利用新型扩散成像技术研究年老对大脑微观结构的影响

了解年老过程中大脑在细胞水平上发生的变化对于揭示老年人认知功能下降的原因有重要意义。扩散MRI(diffusion MRI,d MRI)技术是目前惟一可以无创探查活体组织微观结构的方法。扩散张量成像(DTI,diffusion tensor imaging)是临床上最常用的一种d MRI技术,但是由于某些固有缺陷,它不能充分刻画大脑组织的微观结构。作者介绍三种可以有效弥补DTI不足的新型扩散成像方法:扩散峰度成像(diffusion kurtosis imaging,DKI),扩散的受阻受限合成模型(composite hindered and restricted model of diffusion,CHARMED)和神经突方向离散度与密度成像(neurite orientation dispersion and density imaging,NODDI)。联合使用DTI和这些新技术,研究者可以更深入地了解年老如何影响大脑的微观结构。     

In vivo mapping of brain myo-inositol

Myo-Inositol (MI) is one of the most abundant metabolites in the human brain located mainly in glial cells and functions as an osmolyte. The concentration of MI is altered in many brain disorders including Alzheimer's disease and brain tumors. Currently available magnetic resonance spectroscopy (MRS) methods for measuring MI are limited to low spatial resolution. Here, we demonstrate that the hydroxyl protons on MI exhibit chemical exchange with bulk water and saturation of these protons leads to reduction in bulk water signal through a mechanism known as chemical exchange saturation transfer (CEST). The hydroxyl proton exchange rate (k=600 s(-1)) is determined to be in the slow to intermediate exchange regime on the NMR time scale (chemical shift (?ω)>k), suggesting that the CEST effect of MI (MICEST) can be imaged at high fields such as 7 T (?ω=1.2×10(3)rad/s) and 9.4 T (?ω=1.6×10(3) rad/s). Using optimized imaging parameters, concentration dependent broad CEST asymmetry between ~0.2 and 1.5 ppm with a peak at ~0.6 ppm from bulk water was observed. Further, it is demonstrated that MICEST detection is feasible in the human brain at ultra high fields (7 T) without exceeding the allowed limits on radiofrequency specific absorption rate. Results from healthy human volunteers (N=5) showed significantly higher (p=0.03) MICEST effect from white matter (5.2±0.5%) compared to gray matter (4.3±0.5%). The mean coefficient of variations for intra-subject MICEST contrast in WM and GM were 0.49 and 0.58 respectively. Potential overlap of CEST signals from other brain metabolites with the observed MICEST map is discussed. This noninvasive approach potentially opens the way to image MI in vivo and to monitor its alteration in many disease conditions.     

Age- and gender-related changes in the normal human brain using hybrid diffusion imaging (HYDI)

Diffusion tensor imaging has been widely used to study brain diseases, disorders, development, and aging. However, few studies have explored the effects of aging on diffusion imaging measures with higher b values. Further, the water diffusion in biological tissues appears biexponential, although this also has not been explored with aging. In this study, hybrid diffusion imaging (HYDI) was used to study 52 healthy subjects with an age range from 18 to 72 years. The HYDI diffusion-encoding scheme consisted of five concentric q-space shells with b values ranging from 0 to 9375 s/mm(2). Quantitative diffusion measures were investigated as a function of age and gender using both region-of-interest (whole-brain white matter, genu and splenium of corpus callosum, posterior limb of the internal capsule) and whole-brain voxel-based analyses. Diffusion measures included measures of the diffusion probability density function (zero displacement probability and mean-squared displacement), biexponential diffusion (i.e., volume fractions of fast/slow diffusion compartments and fast/slow diffusivities), and DTI measures (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity). The biexponential volume fraction, the fast diffusivity, and the axial diffusivity measures (f(1), D(1), and D(a)) were found to be more sensitive to normal aging than the restricted, slow and radial diffusion measures (P(0), D(2), and D(r)). The biexponential volume fraction, f(1), showed the most widespread age dependence in the voxel-based analyses, although both FA and mean diffusivity did show changes in frontal white matter regions that may be associated with age-related decline.     

