Diffusion tensor imaging to study anisotropy in a particular porous system: the trabecular bone network

During the last decade, considerable effort has been invested into the development of diffusion tensor imaging (DTI) mainly used to investigate cerebral morphology. The aim of this paper is to review and to discuss our recent results about high magnetic field DTI application to study spongy bone tissue. Due to its peculiar properties, spongy bone represents a particular porous system sample. Strategies to perform DTI on porous systems and issues linked to DTI outcome interpretation are presented on the basis of our results concerning trabecular bone network characterization.     

磁共振扩散张量成像中扩散敏感梯度磁场方向分布方案的研究进展

磁共振扩散张量成像可以定量无创研究人体内水分子在三维空间中的各向异性扩散规律,进而获取重要的病理及生理信息.为了得到水分子各向异性扩散信息,需要按照一定的方案依次施加不同方向的扩散敏感梯度磁场,测量水分子在这些方向上的扩散系数用以估算扩散张量.扩散张量成像测量结果的准确程度受梯度磁场方向分布方案的影响,本文对扩散敏感梯度磁场方向分布方案进行综述,包括完全随机方案、启发式方案、规则多面体式方案和数值优化方案等,分析这些方案的优势与局限性,并提出需进一步研究的问题.     

Repeatability of echo-planar-based diffusion measurements of the human prostate at 3 T

Echo-planar-based diffusion-weighted imaging (DWI) of the prostate is increasingly being suggested as a viable technique, complementing information derived from conventional magnetic resonance imaging methods for use in tissue discrimination. DWI has also been suggested as a potentially useful tool in the assessment of tumor response to treatment. In this study, the repeatability of apparent diffusion coefficient (ADC) values obtained from both DWI and diffusion tensor imaging (DTI) has been assessed as a precursor to determining the magnitude of treatment-induced changes required for reliable detection. The repeatability values of DWI and DTI were found to be similar, with ADC values repeatable to within 35% or less over a short time period of a few minutes and a longer time period of a month. Fractional anisotropy measurements were found to be less repeatable (between 26% and 71%), and any changes duly recorded in longitudinal studies must therefore be treated with a degree of caution.     

In vivo multiple spin echoes imaging of trabecular bone on a clinical 1.5 T MR scanner

In vivo multiple spin echoes (MSE) images of bone marrow in trabecular bone were obtained for the first time on a clinical 1.5 T scanner. Despite of a reduced sensitivity of the MSE trabecular bone images with respect to the cerebral matter ones, it is possible to observe some features in the MSE trabecular bone images that may be useful in the diagnosis of osteopenic states. Two different CRAZED-type MSE imaging sequences based on spin-echo and EPI imaging modalities were applied in phantom and in vivo. Preliminary experimental results indicate that EPI imaging readout seems to conceal the MSE contrast correlated with pore dimension in porous media. However it is still possible to detect anisotropy effects related to the bone structure in MSE-EPI images. Some strategies are suggested to optimize the quality of MSE trabecular bone images.     

A method for vibrational assessment of cortical bone

Large bones from many anatomical locations of the human skeleton consist of an outer shaft (cortex) surrounding a highly porous internal region (trabecular bone) whose structure is reminiscent of a disordered cubic network. Age related degradation of cortical and trabecular bone takes different forms. Trabecular bone weakens primarily by loss of connectivity of the porous network, and recent studies have shown that vibrational response can be used to obtain reliable estimates for loss of its strength. In contrast, cortical bone degrades via the accumulation of long fractures and changes in the level of mineralization of the bone tissue. In this paper, we model cortical bone by an initially solid specimen with uniform density to which long fractures are introduced; we find that, as in the case of trabecular bone, vibrational assessment provides more reliable estimates of residual strength in cortical bone than is possible using measurements of density or porosity.     

High-resolution in vivo diffusion tensor imaging of the injured cat spinal cord using self-navigated,interleaved, variable-density spiral acquisition (SNAILS-DTI)

Diffusion tensor magnetic resonance imaging (DTI) is useful for studying the microstructural changes in the spinal cord following traumatic injury; however, image quality is generally poor due to the small size of the spinal cord, physiological motion and susceptibility artifacts. Self-navigated, interleaved, variable-density spiral diffusion tensor imaging (SNAILS-DTI) is a distinctive pulse sequence that bypasses many of the challenges associated with DTI of the spinal cord, particularly if imaging gradient hardware is of conventional quality. In the current study, we have demonstrated the feasibility of implementing SNAILS-DTI on a clinical 3.0-T MR scanner and examined the effect of navigator filter parameters on image quality and reconstruction time. Results demonstrate high-quality, high-resolution (546 μm×546 μm) in vivo DTI images of the cat spinal cord after traumatic spinal cord injury.     

