扩散张量成像导出量对扩散时间依赖关系的实验研究

目的研究扩散张量导出量与扩散时间的关系。方法保持扩散敏感梯度磁场强度不变,使用8个不同的扩散时间对11名被试者进行扩散张量成像扫描得到脑部的各向异性与各向同性信息,计算出各个感兴趣区的平均扩散率与各向异性分数后进行比较。结果不同扩散时间对应的平均扩散率有显著性差异（P〈0．05）,而各向异性分数无显著性差异（P〉0．05）。结论扩散时间对扩散张量成像导出量中的各向异性分数无影响,对平均扩散率有影响。原因是细胞内外水分子扩散性质不同。     

The tensor distribution function

Diffusion weighted magnetic resonance imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion‐sensitized gradients along a minimum of six directions, second‐order tensors (represented by three‐by‐three positive definite matrices) can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve more complicated white matter configurations, e.g., crossing fiber tracts. Recently, a number of high‐angular resolution schemes with more than six gradient directions have been employed to address this issue. In this article, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Using the calculus of variations, we solve the TDF that optimally describes the observed data. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function. Moreover, a tensor orientation distribution function (TOD) may also be derived from the TDF, allowing for the estimation of principal fiber directions and their corresponding eigenvalues. Magn Reson Med 61:205–214, 2009. © 2008 Wiley‐Liss, Inc.     

Echo-Planar Diffusion Spectroscopic Imaging

High-speed diffusion spectroscopic imaging based on an echo-planar technique is presented. A pair of diffusion gradients is applied prior to a rapidly oscillating magnetic field gradient which encodes both chemical shift and spatial information. By applying this technique to a phantom consisting of acetone and water, a diffusion spectroscopic image is obtained in about 15 min, about 64 times faster than the time required in the conventional method. The measured diffusion coefficients show good agreement with previously reported values. This kind of diffusion spectroscopic imaging is expected to provide a way to observe more specific metabolism.     

Q-ball imaging.

Magnetic resonance diffusion tensor imaging (DTI) provides a powerful tool for mapping neural histoarchitecture in vivo. However, DTI can only resolve a single fiber orientation within each imaging voxel due to the constraints of the tensor model. For example, DTI cannot resolve fibers crossing, bending, or twisting within an individual voxel. Intravoxel fiber crossing can be resolved using q-space diffusion imaging, but q-space imaging requires large pulsed field gradients and time-intensive sampling. It is also possible to resolve intravoxel fiber crossing using mixture model decomposition of the high angular resolution diffusion imaging (HARDI) signal, but mixture modeling requires a model of the underlying diffusion process.Recently, it has been shown that the HARDI signal can be reconstructed model-independently using a spherical tomographic inversion called the Funk-Radon transform, also known as the spherical Radon transform. The resulting imaging method, termed q-ball imaging, can resolve multiple intravoxel fiber orientations and does not require any assumptions on the diffusion process such as Gaussianity or multi-Gaussianity. The present paper reviews the theory of q-ball imaging and describes a simple linear matrix formulation for the q-ball reconstruction based on spherical radial basis function interpolation. Open aspects of the q-ball reconstruction algorithm are discussed.     

Effects of osmotically driven cell volume changes on diffusion-weighted imaging of the rat optic nerve

The apparent diffusion coefficient (ADC) of the rat optic nerve was measured In vitro, using magnetic resonance imaging, to determine the effects of changes in cellular volume fraction on the diffusion of tissue water. Nerve ADC was determined under conditions of cell membrane depolarization and (i) increased intracellular volume, (ii) decreased intracellular volume, and (iii) negligible volume change. Depolarization alone had little affect on ADC, whereas volume changes produced strong, reversible effects. Increased cell volume decreased ADC and vice versa. These results are consistent with the view that changes in the extracellular space are the major source of ADC changes in brain tissue.     

In vivo generalized diffusion tensor imaging (GDTI) using higher‐order tensors (HOT)

Generalized diffusion tensor imaging (GDTI) using higher‐order tensor (HOT) statistics generalizes the technique of diffusion tensor imaging by including the effect of nongaussian diffusion on the signal of MRI. In GDTI‐HOT, the effect of nongaussian diffusion is characterized by higher‐order tensor statistics (i.e., the cumulant tensors or the moment tensors), such as the covariance matrix (the second‐order cumulant tensor), the skewness tensor (the third‐order cumulant tensor), and the kurtosis tensor (the fourth‐order cumulant tensor). Previously, Monte Carlo simulations have been applied to verify the validity of this technique in reconstructing complicated fiber structures. However, no in vivo implementation of GDTI‐HOT has been reported. The primary goal of this study is to establish GDTI‐HOT as a feasible in vivo technique for imaging nongaussian diffusion. We show that probability distribution function of the molecular diffusion process can be measured in vivo with GDTI‐HOT and be visualized with three‐dimensional glyphs. By comparing GDTI‐HOT to fiber structures that are revealed by the highest resolution diffusion‐weighted imaging (DWI) possible in vivo, we show that the GDTI‐HOT can accurately predict multiple fiber orientations within one white matter voxel. Furthermore, through bootstrap analysis we demonstrate that in vivo measurement of HOT elements is reproducible, with a small statistical variation that is similar to that of diffusion tensor imaging. Magn Reson Med, 2010. © 2009 Wiley‐Liss, Inc.     

