急性肌肉拉伤的MRI应用进展

急性肌肉拉伤是一种急性间接肌肉损伤,常见于日常生活和运动中。仅根据临床症状和体格检查难以准确诊断。常规MRI软组织分辨率高,是急性肌肉拉伤诊断和预后评估的重要手段。近年发展的肌肉MR弹性成像、T2弛豫时间图、磁共振扩散加权成像、扩散张量成像和磁共振波谱具有从微观结构、病理和生理层面为急性肌肉拉伤的诊断提供定量、客观依据的潜力。作者对应用于急性肌肉拉伤的各项MRI技术进行综述。     

Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers

MRI tractography is the mapping of neural fiber pathways based on diffusion MRI of tissue diffusion anisotropy. Tractography based on diffusion tensor imaging (DTI) cannot directly image multiple fiber orientations within a single voxel. To address this limitation, diffusion spectrum MRI (DSI) and related methods were developed to image complex distributions of intravoxel fiber orientation. Here we demonstrate that tractography based on DSI has the capacity to image crossing fibers in neural tissue. DSI was performed in formalin-fixed brains of adult macaque and in the brains of healthy human subjects. Fiber tract solutions were constructed by a streamline procedure, following directions of maximum diffusion at every point, and analyzed in an interactive visualization environment (TrackVis). We report that DSI tractography accurately shows the known anatomic fiber crossings in optic chiasm, centrum semiovale, and brainstem; fiber intersections in gray matter, including cerebellar folia and the caudate nucleus; and radial fiber architecture in cerebral cortex. In contrast, none of these examples of fiber crossing and complex structure was identified by DTI analysis of the same data sets. These findings indicate that DSI tractography is able to image crossing fibers in neural tissue, an essential step toward non-invasive imaging of connectional neuroanatomy.     

Polarized light spatial frequency domain imaging for non-destructive quantification of soft tissue fibrous structures

The measurement of soft tissue fiber orientation is fundamental to pathophysiology and biomechanical function in a multitude of biomedical applications. However, many existing techniques for quantifying fiber structure rely on transmitted light, limiting general applicability and often requiring tissue processing. Herein, we present a novel wide-field reflectance-based imaging modality, which combines polarized light imaging (PLI) and spatial frequency domain imaging (SFDI) to rapidly quantify preferred fiber orientation on soft collagenous tissues. PLI utilizes the polarization dependent scattering property of fibers to determine preferred fiber orientation; SFDI imaging at high spatial frequency is introduced to reject the highly diffuse photons and to control imaging depth. As a result, photons scattered from the superficial layer of a multi-layered sample are highlighted. Thus, fiber orientation quantification can be achieved for the superficial layer with optical sectioning. We demonstrated on aortic heart valve leaflet that, at spatial frequency of f = 1mm⁻¹, the diffuse background can be effectively rejected and the imaging depth can be limited, thus improving quantification accuracy.OCIS codes: (170.0110) Imaging systems, (170.6935) Tissue characterization, (290.5855) Scattering, polarization, (110.0113) Imaging through turbid media     

Real-time MR diffusion tensor and Q-ball imaging using Kalman filtering

Diffusion magnetic resonance imaging (dMRI) has become an established research tool for the investigation of tissue structure and orientation. In this paper, we present a method for real-time processing of diffusion tensor and Q-ball imaging. The basic idea is to use Kalman filtering framework to fit either the linear tensor or Q-ball model. Because the Kalman filter is designed to be an incremental algorithm, it naturally enables updating the model estimate after the acquisition of any new diffusion-weighted volume. Processing diffusion models and maps during ongoing scans provides a new useful tool for clinicians, especially when it is not possible to predict how long a subject may remain still in the magnet. First, we introduce the general linear models corresponding to the two diffusion tensor and analytical Q-ball models of interest. Then, we present the Kalman filtering framework and we focus on the optimization of the diffusion orientation sets in order to speed up the convergence of the online processing. Last, we give some results on a healthy volunteer for the online tensor and the Q-ball model, and we make some comparisons with the conventional offline techniques used in the literature. We could achieve full real-time for diffusion tensor imaging and deferred time for Q-ball imaging, using a single workstation.     