White matter changes in multiple sclerosis: correlation of q-space diffusion MRI and 1H MRS

OBJECTIVE: To explore the diagnostic usefulness of high b-value diffusion magnetic resonance brain imaging ("q-space" imaging) in multiple sclerosis (MS). More specifically, we aimed at evaluating the ability of this methodology to identify tissue damage in the so-called normal-appearing white matter (NAWM). DESIGN: In this study we examined the correlation between q-space diffusion imaging and magnetic resonance spectroscopy (MRS)-based two-dimensional 1H chemical shift imaging. Eight MS patients with different degree of disease severity and seven healthy subjects were scanned in a 1.5-T magnetic resonance imaging (MRI) scanner. The MRI protocol included diffusion tensor imaging (DTI) (with bmax of 1000 s/mm2), high b-value diffusion-weighted imaging (with bmax of 14,000 s/mm2) and 2D chemical shift imaging. The high b-value data set was analyzed using the q-space methodology to produce apparent displacement and probability maps. RESULTS: We found that the q-space diffusion displacement and probability image intensities correlated well with N-acetylaspartate levels (r=.61 and .54, respectively). Furthermore, NAWM that was abnormal on MRS was also found to be abnormal using q-space diffusion imaging. In these areas, the q-space displacement values increased from 3.8+/-0.2 to 4.6+/-0.6 microm (P<.02), the q-space probability values decreased from 7.4+/-0.3 to 6.8+/-0.3 (P<.002), while DTI revealed only a small, but still significant, reduction in fractional anisotropy values from 0.40+/-0.02 to 0.37+/-0.02 (P<.05). CONCLUSION: High b-value diffusion imaging can detect tissue damage in the NAWM of MS patients. Despite the theoretical limitation of this method, in practice it provides additional information which is clinically relevant for detection of tissue damage not seen in conventional imaging techniques.     

Diffusion tensor encoding schemes optimized for white matter fibers with selected orientations

A method to produce gradient encoding schemes that minimize the noise of diffusion tensor imaging (DTI) indices for selected fiber orientations has been developed. The accuracy of DTI measurements depends on the gradient encoding scheme used. Most current acquisition schemes contain diffusion directions uniformly distributed in 3D space in order to provide equal noise levels for fibers in any orientation. However, when considering specific fiber bundles such as the corticospinal tract (CST) or parts of fiber bundles, the range of fiber orientations of interest may be limited. We hypothesized that, when studying fiber tracts with a limited range of orientations, measuring diffusion in directions that are uniformly distributed in 3D space may be suboptimal for the noise levels of various DTI indices. Therefore, we first used simulations to determine six diffusion directions that minimize the noise of DTI measurements for selected fiber orientations. The resulting optimized set of directions was then tested on the right CST of a healthy human subject, and its performance was compared with that of conventional acquisition strategies. Both the simulations and the experiments on the human subject demonstrated that the new scheme significantly reduced the standard deviation of DTI indices for tensors with primary eigenvectors within a selected range of orientations.     

Effect of cerebrovascular changes on brain DTI quantitation: a hypercapnia study

Quantitative diffusion tensor imaging (DTI) offers a valuable tool to probe the microstructural changes in neural tissues in vivo, where absolute quantitation accuracy and reproducibility are essential. It has been long recognized that measurement of apparent diffusion coefficient (ADC) using DTI could be influenced by the presence of water molecules in cerebrovasculature. However, little is known about to what extent such blood signal affects DTI quantitation. In this study, we quantitatively examined the effect of cerebral hemodynamic change on DTI indices by using a standard multislice echo planar imaging (EPI) spin echo (SE) DTI acquisition protocol and a rat model of hypercapnia. In response to 5% CO(2) challenge, mean, radial and axial diffusivities measured with diffusion factor (b-value) of b=1.0 ms/μm(2) were found to increase in whole brain (1.52%±0.22%, 1.66%±0.16% and 1.35%±0.37%, respectively), gray matter (1.56%±0.23%, 1.63%±0.14% and 1.47%±0.45%, respectively) and white matter regions (1.45%±0.28%, 1.88%±0.33% and 1.10%±0.26%, respectively). Fractional anisotropy (FA) was found to decrease by 1.67%±0.38%, 1.91%±0.59% and 1.46%±0.30% in whole brain, gray matter and white matter regions, respectively. In addition, these diffusivity increases and FA decreases became more pronounced at a lower b-value (b=0.3 ms/μm(2)). The results indicated that in vivo DTI quantitation in brain can be contaminated by vascular factors on the order of few percentages. Consequently, alterations in cerebrovasculature and hemodynamics can affect the DTI quantitation and its efficacy in characterizing the neural tissue microstructures in normal and diseased states. Caution should be taken in designing and interpreting quantitative DTI studies as all DTI indices can be potentially confounded by physiologic conditions and by cerebrovascular and hemodynamic characteristics.     

