The existence of biexponential signal decay in magnetic resonance diffusion-weighted imaging appears to be independent of compartmentalization

Triexponential Diffusion Analysis of Diffusion-weighted Imaging for Breast Ductal Carcinoma in Situ and Invasive Ductal Carcinoma

Purpose

To obtain detailed information in breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) using triexponential diffusion analysis.

Methods

Diffusion-weighted images (DWI) of the breast were obtained using single-shot diffusion echo-planar imaging with 15 b-values. Mean signal intensities at each b-value were measured in the DCIS and IDC lesions and fitted with the triexponential function based on a two-step approach: slow-restricted diffusion coefficient (D_s) was initially determined using a monoexponential function with b-values > 800 s/mm². The diffusion coefficient of free water at 37°C was assigned to the fast-free diffusion coefficient (D_f). Finally, the perfusion-related diffusion coefficient (D_p) was derived using all the b-values. Furthermore, biexponential analysis was performed to obtain the perfusion-related diffusion coefficient (D^*) and the perfusion-independent diffusion coefficient (D). Monoexponential analysis was performed to obtain the apparent diffusion coefficient (ADC). The sensitivity and specificity of the aforementioned diffusion coefficients for distinguishing between DCIS and IDC were evaluated using the pathological results.

Results

The D_s, D, and ADC of DCIS were significantly higher than those of IDC (P < 0.01 for all). There was no significant correlation between D_p and D_s, but there was a weak correlation between D^* and D. The combination of D_p and D_s showed higher sensitivity and specificity (85.9% and 71.4%, respectively), compared to the combination of D^* and D (81.5% and 33.3%, respectively).

Conclusion

Triexponential analysis can provide detailed diffusion information for breast tumors that can be used to differentiate between DCIS and IDC.

Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice

Purpose

To evaluate fitting quality and repeatability of four mathematical models for diffusion weighted imaging (DWI) during tumor progression in mouse xenograft model of prostate cancer.

Methods

Human prostate cancer cells (PC-3) were implanted subcutaneously in right hind limbs of 11 immunodeficient mice. Tumor growth was followed by weekly DWI examinations using a 7T MR scanner. Additional DWI examination was performed after repositioning following the fourth DWI examination to evaluate short term repeatability. DWI was performed using 15 and 12 b-values in the ranges of 0-500 and 0-2000 s/mm², respectively. Corrected Akaike information criteria and F-ratio were used to evaluate fitting quality of each model (mono-exponential, stretched exponential, kurtosis, and bi-exponential).

Results

Significant changes were observed in DWI data during the tumor growth, indicated by ADC_m, ADC_s, and ADC_k. Similar results were obtained using low as well as high b-values. No marked changes in model preference were present between the weeks 1−4. The parameters of the mono-exponential, stretched exponential, and kurtosis models had smaller confidence interval and coefficient of repeatability values than the parameters of the bi-exponential model.

Conclusion

Stretched exponential and kurtosis models showed better fit to DWI data than the mono-exponential model and presented with good repeatability.

Modeling the diffusion-weighted imaging signal for breast lesions in the b = 200 to 3000 s/mm2 range: quality of fit and classification accuracy for different representations

PURPOSE

To evaluate different non-Gaussian representations for the diffusion-weighted imaging (DWI) signal in the b-value range 200 to 3000 s/mm² in benign and malignant breast lesions.

METHODS

Forty-three patients diagnosed with benign (n = 18) or malignant (n = 25) tumors of the breast underwent DWI (b-values 200, 600, 1200, 1800, 2400, and 3000 s/mm² ). Six different representations were fit to the average signal from regions of interest (ROIs) at different b-value ranges. Quality of fit was assessed by the corrected Akaike information criterion (AICc), and the Friedman test was used for assessing representation ranks. The area under the curve (AUC) of receiver operating characteristic curves were used to evaluate the power of derived parameters to differentiate between malignant and benign lesions. The lesion ROI was divided in central and peripheral parts to assess potential effect of heterogeneity. Sensitivity to noise-floor correction was also evaluated.