Calibration of Ultrasound Backscatter Temperature Imaging for High-Intensity Focused Ultrasound Treatment Planning

High-intensity focused ultrasound (HIFU) is rapidly gaining acceptance as a non-invasive method for soft tissue tumor ablation, but improvements in the methods of treatment delivery, planning and monitoring are still required. Backscatter temperature imaging (BTI) uses ultrasound to visualize heating-induced echo strain and may be used to indicate the position of the HIFU focal region using low-power “sub-lesioning” exposure. The technique may also provide a quantitative tool for assessing the efficacy of treatment delivery if apparent strain measurements can be related to the underlying temperature rise. To obtain temperature estimates from strain measurements, the relationship between these variables has to be either measured or otherwise assumed from previous calibrations in similar tissues. This article describes experimental measurements aimed at deriving the relationship between temperature rise and apparent strain in the laboratory environment using both ex vivo bovine liver tissue samples and normothermically perfused porcine livers. A BTI algorithm was applied to radiofrequency ultrasound echo data acquired from a clinical ultrasound scanner (Z.One, Zonare Medical Systems, Mountain View, CA, USA) where the imaging probe was aligned with the focal region of a HIFU transducer. Temperature measurements were obtained using needle thermocouples implanted in the liver tissue. A series of “non-ablative” HIFU exposures giving peak temperatures below 10°C were made in three separate ex vivo bovine livers, yielding an average strain/temperature coefficient of 0.126 ± 0.088 percentage strain per degree Celsius. In the perfused porcine livers at a starting temperature of 38°C (normal body temperature) the strain/temperature coefficients were found to be 0.040 ± 0.029 percentage strain per degree Celsius. The uncertainty in these results is directly linked to the precision of the strain measurement, as well as the naturally occurring variance between different tissue samples, indicating that BTI may lack the accuracy required to be implemented successfully in practice as a quantitative treatment planning technique at a sub-lesioning exposure level. This is because, to be of use in treatment planning, temperature-rise estimates may require an accuracy greater (<10%) than that offered by BTI measurement. BTI may, however, still play a role in ensuring the correct positioning of the focal region and as a treatment monitoring modality capable of detecting an increased rate of heating in tissue after HIFU ablation.     

Examining brain microstructure using structure tensor analysis of histological sections

The mammalian central nervous system has a tremendous structural complexity, and diffusion tensor imaging (DTI) is unique in its ability to extract microstructural tissue properties at a macroscopic scale. However, despite its widespread use and applications in clinical and research settings, accurate validation of DTI has notoriously lagged the advances in image acquisition and analysis. In this report, we demonstrate an approach to visualize and quantify the microscopic features of histological sections on multiple length scales using techniques derived from image texture analysis. Structure tensor (ST) analysis was applied to fluorescence microscopy images of rat brain sections to visualize and quantify tissue microstructure. Images were digitally color-coded based on the local orientation in the pixelwise ST implementation, which allowed direct visualization of white matter complexity at the microscopic level. A piecewise ST algorithm was also employed to quantify anisotropy and orientation at a resolution comparable to that typically acquired with DTI. Anisotropy measured with ST analysis of stained histological sections was highly correlated with anisotropy measured by ex vivo DTI of the same brains (R(2)=0.92). Furthermore, angular histograms, or Fiber Orientation Distributions (FODs), were computed to mimic similar measures derived from high angular resolution diffusion imaging methods. The FODs for each pixel were fit to a mixture of von Mises distributions to identify putative regions of multiple fiber populations (i.e. crossing fibers). Despite its current application to two-dimensional microscopy, the ST analysis is a novel approach to visualize and quantify microstructure in the central nervous system in both health and disease, and advances the available set of tools for validating DTI and other diffusion MRI techniques.     

Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI

Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture and brain connectivity. In regions of crossing fibers, however, the tensor model fails because it cannot represent multiple, independent intra-voxel orientations. Most of the methods that have been proposed to resolve this problem require diffusion magnetic resonance imaging (MRI) data that comprise large numbers of angles and high b-values, making them problematic for routine clinical imaging and many scientific studies. We present a technique based on compressed sensing that can resolve crossing fibers using diffusion MRI data that can be rapidly and routinely acquired in the clinic (30 directions, b-value equal to 700 s/mm²). The method assumes that the observed data can be well fit using a sparse linear combination of tensors taken from a fixed collection of possible tensors each having a different orientation. A fast algorithm for computing the best orientations based on a hierarchical compressed sensing algorithm and a novel metric for comparing estimated orientations are also proposed. The performance of this approach is demonstrated using both simulations and in vivo images. The method is observed to resolve crossing fibers using conventional data as well as a standard q-ball approach using much richer data that requires considerably more image acquisition time.     