Brain tissue classification based on DTI using an improved Fuzzy C-means algorithm with spatial constraints

We present an effective method for brain tissue classification based on diffusion tensor imaging (DTI) data. The method accounts for two main DTI segmentation obstacles: random noise and magnetic field inhomogeneities. In the proposed method, DTI parametric maps were used to resolve intensity inhomogeneities of brain tissue segmentation because they could provide complementary information for tissues and define accurate tissue maps. An improved fuzzy c-means with spatial constraints proposal was used to enhance the noise and artifact robustness of DTI segmentation. Fuzzy c-means clustering with spatial constraints (FCM_S) could effectively segment images corrupted by noise, outliers, and other imaging artifacts. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to the exploitation of spatial contextual information. We proposed an improved FCM_S applied on DTI parametric maps, which explores the mean and covariance of the feature spatial information for automated segmentation of DTI. The experiments on synthetic images and real-world datasets showed that our proposed algorithms, especially with new spatial constraints, were more effective.     

Reduced field-of-view DTI segmentation of cervical spine tissue

The number of diffusion tensor imaging (DTI) studies regarding the human spine has considerably increased and it is challenging because of the spine’s small size and artifacts associated with the most commonly used clinical imaging method. A novel segmentation method based on the reduced field-of-view (rFOV) DTI dataset is presented in cervical spinal canal cerebrospinal fluid, spinal cord grey matter and white matter classification in both healthy volunteers and patients with neuromyelitis optica (NMO) and multiple sclerosis (MS). Due to each channel based on high resolution rFOV DTI images providing complementary information on spinal tissue segmentation, we want to choose a different contribution map from multiple channel images. Via principal component analysis (PCA) and a hybrid diffusion filter with a continuous switch applied on fourteen channel features, eigen maps can be obtained and used for tissue segmentation based on the Bayesian discrimination method. Relative to segmentation by a pair of expert readers, all of the automated segmentation results in the experiment fall in the good segmentation area and performed well, giving an average segmentation accuracy of about 0.852 for cervical spinal cord grey matter in terms of volume overlap. Furthermore, this has important applications in defining more accurate human spinal cord tissue maps when fusing structural data with diffusion data. rFOV DTI and the proposed automatic segmentation outperform traditional manual segmentation methods in classifying MR cervical spinal images and might be potentially helpful for detecting cervical spine diseases in NMO and MS.     

Keyhole and zero-padding approaches for reduced-encoding diffusion tensor imaging of the mouse brains

Keyhole diffusion tensor imaging (keyhole DTI) was previously proposed in cardiac imaging to reconstruct DTI maps from the reduced phase-encoding images. To evaluate the feasibility of keyhole DTI in brain imaging, keyhole and zero-padding DTI algorithms were employed on in vivo mouse brain. The reduced phase-encoding portion, also termed as the sharing rate, was varied from 50% to 90% of the full k-space. Our data showed that zero-padding DTI resulted in decreased fractional anisotropy (FA) and decreased mean apparent diffusion coefficient (mean ADC) in white matter (WM) regions. Keyhole DTI showed a better edge preservation on mean ADC maps but not on FA maps as compared to the zero-padding DTI. When increasing the sharing rate in keyhole approach, an underestimation of FA and an over- or underestimation of mean ADC were measured in WM depending on the selected reference image. The inconsistency of keyhole DTI may add a challenge for the wide use of this modality. However, with a carefully selected directive diffusion-weighted image to serve as the reference image in the keyhole approach, this study demonstrated that one may obtain DTI indices of reduced-encoding images with high consistency to those derived with full k-space DTI.     

扩散张量成像的人脑模板构建

扩散张量脑模板包含丰富的大脑白质组织信息,在空间标准化或者脑图谱创建中具有重要价值,然而基于扩散张量模型构建的脑模板精度不高,特别是在脑部复杂的神经元微观结构区域中应用受到限制.针对这一问题,研究者们提出了基于高分辨率扩散成像构建大脑模板的方法.本文对使用扩散张量成像方法进行脑模板构建的研究进展进行了综述,首先介绍了扩散张量脑模板构建的发展进程,阐述了脑模板构建中解决的技术问题及同时存在的局限性;接着详细论述了基于扩散频谱成像及高角度分辨率扩散成像构建脑模板的不同方法间的差异,并总结了这些研究方法取得的重要进展;最后通过分析目前研究进展提出该研究问题中存在的不足以及未来的发展趋势.     