RESULTS

The Padé exponent was ranked as the best based on AICc, whereas 3 models (kurtosis, fractional, and biexponential) achieved the highest AUC = 0.99 for lesion differentiation. The monoexponential model at b_max = 600 s/mm² already provides AUC = 0.96, with considerably shorter acquisition time and simpler analysis. Significant differences between central and peripheral parts of lesions were found in malignant lesions. The mono- and biexponential models were most stable against varying degrees of noise-floor correction.

CONCLUSION

Non-Gaussian representations are required for fitting of the DWI curve at high b-values in breast lesions. However, the added clinical value from the high b-value data for differentiation of benign and malignant lesions is not clear.

Intravoxel incoherent motion diffusion-weighted imaging evaluated the response to concurrent chemoradiotherapy in patients with cervical cancer

To evaluate the application of multiple b values diffusion-weighted imaging based on biexponential signal decay model to predict the response to concurrent chemoradiotherapy in cervical cancer patients.This prospective study enrolled 28 patients (mean age: 50.89 ± 10.70 years) with cervical cancer confirmed by biopsy who received concurrent chemoradiotherapy. Pelvic magnetic resonance scans were performed 2 weeks before, 7 days and 21 days after the initiation of therapy, and 1 month after the end of the treatment. Diffusion-weighted imaging with b values of 0, 50, 450, and 850 s/mm were performed, and tumor volume, means of tumor apparent diffusion coefficient (ADC)min, ADCmean, ADCslow, ADCfast, and Ffast were measured.Pretreatment ADCmin and ADCslow of good outcome group were significantly higher than those of poor outcome group (P < .05). At the 7th day of the treatment, Ffast and its change rate of good outcome group were significantly higher than those of poor outcome group (P < .05). At the 7th day and 21st day of the treatment, Ffast showed a slowly increasing tendency with no significant difference compared with pretreatment value in poor outcome group (P < .05). One month post-treatment, only ADCslow change rate was significantly higher in good outcome group than that in poor outcome group.Intravoxel incoherent motion-related ADC values could be utilized to better predict the outcome of cervical cancer chemoradiotherapy.

Anomalous water dynamics in brain: a combined diffusion magnetic resonance imaging and neutron scattering investigation

Water diffusion is an optimal tool for investigating the architecture of brain tissue on which modern medical diagnostic imaging techniques rely. However, intrinsic tissue heterogeneity causes systematic deviations from pure free-water diffusion behaviour. To date, numerous theoretical and empirical approaches have been proposed to explain the non-Gaussian profile of this process. The aim of this work is to shed light on the physics piloting water diffusion in brain tissue at the micrometre-to-atomic scale. Combined diffusion magnetic resonance imaging and first pioneering neutron scattering experiments on bovine brain tissue have been performed in order to probe diffusion distances up to macromolecular separation. The coexistence of free-like and confined water populations in brain tissue extracted from a bovine right hemisphere has been revealed at the micrometre and atomic scale. The results are relevant for improving the modelling of the physics driving intra- and extracellular water diffusion in brain, with evident benefit for the diffusion magnetic resonance imaging technique, nowadays widely used to diagnose, at the micrometre scale, brain diseases such as ischemia and tumours.

The Kärger vs bi-exponential model: Theoretical insights and experimental validations

We revise three common models accounting for water exchange in pulsed-gradient spin-echo measurements: a bi-exponential model with time-dependent water fractions, the Kärger model, and a modified Kärger model designed for restricted diffusion, e.g. inside cells. The three models are compared and applied to experimental data from yeast cell suspensions. The Kärger model and the modified Kärger model yield very close results and accurately fit the data. The bi-exponential model, although less rigorous, has a natural physical interpretation and suggests a new experimental modality to estimate the water exchange time.

A Modified Tri-Exponential Model for Multi-b-value Diffusion-Weighted Imaging: A Method to Detect the Strictly Diffusion-Limited Compartment in Brain

Purpose: To present a new modified tri-exponential model for diffusion-weighted imaging (DWI) to detect the strictly diffusion-limited compartment, and to compare it with the conventional bi- and tri-exponential models.

