。自动化的方法可以在脚本中预先设定处理步骤和参数,修改方便,且过程中不需要人工干预,有效提高了数据处理的效率。
第三部分,运动校正方法在DKI中的应用研究。该部分尝试将本文第一部分的光学运动校正和监控模型应用到了DKI数据采集过程中,从原理上证明该方法可以准确的校正运动对弥散图像和弥散梯度方向的影响。
Magnetic Resonance Imaging (MRI) has become an important technique in medical imaging, and it has been widely used in many fields including medicine and cognitive neuroscience research, because it is non-invasive, safe, and provides high tissue contrast which are distinctive and are not available in other imaging modalities for living tissues. While MRI is a relatively young technique, many aspects of it need to be and can be improved. Meanwhile, clinical application of MRI is also fast evolving, and investigators are developing various novel approaches to solve practical problems that they are facing in daily clinical studies. However, the excessive speed of fast development of these solutions naturally brings about a series of technical difficulties that cannot be solved properly in time This dissertation aims to make some contributions along these two axes.
In Part1, we study the topic of real-time motion correction using an optical tracking system. This part first introduces the principle of MRI, describes how motion affects imaging quality, and reviews the methods of motion correction that are currently available. We then study in detail our proposed method for motion correction using an optical tracking. We have developed a tracking system for motion in3D space based on a two-camera system, and have applied it to data acquisition on a Siemens3T MR platform, which has been designed for acquiring MR imaging data that are free of motion from moving objects using a technique called "real time correction of head motion". The study shows that solving the motion problem using the optical system is feasible, and this method has the potential to completely avoid motion artifacts in MRI.
In Part2, our study focuses on the applications of MRI. In particular, we study a novel diffusion imaging technique, called "Diffusion Kurtosis Imaging (DKI)". Diffusion imaging is an important clinical application of MRI, and it can measure the motion property of water molecules in biological tissue. DKI is a diffusion model proposed in recent years, which can provide new insight on structural information of biological tissues as to the compartment complexity in living tissue. This part first introduces the principle and various models of diffusion imaging, and describes the technical details of the DKI model. Subsequently, we study the following issues pertaining to the DKI model:
1) Fast schemas for acquisition of DKI data. This section first reviews various acquisition schemas adopted in previous studies, and qualitatively and quantitatively evaluates a great number of acquisition schemas (varying the involved numbers of b-value and gradient direction) using simulated datasets. We search for schemas of high performance and consequently make recommendations of optimized acquisition schemas. We subsequently test the performance of the optimized schema based on DKI datasets from patients of a stroke study. The conventional analysis methods, such as voxel-based analysis (VBA) and region-of-interest (ROI) analysis, are applied to examine the performance of the recommended schemas. The study shows that the optimized schema yields very similar results in VBA and ROI analysis, compared to those from the conventional schemas. However, the optimized schema reduces the acquisition time by more than a half. It therefore leads to a conclusion that the fast acquisition schema can significantly accelerate the data acquisition process of DKI, which is a desired feature for clinical applications in practice.
2) A Fast algorithm for estimation of DKI parameters. In this section, we propose an alternatively iterative method, which adopts an iterative framework to calculate the two parameters of the DKI model, namely apparent diffusion coefficient (ADC) and apparent kurtosis coefficient (AKC), alternatively one after the other. The proposed method converges extremely quickly with great accuracy and precision. It reduces the computational time for a typical DKI dataset from tens of minutes or even several hours to merely one or two minutes, which is a feature particularly suitable for clinical applications. In addition, the algorithm incorporates biological and physical constraints that are popularly agreed, and it also incorporates a smoothing procedure into the iterative framework, which effectively improves the estimation accuracy of DKI parameters, and enhances the visibility of delicate structure in AKC and ADC maps.
3) Automatic processing of diffusion data. This section first introduces conventional processing pipeline of diffusion data, and then creates Linux and Matlab scripts to automatically run the processing pipeline. It allows to prescribe all the processing procedures and related parameters in one script program, and to run the program without extra human intervention. Using such a script for data processing facilitates the processing of large amount of imaging data, because the script typically logs all the actions taken on the data, and therefore is easy for modification and future maintenance of the data.
In Part3, combining our work in Part1and2, we study the application of our motion correction method to DKI data acquisition. This part attempts to incorporate the optical system for motion correction that we have developed in Part1into the data acquisition process of DKI. The study verifies that in principle the optical system can accurately correct the errors induced by motion in diffusion images and gradient directions of diffusion.
引文
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