MR microscopy of water diffusion tensor in biological systems

Diffusion tensor imaging (DTI) of the control and mechanically injured spinal cord of a ratin vitro andin vivo are reported.In vitro experiments were done on a home-built 6.4 T magnetic resonance microscope. Results for formaline-fixed samples show significant differences in diffusion tensor components between gray matter and white matter of the control spinal cord. Moreover, it is shown that already 6–10 min after the injury DTI can detect changes in water diffusion in areas extending far beyond the region of primary tissue damage.In vivo experiments were performed using a 9.4 T Magnex magnet and Bruker Medspec imaging system. Good-quality DTI images, free from motion artifacts were obtained. Results from control samples confirm differences in water diffusion between white matter and gray matter, observedin vitro. In vivo experiments show that characteristic changes in water diffusion observedin vitro 6–10 min after injury are preserved 60–360 min after injury, without significant alteration during this time.     

Evaluation of Disease Stage of Radiation-Induced Brain Injury: a DTI Study with Pathological Correlation

The aim of this study is to evaluate if diffusion tensor imaging (DTI) can distinguish the disease process of radiation-induced brain injury when combined with apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values. Twenty-one rabbits received irradiation of 100 Gy in the right brain hemisphere. Twelve rabbits were screened with magnetic resonance imaging (MRI) and DTI before radiation, and imaged at every week until week 9 following radiation. The rabbits that had MRI were euthanized at week 9 for histologic evaluation, while other nine rabbits without MRI were randomly killed for histologic evaluation at weeks 2, 4 and 6, respectively. From the DTI, the ADC and FA values were measured, and rADC and rFA were calculated. After radiation, the trend of the ADC value can be divided into three stages. In the first stage, the ADC value of the target tissues gradually decreased. In the second stage, the ADC value of white matter in the target tissues showed a recovery trend, back to the initial level similar to that in contralateral. In the third stage, the ADC value of white matter in the target tissue continues to increase over the ADC value of baseline and contralateral white matter. The FA value of radiation-targeted area showed continuous decreasing tendency. Pathological evaluation showed the different features in three stages. DTI can distinguish the different disease stages when combined with the ADC and FA values.     

Diffusion tensor imaging of native and degenerated human articular cartilage

Diffusion tensor imaging (DTI) is potentially sensitive to collagen degeneration in cartilage. In this study, DTI was measured on human cartilage samples with interventions of trypsin and collagenase. The measured preferred diffusion direction was consistent with the zonal structure of collagen network. The glycosaminoglycan concentration decreased and apparent diffusion coefficient increased with both interventions. The fractional anisotropy (FA) was not affected by trypsin and showed a slight increase with combined trypsin and collagenase intervention. DTI in cartilage is technically challenging due to the low FA and the almost undetectable change with collagen disruption seen here.     

Crossing fibers, diffractions and nonhomogeneous magnetic field: correction of artifacts by bipolar gradient pulses

In recent years, diffusion tensor imaging (DTI) and its variants have been used to describe fiber orientations and q-space diffusion MR was proposed as a means to obtain structural information on a micron scale. Therefore, there is an increasing need for complex phantoms with predictable microcharacteristics to challenge different indices extracted from the different diffusion MR techniques used. The present study examines the effect of diffusion pulse sequence on the signal decay and diffraction patterns observed in q-space diffusion MR performed on micron-scale phantoms of different geometries and homogeneities. We evaluated the effect of the pulse gradient stimulated-echo, the longitudinal eddy current delay (LED) and the bipolar LED (BPLED) pulse sequences. Interestingly, in the less homogeneous samples, the expected diffraction patterns were observed only when diffusion was measured with the BPLED sequence. We demonstrated the correction ability of bipolar diffusion gradients and showed that more accurate physical parameters are obtained when such a diffusion gradient scheme is used. These results suggest that bipolar gradient pulses may result in more accurate data if incorporated into conventional diffusion-weighted imaging and DTI.     

Optimal real-time estimation in diffusion tensor imaging

Diffusion tensor imaging (DTI) constitutes the most used paradigm among the diffusion-weighted magnetic resonance imaging (DW-MRI) techniques due to its simplicity and application potential. Recently, real-time estimation in DW-MRI has deserved special attention, with several proposals aiming at the estimation of meaningful diffusion parameters during the repetition time of the acquisition sequence. Specifically focusing on DTI, the underlying model of the noise present in the acquired data is not taken into account, leading to a suboptimal estimation of the diffusion tensor. In this paper, we propose an optimal real-time estimation framework for DTI reconstruction in single-coil acquisitions. By including an online estimation of the time-changing noise variance associated to the acquisition process, the proposed method achieves the sequential best linear unbiased estimator. Results on both synthetic and real data show that our method outperforms those so far proposed, reaching the best performance of the existing proposals by processing a substantially lower number of diffusion images.