Alveolar-capillary membrane dysfunction in chronic heart failure: pathophysiology and therapeutic implications

Chronic heart failure (CHF) disturbs the alveolar-capillary interface and increases the resistance to gas transfer. Alveolar-capillary membrane conductance (D(M)) and capillary blood volume (V(c)) are subcomponents of the lung diffusion capacity. Elevation of the capillary pressure causes alveolar-capillary membrane stress failure (i.e. increase in capillary permeability to water and ions, and disruption of local regulatory mechanisms for gas exchange), leading to a decrease in D(M), an increase in V(c) and subsequent impairment of diffusion capacity. Renewed recent interest in abnormalities in lung diffusion in patients with CHF has brought about new pathophysiological insights. A significant contribution of the altered gas transfer to the pathogenesis of exercise limitation and ventilatory abnormalities has been reported, and D(M) has been identified as the best lung function predictor of oxygen uptake at peak exercise. This review examines the pathophysiological and clinical significance of assessing lung diffusion capacity in patients with CHF.     

Fractal analysis of spontaneous fluctuations of the BOLD signal in rat brain

Analysis of task-evoked fMRI data ignores low frequency fluctuations (LFF) of the resting-state the BOLD signal, yet LFF of the spontaneous BOLD signal is crucial for analysis of resting-state connectivity maps. We characterized the LFF of resting-state BOLD signal at 11.7T in α-chloralose and domitor anesthetized rat brain and modeled the spontaneous signal as a scale-free (i.e., fractal) distribution of amplitude power (|A|2) across a frequency range (f) compatible with an |A(f)|2 ∝ 1/f(β) model where β is the scaling exponent (or spectral index). We compared β values from somatosensory forelimb area (S1FL), cingulate cortex (CG), and caudate putamen (CPu). With α-chloralose, S1FL and CG β values dropped from ~0.7 at in vivo to ~0.1 at post mortem (p<0.0002), whereas CPu β values dropped from ~0.3 at in vivo to ~0.1 at post mortem (p<0.002). With domitor, cortical (S1FL, CG) β values were slightly higher than with α-chloralose, while subcortical (CPu) β values were similar with α-chloralose. Although cortical and subcortical β values with both anesthetics were significantly different in vivo (p<0.002), at post mortem β values in these regions were not significantly different and approached zero (i.e., range of -0.1 to 0.2). Since a water phantom devoid of susceptibility gradients had a β value of zero (i.e., random), we conclude that deoxyhemoglobin present in voxels post-sacrifice still impacts tissue water diffusion. These results suggest that in the anesthetized rat brain the LFF of BOLD signal at 11.7T follow a general 1/f(β) model of fractality where β is a variable responding to physiology. We describe typical experimental pitfalls which may elude detection of fractality in the resting-state BOLD signal.     

Cultured skin loaded with tetracycline HCl and chloramphenicol as dermal delivery system: mathematical evaluation of the cultured skin containing antibiotics.

Dermal patches consisting of cultured human skin with antibiotics, which have a protective effect on wound skin as well as a preventative effect on second infection of the skin, were prepared and mathematically analyzed as a new drug delivery system (DDS) that can be applied to serious skin defects such as severe burns. In the present study, a three-dimensional cultured human skin model (living skin equivalent-high, LSE-high) was used as a cultured skin membrane and tetracycline HCl (TC-HCl) and chloramphenicol (CP) were used as antibiotics. At first, antibiotics were entrapped in the LSE-high from the dermal side through culture medium in order to obtain a drug-loaded LSE-high. The antibiotic release from the drug-loaded LSE-high was then examined and the resulting release data were used to calculate the effective diffusion coefficient of the antibiotics (D(LSE)) and initial loading concentration of the antibiotics (C0) in the LSE-high. The release profile of TC-HCl was represented by general diffusion-limited kinetics, whereas an initial burst effect was found in the release profile of CP. Therefore, the burst effect was taken into account for analyzing the release profile of CP. Stripped skin excised from hairless rats was used as a wound model, and the antibiotic permeation through the skin from aqueous solution was examined and evaluated using differential equations for Fick's second law of diffusion to obtain the effective diffusion coefficient of the antibiotics in the wound skin (D(skin)). Furthermore, the antibiotic permeation profile through the excised stripped skin from the drug-loaded LSE-high was measured and theoretically evaluated by Fick's second law of diffusion with previously obtained parameters (C0, D(LSE), D(skin)) using a newly constructed two- or three-layered diffusion model. The calculated concentrations of TC-HCl and CP in the upper epidermis of the model wound skin were over their minimum inhibitory concentration (MIC) for several hours against various bacteria, suggesting that this dosage system is useful for the treatment of severe burns. In addition, the present analytical method and diffusion model, with the drug-loaded LSE-high and stripped rat skin, are useful tools for evaluating this new DDS.     