Methods: Multi-b-value diffusion-weighted imaging (DWI) with 17 b-values up to 8,000 s/mm² were performed on six volunteers. The corrected Akaike information criterions (AICc) and squared predicted errors (SPE) were calculated to compare these three models.

Results: The mean f₀ values were ranging 11.9–18.7% in white matter ROIs and 1.2–2.7% in gray matter ROIs. In all white matter ROIs: the AICcs of the modified tri-exponential model were the lowest (p < 0.05 for five ROIs), indicating the new model has the best fit among these models; the SPEs of the bi-exponential model were the highest (p < 0.05), suggesting the bi-exponential model is unable to predict the signal intensity at ultra-high b-value. The mean ADC_very−slow values were extremely low in white matter (1–7 × 10⁻⁶ mm²/s), but not in gray matter (251–445 × 10⁻⁶ mm²/s), indicating that the conventional tri-exponential model fails to represent a special compartment.

Conclusions: The strictly diffusion-limited compartment may be an important component in white matter. The new model fits better than the other two models, and may provide additional information.

Non-Gaussian diffusion imaging for malignant and benign pulmonary nodule differentiation: a preliminary study

BACKGROUND

Diffusion-weighted imaging (DWI) derived apparent diffusion coefficient (ADC) has demonstrated inconsistent results in pulmonary nodule differentiation. Diffusion kurtosis imaging (DKI), which quantifies non-Gaussian diffusion, is believed to better characterize tissue micro-structure than conventional DWI.

PURPOSE

To assess the feasibility of DKI in human lungs and to compare its diagnostic value with standard DWI in differentiating malignancies from benign pulmonary nodules.

MATERIAL AND METHODS

Thirty-five pulmonary nodules in 32 consecutive patients were evaluated by DKI by using 3b-values of 0, 500, and 1000 s/mm² and conventional DWI with b values of 0 and 800 s/mm². Two observers independently evaluated and compared diagnostic accuracy of mean kurtosis (MK) and ADC values in differentiating malignancies from benign pulmonary nodules. The intra- and inter-observer repeatability (intra-class correlation coefficient [ICC]) were also assessed for each derived measures.

RESULTS

The diagnostic accuracy, and the area under curve (AUC) in differentiating malignancies from benign pulmonary nodule, were not significantly higher for MK (Obs. 1a: 85.70%, 0.87; Obs. 1b: 80.00%, 0.80; and Obs. 2: 82.80%, 0.91) as compared to ADC (Obs. 1a: 77.14%, 0.81; Obs. 1b: 80.00%, 0.85; and Obs. 2: 77.14%, 0.85 respectively). The intra- and inter-observer agreement (ICC) for malignant and benign lesions was substantial for each reading.

CONCLUSION

The initial results of this study indicate the feasibility of DKI in human lungs. However, there was no significant benefit of DKI derived MK values over ADC for malignant and benign pulmonary nodule differentiation.

A comparative assessment of preclinical chemotherapeutic response of tumors using quantitative non-Gaussian diffusion MRI

BACKGROUND

Diffusion-weighted MRI (DWI) signal attenuation is often not mono-exponential (i.e. non-Gaussian diffusion) with stronger diffusion weighting. Several non-Gaussian diffusion models have been developed and may provide new information or higher sensitivity compared with the conventional apparent diffusion coefficient (ADC) method. However the relative merits of these models to detect tumor therapeutic response is not fully clear.

METHODS

Conventional ADC, and three widely-used non-Gaussian models, (bi-exponential, stretched exponential, and statistical model), were implemented and compared for assessing SW620 human colon cancer xenografts responding to barasertib, an agent known to induce apoptosis via polyploidy. Bayesian Information Criterion (BIC) was used for model selection among all three non-Gaussian models.

RESULTS

All of tumor volume, histology, conventional ADC, and three non-Gaussian DWI models could show significant differences between control and treatment groups after four days of treatment. However, only the non-Gaussian models detected significant changes after two days of treatment. For any treatment or control group, over 65.7% of tumor voxels indicate the bi-exponential model is strongly or very strongly preferred.