Optimal real-time Q-ball imaging using regularized Kalman filtering with incremental orientation sets

Diffusion MRI has become an established research tool for the investigation of tissue structure and orientation. Since its inception, Diffusion MRI has expanded considerably to include a number of variations such as diffusion tensor imaging (DTI), diffusion spectrum imaging (DSI) and Q-ball imaging (QBI). The acquisition and analysis of such data is very challenging due to its complexity. Recently, an exciting new Kalman filtering framework has been proposed for DTI and QBI reconstructions in real-time during the repetition time (TR) of the acquisition sequence. In this article, we first revisit and thoroughly analyze this approach and show it is actually sub-optimal and not recursively minimizing the intended criterion due to the Laplace–Beltrami regularization term. Then, we propose a new approach that implements the QBI reconstruction algorithm in real-time using a fast and robust Laplace–Beltrami regularization without sacrificing the optimality of the Kalman filter. We demonstrate that our method solves the correct minimization problem at each iteration and recursively provides the optimal QBI solution. We validate with real QBI data that our proposed real-time method is equivalent in terms of QBI estimation accuracy to the standard offline processing techniques and outperforms the existing solution. Last, we propose a fast algorithm to recursively compute gradient orientation sets whose partial subsets are almost uniform and show that it can also be applied to the problem of efficiently ordering an existing point-set of any size. This work enables a clinician to start an acquisition with just the minimum number of gradient directions and an initial estimate of the orientation distribution functions (ODF) and then the next gradient directions and ODF estimates can be recursively and optimally determined, allowing the acquisition to be stopped as soon as desired or at any iteration with the optimal ODF estimates. This opens new and interesting opportunities for real-time feedback for clinicians during an acquisition and also for researchers investigating into optimal diffusion orientation sets and real-time fiber tracking and connectivity mapping.     

Local white matter geometry from diffusion tensor gradients

We introduce a mathematical framework for computing geometrical properties of white matter fibers directly from diffusion tensor fields. The key idea is to isolate the portion of the gradient of the tensor field corresponding to local variation in tensor orientation, and to project it onto a coordinate frame of tensor eigenvectors. The resulting eigenframe-centered representation then makes it possible to define scalar indices (or measures) that describe the local white matter geometry directly from the diffusion tensor field and its gradient, without requiring prior tractography. We derive new scalar indices of (1) fiber dispersion and (2) fiber curving, and we demonstrate them on synthetic and in vivo data. Finally, we illustrate their applicability to a group study on schizophrenia.     

弥散频谱成像的研究进展

研究表明人类大脑中的很多区域内存在着多方向的纤维束,这类区域的体素内水分子的平均弥散并不符合高斯分布。因此,基于单方向纤维束模型的弥散张量成像在揭示组织微观结构中存在先天缺陷。弥散频谱成像使用多b值多方向的扫描序列,采集水分子在整个q空间的弥散信息,通过一个具有高角分辨率的概率密度函数来描述水分子的弥散运动,能够可靠地观测单体素内多方向的纤维束,并据此进行纤维束追踪重建出真实而复杂的组织结构。作者全面介绍了近年来弥散频谱成像在基本原理、方法学和应用方面的研究进展,为推动国内弥散成像技术在科研和临床上的发展提供帮助。     

Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution

Diffusion-weighted magnetic resonance imaging can provide information related to the arrangement of white matter fibers. The diffusion tensor is the model most commonly used to derive the orientation of the fibers within a voxel. However, this model has been shown to fail in regions containing several fiber populations with distinct orientations. A number of alternative models have been suggested, such as multiple tensor fitting, q-space, and Q-ball imaging. However, each of these has inherent limitations. In this study, we propose a novel method for estimating the fiber orientation distribution directly from high angular resolution diffusion-weighted MR data without the need for prior assumptions regarding the number of fiber populations present. We assume that all white matter fiber bundles in the brain share identical diffusion characteristics, thus implicitly assigning any differences in diffusion anisotropy to partial volume effects. The diffusion-weighted signal attenuation measured over the surface of a sphere can then be expressed as the convolution over the sphere of a response function (the diffusion-weighted attenuation profile for a typical fiber bundle) with the fiber orientation density function (ODF). The fiber ODF (the distribution of fiber orientations within the voxel) can therefore be obtained using spherical deconvolution. The properties of the technique are demonstrated using simulations and on data acquired from a volunteer using a standard 1.5-T clinical scanner. The technique can recover the fiber ODF in regions of multiple fiber crossing and holds promise for applications such as tractography.     

