Data analysis for dynamic contrast-enhanced MRI-based cerebral perfusion measurements: correcting for changing cortical CSF volumes

The time evolution of the histogram (number of pixels versus signal intensity) is used to calculate ΔR ₂ parameters from dynamic contrast-enhanced magnetic resonance (MR) imaging of the brain. This method partially corrects for partial volume effects and is an improvement over the approach using the signal intensity as a function of time when confounding factors such as changing cortical cerebrospinal fluid volumes are involved. The maximum value for ΔR ₂ is found to correlate with relative cerebral blood flow as assessed by xenon inhalation and can be used to discriminate between vascular dementia and healthy volunteers. With this method, the normal range for ΔR ₂ values is found to be the same for both young (19–40 years old) and elderly (65–85 years old) healthy volunteers.     

Representation of High Resolution Images from Low Sampled Fourier Data: Applications to Dynamic MRI

In this work we propose the use of B-spline functions for the parametric representation of high resolution images from low sampled data in the Fourier domain. Traditionally, exponential basis functions are employed in this situation, but they produce artifacts and amplify the noise on the data. We present the method in an algorithmic form and carefully consider the problem of solving the ill-conditioned linear system arising from the method by an efficient regularization method. Two applications of the proposed method to dynamic Magnetic Resonance images are considered. Dynamic Magnetic Resonance acquires a time series of images of the same slice of the body; in order to fasten the acquisition, the data are low sampled in the Fourier space. Numerical experiments have been performed both on simulated and real Magnetic Resonance data. They show that the B-splines reduce the artifacts and the noise in the representation of high resolution Magnetic Resonance images from low sampled data. This work was supported by the Italian MIUR project Inverse Problems in Medical Imaging 2004–2006 (grant no 2004015818). Germana Landi received the BS degree in Mathematics from the University of Bologna in 1997 and the Ph.D. degree in Computational Mathematics from the University of Padova in 2000. She is currently a postdoctoral researcher in Numerical Analysis at the Department of Mathematics of the University of Bologna. Her research interests include medical imaging and inverse ill-posed problems. Elena Loli Piccolomini received the BS degree in Mathematics from the University of Bologna in 1988. She is an associate professor in Numerical Analysis at the Department of Mathematics of the University of Bologna. Her research interests include numerical methods for the regularization of discrete ill-posed problems with application to medical imaging (MR, TAC, SPECT, PET).     

一种MRI图像中自动检测肿瘤大小的新方法

MRI用于诊断脑部肿瘤、制定手术计划以及评估手术疗效等时,肿瘤大小的自动检测十分重要,其中的关键技术是检测出肿瘤部位的封闭边缘。虽然现在已有许多边缘提取算法,但这些方法应用于医学图像时很少能同时满足速度快、定位准、去噪好等要求。本文提出采用数学形态学和线形广义模糊算子相结合的方法检测肿瘤边缘,再根据检测结果得到的肿瘤部位轮廓计算肿瘤大小。试验表明,该方法在MRI图像中实施,取得了满意的效果。     

完全开放式磁共振成像磁体的优化设计

采用完全开放式的磁体结构，可满足外科手术对磁共振成像系统的需求。文章基于一种完全开放式的磁体结构——单边磁体结构的初始结构设计理论，构建了一个由块状永磁体组成的单边成像磁体的初始结构，并进一步应用改进的遗传算法对磁体结构进行了优化设计。最终达到提高其目标区域磁场强度、改善磁场均匀度的目的。数值结果显示，本文提出的设计方法给出的设计方案能够在磁体结构外部的成像区域形成均匀并且强大的主磁场。     

膝关节CT图像处理及其矢量化

定制化人工膝关节置换具有较好的治疗效果，可显著减少病人置换手术后的各种并发症。本文通过对膝关节CT图像处理并提取、矢量化膝关节骨骼轮廓曲线，获得了在CAD软件可利用的轮廓曲线，为膝关节曲面的CAD三维重建及其二次设计铺就了技术路线。     

Fusion of Region and Boundary/Surface-Based Computer Vision and Pattern Recognition Techniques for 2-D and 3-D MR Cerebral Cortical Segmentation (Part-II): A State-of-the-Art Review

Extensive growth in functional brain imaging, perfusion-weighted imaging, diffusion-weighted imaging, brain mapping and brain scanning techniques has led tremendously to the importance of cerebral cortical segmentation both in 2-D and 3-D from volumetric brain magnetic resonance imaging data sets. Besides that, recent growth in deformable brain segmentation techniques in 2-D and 3-D has brought the engineering community, such as the areas of computer vision, image processing, pattern recognition and graphics, closer to the medical community, such as to neuro-surgeons, psychiatrists, oncologists, neuro-radiologists and internists. In Part I of this research (see Suri et al [1]), an attempt was made to review the state-of-the-art in 2-D and 3-D cerebral cortical segmentation techniques from brain magnetic resonance imaging based on two main classes: region- and boundary/surface-based. More than 18 different techniques for segmenting the cerebral cortex from brain slices acquired in orthogonal directions were shown using region-based techniques. We also showed more than ten different techniques to segment the cerebral cortex from magnetic resonance brain volumes using boundary/surface-based techniques. This paper (Part II) focuses on presenting state-of-the-art systems based on the fusion of boundary/surface-based with region-based techniques, also called regional-geometric deformation models, which takes the paradigm of partial differential equations in the level set framework. We also discuss the pros and cons of these various techniques, besides giving the mathematical foundations for each sub-class in the cortical taxonomy. Special emphasis is placed on discussing the advantages, validation, challenges and neuro-science/clinical applications of cortical segmentation. Received: 25 August 2000, Received in revised form: 28 March 2001, Accepted: 28 March 2001     

磁共振血管成象技术及实现

本文探讨磁共振在血管成象方面的技术及其实现方法，磁共振血管成象（MRA）可以抑制人体的静止组织而仅仅表现血管信息，并介绍了这种技术的临床应用，背景知识，成象方法及脉冲序列等等。目前磁共振血管成象可分二类：一类称为渡越时间（TOF）方法，这种方法利用血液流入，流出成象平面对磁共振信号幅度的影响，另一类称为相位对比（PC）方法，这种方法利用流体的磁共振信号感应的相移，在我们自己生产的低场磁共振系统上，设计了专门的磁共振血管成象脉冲序列，可以分别得到人体动脉，静脉和动静脉血管图象，实验结果令人满意，最后讨论了实现MRA的有关困难。