CONCLUSION

Non-Gaussian DWI model-derived biomarkers are capable of detecting tumor earlier chemotherapeutic response of tumors compared with conventional ADC and tumor volume. The bi-exponential model provides better fitting compared with statistical and stretched exponential models for the tumor and treatment models used in the current work.

Diffusion-weighted imaging uncovers likely sources of processing-speed deficits in schizophrenia

Schizophrenia, a devastating psychiatric illness with onset in the late teens to early 20s, is thought to involve disrupted brain connectivity. Functional and structural disconnections of cortical networks may underlie various cognitive deficits, including a substantial reduction in the speed of information processing in schizophrenia patients compared with controls. Myelinated white matter supports the speed of electrical signal transmission in the brain. To examine possible neuroanatomical sources of cognitive deficits, we used a comprehensive diffusion-weighted imaging (DWI) protocol and characterized the white matter diffusion signals using diffusion kurtosis imaging (DKI) and permeability-diffusivity imaging (PDI) in patients (n = 74), their nonill siblings (n = 41), and healthy controls (n = 113). Diffusion parameters that showed significant patient-control differences also explained the patient-control differences in processing speed. This association was also found for the nonill siblings of the patients. The association was specific to processing-speed abnormality but not specific to working memory abnormality or psychiatric symptoms. Our findings show that advanced diffusion MRI in white matter may capture microstructural connectivity patterns and mechanisms that govern the association between a core neurocognitive measure-processing speed-and neurobiological deficits in schizophrenia that are detectable with in vivo brain scans. These non-Gaussian diffusion white matter metrics are promising surrogate imaging markers for modeling cognitive deficits and perhaps, guiding treatment development in schizophrenia.

Estimation of the Number of Compartments Associated With the Apparent Diffusion Coefficient in MRI: The Theoretical and Experimental Investigations

OBJECTIVE

The goal of the present study was to estimate the number of compartments and the mean apparent diffusion coefficient (ADC) value with the use of the DWI signal curve.

MATERIALS AND METHODS

A useful new mathematic model that includes internal correlation among subcompartments with a distinct number of compartments was proposed. The DWI signal was simulated to estimate the approximate association between the number of subcompartments and the molecular density, with density corresponding to the ratio of the ADC values of the compartments, as determined using the Monte Carlo method.

RESULTS

Various factors, such as energy depletion, temperature, intracellular water accumulation, changes in the tortuosity of the extracellular diffusion paths, and changes in cell membrane permeability, have all been implicated as factors contributing to changes in the ADC of water (ADCw); therefore, one may consider them as pseudocompartments in the new model proposed in this study. The lower the coefficient is, the lower the contribution of the compartment to the net signal will be. The results of the simulation indicate that when the number of compartments increases, the signal will become significantly lower, because the gradient factor (i.e., the b value) will increase. In other words, the signal curve is approximately linear at all b values when the number of compartments in which the tissues have been severely damaged is low; however, when the number of compartments is high, the curve will become constant at high b values, and the perfusion parameters will prevail on the diffusion parameters at low b values. Therefore, normal tissues will be investigated when the number of compartments and the ADC values are high and the b values are low, whereas damaged tissues will be evaluated when the number of compartments and the ADC values are low and the b values are high.

CONCLUSION

The present study investigates damaged tissues at high b values for which the effect of eddy currents will also be compensated. These b values will probably be used in functional MRI.

Apparent Diffusion Coefficient Value Is Not Dependent on Magnetic Resonance Systems and Field Strength Under Fixed Imaging Parameters in Brain

OBJECTIVE

The aim of the study was to investigate the causes of apparent diffusion coefficient (ADC) measurement errors and to determine the optimal scanning parameters that are independent of the field strength and vendors of the magnetic resonance (MR) system.