Electroporation and transcutaneous extraction (ETE) for pharmacokinetic studies of drugs.

The therapeutic activity and toxicity of drugs often depends on the accumulation of drugs in the peripheral anatomical compartment rather than the central compartment. In the routine practice of therapeutic drug monitoring (TDM) and pharmacokinetic studies, drug concentration determined by intermittent blood sampling is used as a surrogate for calculating the drug concentration in the peripheral compartment tissues. Microdialysis, a relatively less invasive procedure, has been used for estimation of free drug levels in dermal, subcutaneous and muscle tissues. Transcutaneous extraction of drugs from the dermal tissue is a good noninvasive alternative to phlebotomy and microdialysis. This requires a technique, which can facilitate the extraction of significant and reproducible amounts of drugs from the dermal extracellular fluid (ECF) within a short sampling duration. In the present work, we assessed the feasibility of electroporation and transcutaneous extraction (ETE) method for determining the time course of drugs in dermal ECF, using salicylic acid (SA) as a test drug. Electroporation protocol was optimized based on the in vitro diffusion studies of salicylic acid across rat skin. The concentration-time profile of total SA was determined in rats after a single i.v. bolus administration. The in vivo permeability coefficient (P(in vivo)) of rat skin was determined under steady state plasma concentration of drug created by i.v. bolus followed by constant rate infusion of SA. The pharmacokinetic parameters of the drug were determined using a two-compartment pharmacokinetic model. The theoretical predicted time course of free SA in the dermal ECF after a single i.v. bolus administration was calculated using standard formulae. The concentration of free SA determined by ETE is in good agreement with that calculated using two-compartment pharmacokinetic model. This study thus provides a credible evidence for the validity of ETE technique for determining the concentration of SA in the dermal ECF.     

Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison

This paper aims to identify the minimum requirements for an accurate model of the diffusion MR signal in white matter of the brain. We construct a taxonomy of multi-compartment models of white matter from combinations of simple models for the intra- and the extra-axonal spaces. We devise a new diffusion MRI protocol that provides measurements with a wide range of imaging parameters for diffusion sensitization both parallel and perpendicular to white matter fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and compare the different models. The study examines a total of 47 analytic models, including several well-used models from the literature, which we place within the taxonomy. The results show that models that incorporate intra-axonal restriction, such as ball and stick or CHARMED, generally explain the data better than those that do not, such as the DT or the biexponential models. However, three-compartment models which account for restriction parallel to the axons and incorporate pore size explain the measurements most accurately. The best fit comes from combining a full diffusion tensor (DT) model of the extra-axonal space with a cylindrical intra-axonal component of single radius and a third spherical compartment of non-zero radius. We also measure the stability of the non-zero radius intra-axonal models and find that single radius intra-axonal models are more stable than gamma distributed radii models with similar fitting performance.     

基于布洛克磁共振流动方程和贝塞尔函数的磁共振成像序列数学设计(英文)

Bloch方程是NMR/MRI计算、模拟和实验的基础,但通常在不加特定的绝热和非绝热条件的前提下获得Bloch流动方程的解析解是非常困难的。流动方程的一般解析解可以为理解NMR/MRI的基本概念提供额外的信息,而又不需要通常的指数方程。作者的目的是通过贝塞尔函数及其特性得到与时间无关的NMR流动方程的解析解。在不需要主观添加弥散项的前提下利用贝塞尔函数及其特性从NMR流动方程中获得了Stejskal-Tanner公式。这证实了弥散是Bloch流动方程的内在属性并可以通过如贝塞尔函数的适当数学函数提取出来。从解析解得到的非高斯行为的弥散信号在如脑白质的各项异性组织环境中是非常有意义的。发现弥散系数是与T1和T2弛豫参数直接相关的,因此通过对大量已有的贝塞尔函数进行合适利用可以在四个分离的缓存内采集MRI信号(实部和虚部,相位和绝对值)。能够利用MRI监测药物对于不同组织尤其是脑部功能活动的效果。     