磁共振扩散张量成像在显示正常人脑白质纤维中的应用

目的应用DTI技术显示正常人脑白质纤维,探讨其与解剖学描述的一致性。方法20名健康志愿者行颅脑MRI与颅脑单次激发回波平面扩散张量成像扫描(b值=0,500s/mm2),在SiemensLeonardo工作站应用纤维束跟踪软件(SiemensStandar12dirs)进行后处理重建出白质纤维束。结果对主要白质纤维如皮质脊髓束、皮质核束、胼胝体、扣带、上纵束、下纵束、上枕额束、下枕额束、钩束进行模拟显示,不同纤维需要选择适合的感兴趣区、各向异性阈值、角度阈值、步长和体素内采样数目等参数,显示结果与解剖学描述具有较好的一致性。结论利用扩散张量成像技术可模拟显示正常人脑白质纤维,与解剖学描述具有较好的一致性,是在活体中研究人脑白质纤维的一种较可靠的方法。     

磁共振扩散成像在脑胶质瘤预后预测的研究进展

脑胶质瘤是中枢神经系统最常见的恶性肿瘤,不同病理级别及基因型的脑胶质瘤预后差别很大。MR扩散成像通过检测水分子的微观运动反映组织结构的变化,对于脑胶质瘤预后预测有着重要的临床意义。扩散张量成像、扩散峰度成像、体素内非相干运动成像、拉伸指数模型扩散加权成像、超高b值DWI成像、神经突起方向离散度与密度成像等可以定量检测组织内水分子的扩散信息,反映肿瘤的异质性及细胞增殖情况,这为准确预测脑胶质瘤预后提供了新的思路。作者对MR扩散成像在脑胶质瘤预后预测的研究进展进行综述。     

DTI及tractography对失写症书写中枢及相关结构的初步分析

目的扩散张量成像技术探讨失写症脑内神经纤维改变的特征.方法失写症5例,SIEMENS Trio 2003T行常规MRI和扩散张量成像.结果 5例左侧额中回后部不同程度信号异常.DTI fiber tractography显示书写中枢纤维数量明显降低,尤其U形纤维明显,额叶脑回间、中央前回、Broca区的纤维联系明显减少,内囊后肢前部的纤维中断,书写中枢与内囊后肢前部间的关系松散.结论失写症脑内纤维改变主要以额叶脑回间的U形纤维的减少,书写中枢与内囊后肢前部间的纤维联系明显减少.     

Structural insights from high-resolution diffusion tensor imaging and tractography of the isolated rat hippocampus

The hippocampus is a critical structure for learning and memory formation injured by diverse neuropathologies such as epilepsy or Alzheimer's disease. Recently, clinical investigations have attempted to use diffusion tensor MRI as a more specific surrogate marker for hippocampal damage. To first better understand the tissue architecture of healthy hippocampal regions, this study characterized 10 rat hippocampi with diffusion tensor imaging (DTI) at 50-microm in-plane image resolution using a 14.1-T magnet. Chemical fixation of the dissected and straightened rat hippocampus provided a simple, effective way to reduce partial volume effects when segmenting hippocampal regions and improved mean signal-to-noise per unit time (e.g. 50.6+/-4.4 at b=1250 s/mm2 in 27 min). Contrary to previous reports that water diffusion is homogeneous throughout the nervous system, statistically different mean diffusivities were observed (e.g. 0.238+/-0.054 and 0.318+/-0.084 microm2/ms for the molecular and granule cell layers respectively) (ANOVA, P<0.05). Different hippocampal subregions had lower fractional anisotropy than uniformly fibrous structures like corpus callosum because of their complex architecture. DTI-derived color fiber orientation maps and tractography demonstrated most components of the trisynaptic intrahippocampal pathway (e.g. orientations in stratum lacunosum-moleculare were dominated by perforant and Schaffer fibers) and also permitted some assessment of connectivity in the rat hippocampus.     

The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain

In diffusion tensor magnetic resonance imaging (DT-MRI), limitations concerning complex fiber architecture (when an image voxel contains fiber populations with more than one dominant orientation) are well-known. Fractional anisotropy (FA) values are lower in such areas because of a lower directionality of diffusion on the voxel-scale, which makes the interpretation of FA less straightforward. Moreover, the interpretation of the axial and radial diffusivities is far from trivial when there is more than one dominant fiber orientation within a voxel. In this work, using (i) theoretical considerations, (ii) simulations, and (iii) experimental data, it is demonstrated that the mean diffusivity (or the trace of the diffusion tensor) is lower in complex white matter configurations, compared with tissue where there is a single dominant fiber orientation within the voxel. We show that the magnitude of this reduction depends on various factors, including configurational and microstructural properties (e.g., the relative contributions of different fiber populations) and acquisition settings (e.g., the b-value). These results increase our understanding of the quantitative metrics obtained from DT-MRI and, in particular, the effect of the microstructural architecture on the mean diffusivity. More importantly, they reinforce the growing awareness that differences in DT-MRI metrics need to be interpreted cautiously.     