Tumor extension in high-grade gliomas assessed with diffusion magnetic resonance imaging: values and lesion-to-brain ratios of apparent diffusion coefficient and fractional anisotropy

Purpose: To determine whether the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) can distinguish tumor-infiltrated edema in gliomas from pure edema in meningiomas and metastases.

Material and Methods: Thirty patients were studied: 18 WHO grade III or IV gliomas, 7 meningiomas, and 5 metastatic lesions. ADC and FA were determined from ROIs placed in peritumoral areas with T2-signal changes, adjacent normal appearing white matter (NAWM), and corresponding areas in the contralateral healthy brain. Values and lesion-to-brain ratios from gliomas were compared to those from meningiomas and metastases.

Results: Values and lesion-to-brain ratios of ADC and FA in peritumoral areas with T2-signal changes did not differ between gliomas, meningiomas, and metastases (P = 0.40, P = 0.40, P = 0.61, P = 0.34). Values of ADC and FA and the lesion-to-brain ratio of FA in the adjacent NAWM did not differ between tumor types (P = 0.74, P = 0.25, and P = 0.31). The lesion-to-brain ratio of ADC in the adjacent NAWM was higher in gliomas than in meningiomas and metastases (P = 0.004), but overlapped between tumor types.

Conclusion: Values and lesion-to-brain ratios of ADC and FA in areas with T2-signal changes surrounding intracranial tumors and adjacent NAWM were not helpful for distinguishing pure edema from tumor-infiltrated edema when data from gliomas, meningiomas, and metastases were compared.     

Slab scan diffusion imaging.

For maximum robustness of a diffusion-weighted MR imaging sequence, it is desirable to use a single-shot imaging method. This article introduces a new single-shot imaging approach that combines the advantages of multiple spin-echoes with the technique of line scan diffusion imaging. A slab volume, which can be spatially encoded with fewer phase encodes than a regular field of view, is selected with 2D selective pulses. With the shorter echo train, the sensitivity to field inhomogeneities and chemical shift is thus greatly diminished. Further reduction is achieved by interleaving short gradient echo trains with refocusing spin-echo pulses. Optimized slice-selective RF pulses that produce flip angles close to 180 degrees are used to minimize the stimulated echo component. Motion-related phase shifts, which change polarity with each spin-echo excitation, will give rise to artifacts that are avoidable by processing even and odd spin-echoes separately. As with line scan diffusion imaging, the complete field of view is acquired by sequential scanning. Since with each shot several lines of data are collected, a considerable improvement over line scan diffusion imaging in terms of scanning speed is achieved. Diffusion data obtained in phantoms and normal subjects demonstrate the feasibility of this novel approach.     

内分泌治疗后前列腺外周带癌区和非癌区ADC值变化初步研究

目的运用MR扩散加权成像研究内分泌治疗前后前列腺外周带癌区和非癌区的ADC值变化情况。方法28例B超引导下经直肠穿刺活检病理证实的前列腺外周带癌病人。根据穿刺活检结果,将前列腺外周带归类为癌区和非癌区。所有病人在内分泌治疗前和治疗后3~6个月内均进行了单次激发EPI序列的MR扩散加权成像检查,计算各个分区内的ADC平均值,并对所得数值进行配对或独立样本t检验。结果治疗前,所有非癌区(107个)和癌区(61个)的ADC平均值分别为(2.21±0.61)×10-3mm2/s(mean±SD)和(1.65±0.46)×10-3mm2/s,2组间具有统计学差异(t=4.36,P=0.039,独立样本t检验)。经过了3~6个月的内分泌治疗后,非癌区和癌区的ADC值均出现了下降,非癌区ADC值下降至(1.33±0.48)×10-3mm2/s,癌区下降至(1.28±0.53)×10-3mm2/s,均与内分泌治疗前该组区域的ADC值有统计学差异(t=5.28,P=0.024和t=7.39,P=0.015,配对样本t检验)。但是在治疗后癌区和非癌区ADC值之间原有的统计学差异消失(t=0.58,P=0.639,独立样本t检验)。结论内分泌治疗后前列腺外周带癌区和非癌区的ADC值均会出现下降,其中以非癌区下降为明显,致使癌区和非癌区原有的ADC值统计学差异消失。     

Boosting the sampling efficiency of q-Ball imaging using multiple wavevector fusion.

q-Ball imaging (QBI) is a high-angular-resolution diffusion imaging (HARDI) method that is capable of resolving complex, subvoxel white matter (WM) architecture. QBI requires time-intensive sampling of the diffusion signal and large diffusion wavevectors. Here we describe a reconstruction scheme for QBI, termed multiple wavevector fusion (MWF), that substantially boosts the sampling efficiency and signal-to-noise ratio (SNR) of QBI. The MWF reconstruction operates by nonlinearly fusing the diffusion signal from separate low and high wavevector acquisitions. The combination of wavevectors provides the benefits of the high SNR of the low wavevector signal and the high angular contrast-to-noise ratio (CNR) and peak separation of the high wavevector signal. The MWF procedure provides a framework for combining diffusion tensor imaging (DTI) and QBI. Numerical simulations show that MWF of DTI and QBI provides a more accurate estimate of the diffusion orientation distribution function (ODF) than QBI alone. The accuracy improvement can be translated into an efficiency gain of 274-377%. An intravoxel peak connectivity metric (IPCM) is presented that calculates the peak connectivity between an ODF and its neighboring voxels. In human WM, MWF reveals more detailed WM architecture than QBI as measured by the IPCM for all sampling schemes presented.