Tissue microstructure estimation using a deep network inspired by a dictionary-based framework

Diffusion magnetic resonance imaging (dMRI) captures the anisotropic pattern of water displacement in the neuronal tissue and allows noninvasive investigation of the complex tissue microstructure. A number of biophysical models have been proposed to relate the tissue organization with the observed diffusion signals, so that the tissue microstructure can be inferred. The Neurite Orientation Dispersion and Density Imaging (NODDI) model has been a popular choice and has been widely used for many neuroscientific studies. It models the diffusion signal with three compartments that are characterized by distinct diffusion properties, and the parameters in the model describe tissue microstructure. In NODDI, these parameters are estimated in a maximum likelihood framework, where the nonlinear model fitting is computationally intensive. Therefore, efforts have been made to develop efficient and accurate algorithms for NODDI microstructure estimation, which is still an open problem. In this work, we propose a deep network based approach that performs end-to-end estimation of NODDI microstructure, which is named Microstructure Estimation using a Deep Network (MEDN). MEDN comprises two cascaded stages and is motivated by the AMICO algorithm, where the NODDI microstructure estimation is formulated in a dictionary-based framework. The first stage computes the coefficients of the dictionary. It resembles the solution to a sparse reconstruction problem, where the iterative process in conventional estimation approaches is unfolded and truncated, and the weights are learned instead of predetermined by the dictionary. In the second stage, microstructure properties are computed from the output of the first stage, which resembles the weighted sum of normalized dictionary coefficients in AMICO, and the weights are also learned. Because spatial consistency of diffusion signals can be used to reduce the effect of noise, we also propose MEDN+, which is an extended version of MEDN. MEDN+ allows incorporation of neighborhood information by inserting a stage with learned weights before the MEDN structure, where the diffusion signals in the neighborhood of a voxel are processed. The weights in MEDN or MEDN+ are jointly learned from training samples that are acquired with diffusion gradients densely sampling the q-space. We performed MEDN and MEDN+ on brain dMRI scans, where two shells each with 30 gradient directions were used, and measured their accuracy with respect to the gold standard. Results demonstrate that the proposed networks outperform the competing methods.     

White matter characterization with diffusional kurtosis imaging

Diffusional kurtosis imaging (DKI) is a clinically feasible extension of diffusion tensor imaging that probes restricted water diffusion in biological tissues using magnetic resonance imaging. Here we provide a physically meaningful interpretation of DKI metrics in white matter regions consisting of more or less parallel aligned fiber bundles by modeling the tissue as two non-exchanging compartments, the intra-axonal space and extra-axonal space. For the b-values typically used in DKI, the diffusion in each compartment is assumed to be anisotropic Gaussian and characterized by a diffusion tensor. The principal parameters of interest for the model include the intra- and extra-axonal diffusion tensors, the axonal water fraction and the tortuosity of the extra-axonal space. A key feature is that these can be determined directly from the diffusion metrics conventionally obtained with DKI. For three healthy young adults, the model parameters are estimated from the DKI metrics and shown to be consistent with literature values. In addition, as a partial validation of this DKI-based approach, we demonstrate good agreement between the DKI-derived axonal water fraction and the slow diffusion water fraction obtained from standard biexponential fitting to high b-value diffusion data. Combining the proposed WM model with DKI provides a convenient method for the clinical assessment of white matter in health and disease and could potentially provide important information on neurodegenerative disorders.     

Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis

OBJECTIVE: Aim of present study was to evaluate changes in diffusion tensor imaging (DTI) parameters in the whole brain of 28 patients with amyotrophic lateral sclerosis (ALS) compared to 26 healthy controls. METHODS: In both fibertracking and voxel-based analysis, quantitative comparisons of the diffusion parameters between ALS patients and controls were performed. Correlation analyses of diffusion parameters and disease duration and disease severity were performed. A second DTI examination was acquired, allowing the evaluation of the effect of disease progression on the diffusion parameters. RESULTS: Fibertracking analysis revealed that especially the precentral part of the corticospinal tract (CST) was impaired. In the voxel-based analysis, it was shown that changes of diffusion parameters occurred throughout the brain, including frontal, temporal and parietal lobes. Disease severity was inversely correlated with the fractional anisotropy (FA). In the follow-up examination, a further decline of FA over time could be demonstrated in the CST as well as in the whole brain white matter. INTERPRETATION: This study provides support for the view of ALS as being a multisystem degenerative disease, in which abnormalities of extra-motor areas play an important role in the in vivo physiopathology.     

Non-Gaussian diffusion in human brain tissue at high b-factors as examined by a combined diffusion kurtosis and biexponential diffusion tensor analysis

Diffusion tensor imaging (DTI) permits non-invasive probing of tissue microstructure and provides invaluable information in brain diagnostics. Our aim was to examine approaches capable of capturing more detailed information on the propagation mechanisms and underlying tissue microstructure in comparison to the conventional methods. In this work, we report a detailed in vivo diffusion study of the human brain in an extended range of the b-factors (up to 7000 s mm(-2)) performed on a group of 14 healthy volunteers at 3T. Combined diffusion kurtosis imaging (DKI) and biexponential diffusion tensor analysis (BEDTA) were applied to quantify the attenuation curves. New quantitative indices are suggested as map parameters and are shown to improve the underlying structure contrast in comparison to conventional DTI. In particular, fractional anisotropy maps related to the slow diffusion tensor are shown to attain significantly higher values and to substantially improve white matter mapping. This is demonstrated for the specified regions of the frontal and occipital lobes and for the anterior cingulate. The findings of this work are substantiated by the statistical analysis of the whole slice histograms averaged over 14 subjects. Colour-coded directional maps related to the fast and slow diffusion tensors in human brain tissue are constructed for the first time and these demonstrate a high degree of axial co-alignment of the two tensors in the white matter regions. It is concluded that a combined DKI and BEDTA offers a promising framework for monitoring tissue alteration during development and degeneration or as a consequence of the neurological disease.     

Multi-shell diffusion signal recovery from sparse measurements

For accurate estimation of the ensemble average diffusion propagator (EAP), traditional multi-shell diffusion imaging (MSDI) approaches require acquisition of diffusion signals for a range of b-values. However, this makes the acquisition time too long for several types of patients, making it difficult to use in a clinical setting. In this work, we propose a new method for the reconstruction of diffusion signals in the entire q-space from highly undersampled sets of MSDI data, thus reducing the scan time significantly. In particular, to sparsely represent the diffusion signal over multiple q-shells, we propose a novel extension to the framework of spherical ridgelets by accurately modeling the monotonically decreasing radial component of the diffusion signal. Further, we enforce the reconstructed signal to have smooth spatial regularity in the brain, by minimizing the total variation (TV) norm. We combine these requirements into a novel cost function and derive an optimal solution using the Alternating Directions Method of Multipliers (ADMM) algorithm. We use a physical phantom data set with known fiber crossing angle of 45° to determine the optimal number of measurements (gradient directions and b-values) needed for accurate signal recovery. We compare our technique with a state-of-the-art sparse reconstruction method (i.e., the SHORE method of Cheng et al. (2010)) in terms of angular error in estimating the crossing angle, incorrect number of peaks detected, normalized mean squared error in signal recovery as well as error in estimating the return-to-origin probability (RTOP). Finally, we also demonstrate the behavior of the proposed technique on human in vivo data sets. Based on these experiments, we conclude that using the proposed algorithm, at least 60 measurements (spread over three b-value shells) are needed for proper recovery of MSDI data in the entire q-space.     