人脑连合纤维的弥散张量纤维束成像

目的建立弥散张量纤维束成像对人脑连合纤维的显示方法,探讨显示结果与已知解剖知识的一致性.方法对1个正常志愿者进行单次激发回波平面弥散张量成像,利用纤维束成像软件包显示脑内连合纤维,观察重建的连合纤维与已知解剖知识的一致性.结果通过选择恰当的感兴趣区,设置不同的分数各向异性阈值、角度阈值、步长和体素内采样数目等参数,弥散张量纤维束成像可以清楚地显示胼胝体、穹隆和前连合等连合纤维的三维结构.显示结果与已知解剖知识具有良好的一致性.结论纤维束成像的结果与解剖学描述具有高度一致性,该方法是一种可靠的研究人脑纤维连接的方法.     

High-resolution diffusion tensor imaging and tractography of the human optic chiasm at 9.4 T

The optic chiasm with its complex fiber micro-structure is a challenge for diffusion tensor models and tractography methods. Likewise, it is an ideal candidate for evaluation of diffusion tensor imaging tractography approaches in resolving inter-regional connectivity because the macroscopic connectivity of the optic chiasm is well known. Here, high-resolution (156 microm in-plane) diffusion tensor imaging of the human optic chiasm was performed ex vivo at ultra-high field (9.4 T). Estimated diffusion tensors at this high resolution were able to capture complex fiber configurations such as sharp curves, and convergence and divergence of tracts, but were unable to resolve directions at sites of crossing fibers. Despite the complex microstructure of the fiber paths through the optic chiasm, all known connections could be tracked by a line propagation algorithm. However, fibers crossing from the optic nerve to the contralateral tract were heavily underrepresented, whereas ipsilateral nerve-to-tract connections, as well as tract-to-tract connections, were overrepresented, and erroneous nerve-to-nerve connections were tracked. The effects of spatial resolution and the varying degrees of partial volume averaging of complex fiber architecture on the performance of these methods could be investigated. Errors made by the tractography algorithm at high resolution were shown to increase at lower resolutions closer to those used in vivo. This study shows that increases in resolution, made possible by higher field strengths, improve the accuracy of DTI-based tractography. More generally, post-mortem investigation of fixed tissue samples with diffusion imaging at high field strengths is important in the evaluation of MR-based diffusion models and tractography algorithms.     

Quantifying collagen structure in breast biopsies using second-harmonic generation imaging

Quantitative second-harmonic generation imaging is employed to assess stromal collagen in normal, hyperplastic, dysplastic, and malignant breast tissues. The cellular scale organization is quantified using Fourier transform-second harmonic generation imaging (FT-SHG), while the molecular scale organization is quantified using polarization-resolved second-harmonic generation measurements (P-SHG). In the case of FT-SHG, we apply a parameter that quantifies the regularity in collagen fiber orientation and find that malignant tissue contains locally aligned fibers compared to other tissue conditions. Alternatively, using P-SHG we calculate the ratio of tensor elements (d₁₅/d₃₁, d₂₂/d₃₁, and d₃₃/d₃₁) of the second-order susceptibility χ² for collagen fibers in breast biopsies. In particular, d₁₅/d₃₁ shows potential differences across the tissue pathology. We also find that trigonal symmetry (3m) is a more appropriate model to describe collagen fibers in malignant tissues as opposed to the conventionally used hexagonal symmetry (C6). This novel method of targeting collagen fibers using a combination of two quantitative SHG techniques, FT-SHG and P-SHG, holds promise for breast tissue analysis and applications to characterizing cancer in a manner that is compatible with clinical practice.OCIS codes: (180.4315) Nonlinear microscopy, (100.2960) Image analysis     

磁共振扩散张量成像诊断前列腺癌的理论基础

前列腺癌是威胁老年男性健康的重要疾病,MRI目前被一致认为是前列腺疾病理想的检查方法。MR扩散张量成像(DTI)通过在多个方向施加扩散敏感梯度而测量水分子扩散程度的方向性,从而提供组织微观结构的信息;可以弥补常规磁共振诊断前列腺疾病的不足。该文对DTI诊断前列腺癌理论基础进行综述。