Axon diameter mapping in the presence of orientation dispersion with diffusion MRI

Direct measurement of tissue microstructure with diffusion MRI offers a new class of markers, such as axon diameters, that give more specific information about tissue than measures derived from diffusion tensor imaging. The existing techniques of this kind assume a single axon orientation in the tissue model, which may be a reasonable approximation only for the most coherently oriented brain white matter, such as the corpus callosum. For most other areas, orientation dispersion is not negligible and, if unaccounted for, leads to overestimation of the axon diameters, prohibiting their accurate mapping over the whole brain. Here we propose a new model that captures the effect of orientation dispersion explicitly. A numerical scheme is developed to compute the diffusion signal prescribed by the proposed model efficiently, which supports the simultaneous estimation of the axon diameter and orientation dispersion. Synthetic data experiments demonstrate that the new model provides an axon diameter index that is robust to the presence of orientation dispersion. Results on in vivo human data show reduced axon diameter index and better agreement with histology compared to previous methods suggesting improvements in the axon diameter estimate.     

Trace of diffusion tensor differentiates the Parkinson variant of multiple system atrophy and Parkinson's disease

We have recently shown that diffusion-weighted magnetic resonance (MR) imaging (DWI) discriminates patients with the Parkinson variant of multiple system atrophy (MSA-P) from those with Parkinson's disease (PD) by regional apparent diffusion coefficients (rADC) in the putamen. Because rADCs measured in one direction may underestimate diffusion-related pathologic processes, we investigated the diffusivity in different brain areas by trace of diffusion tensor (Trace(D)) in a new cohort of patients with MSA-P and PD. We studied 11 MSA-P, 17 PD patients, and 10 healthy volunteers matched for age and disease duration. Regional ADCs in three orthogonal directions and Trace(D) values were determined in selected brain regions including the basal ganglia, gray matter, white matter, substantia nigra, and pons. MSA-P patients had significantly higher putaminal and pallidal rTrace(D) values as well as rADCs in y- and z-direction than both PD patients and healthy volunteers. Moreover, putaminal Trace(D) discriminated completely MSA-P from both PD and healthy volunteers. The rADCs in the y- and z-direction provided a complete or near complete separation. In conclusion, our study confirms the results of previous studies of our group that patients with MSA-P show an increased putaminal diffusivity due to neuronal loss and gliosis. Because rADCs in one direction are dependent on the slice orientation relative to the directions of fiber tracts, Trace(D) imaging appears to be more accurate in the separation of MSA-P from PD.     

Spatially-constrained probability distribution model of incoherent motion (SPIM) for abdominal diffusion-weighted MRI

Quantitative diffusion-weighted MR imaging (DW-MRI) of the body enables characterization of the tissue microenvironment by measuring variations in the mobility of water molecules. The diffusion signal decay model parameters are increasingly used to evaluate various diseases of abdominal organs such as the liver and spleen. However, previous signal decay models (i.e., mono-exponential, bi-exponential intra-voxel incoherent motion (IVIM) and stretched exponential models) only provide insight into the average of the distribution of the signal decay rather than explicitly describe the entire range of diffusion scales. In this work, we propose a probability distribution model of incoherent motion that uses a mixture of Gamma distributions to fully characterize the multi-scale nature of diffusion within a voxel. Further, we improve the robustness of the distribution parameter estimates by integrating spatial homogeneity prior into the probability distribution model of incoherent motion (SPIM) and by using the fusion bootstrap solver (FBM) to estimate the model parameters. We evaluated the improvement in quantitative DW-MRI analysis achieved with the SPIM model in terms of accuracy, precision and reproducibility of parameter estimation in both simulated data and in 68 abdominal in-vivo DW-MRIs. Our results show that the SPIM model not only substantially reduced parameter estimation errors by up to 26%; it also significantly improved the robustness of the parameter estimates (paired Student's t-test, p < 0.0001) by reducing the coefficient of variation (CV) of estimated parameters compared to those produced by previous models. In addition, the SPIM model improves the parameter estimates reproducibility for both intra- (up to 47%) and inter-session (up to 30%) estimates compared to those generated by previous models. Thus, the SPIM model has the potential to improve accuracy, precision and robustness of quantitative abdominal DW-MRI analysis for clinical applications.