MATERIALS AND METHODS

Brain MR images of 10 healthy volunteers were scanned using 6 MR scanners of different field strengths and vendors in 2 different institutions. Ethical review board approvals were obtained for this study, and all volunteers gave their informed consents. Coefficient of variation (CV) of ADC values were compared for their differences in various MR scanners and in the scanned subjects.

RESULTS

The CV of ADC values for 6 different scanners of 6 brains was 3.32%. The CV for repeated measurements in 1 day (10 scans per day) and in 10 days (scan per day for 10 days) for 1 subject was 1.72% and 2.96%, respectively (n = 5, P < 0.001). The CV of measurements for 10 healthy subjects was 5.22%. The measurement errors of the ADC values for 6 different MR units in 1 subject were higher than the intrascanner variance for the same subject but were lower than the intersubject variance for the same scanner.

CONCLUSIONS

The variance in the ADC values for different MR scanners is reasonably small if appropriate scanning parameters (repetition time, >3000 ms; echo time, minimum; and high enough signal-to-noise ratio of high-b diffusion-weighted image) are used.

Heritability of complex white matter diffusion traits assessed in a population isolate

INTRODUCTION

Diffusion weighted imaging (DWI) methods can noninvasively ascertain cerebral microstructure by examining pattern and directions of water diffusion in the brain. We calculated heritability for DWI parameters in cerebral white (WM) and gray matter (GM) to study the genetic contribution to the diffusion signals across tissue boundaries.

METHODS

Using Old Order Amish (OOA) population isolate with large family pedigrees and high environmental homogeneity, we compared the heritability of measures derived from three representative DWI methods targeting the corpus callosum WM and cingulate gyrus GM: diffusion tensor imaging (DTI), the permeability-diffusivity (PD) model, and the neurite orientation dispersion and density imaging (NODDI) model. These successively more complex models represent the diffusion signal modeling using one, two, and three diffusion compartments, respectively.

RESULTS

We replicated the high heritability of the DTI-based fractional anisotropy (h(2)  = 0.67) and radial diffusivity (h(2)  = 0.72) in WM. High heritability in both WM and GM tissues were observed for the permeability-diffusivity index from the PD model (h(2)  = 0.64 and 0.84), and the neurite density from the NODDI model (h(2)  = 0.70 and 0.55). The orientation dispersion index from the NODDI model was only significantly heritable in GM (h(2)  = 0.68).

CONCLUSION

DWI measures from multicompartmental models were significantly heritable in WM and GM. DWI can offer valuable phenotypes for genetic research; and genes thus identified may reveal mechanisms contributing to mental and neurological disorders in which diffusion imaging anomalies are consistently found. Hum Brain Mapp 37:525-535, 2016. © 2015 Wiley Periodicals, Inc.

Accuracies and Contrasts of Models of the Diffusion-Weighted-Dependent Attenuation of the MRI Signal at Intermediate b-values

The diffusion-weighted-dependent attenuation of the MRI signal E(b) is extremely sensitive to microstructural features. The aim of this study was to determine which mathematical model of the E(b) signal most accurately describes it in the brain. The models compared were the monoexponential model, the stretched exponential model, the truncated cumulant expansion (TCE) model, the biexponential model, and the triexponential model. Acquisition was performed with nine b-values up to 2500 s/mm² in 12 healthy volunteers. The goodness-of-fit was studied with F-tests and with the Akaike information criterion. Tissue contrasts were differentiated with a multiple comparison corrected nonparametric analysis of variance. F-test showed that the TCE model was better than the biexponential model in gray and white matter. Corrected Akaike information criterion showed that the TCE model has the best accuracy and produced the most reliable contrasts in white matter among all models studied. In conclusion, the TCE model was found to be the best model to infer the microstructural properties of brain tissue.

Parameter estimation using macroscopic diffusion MRI signal models

Macroscopic models of the diffusion MRI (dMRI) signal can be helpful to understanding the relationship between the tissue microstructure and the dMRI signal. We study the least squares problem associated with estimating tissue parameters such as the cellular volume fraction, the residence times and the effective diffusion coefficients using a recently developed macroscopic model of the dMRI signal called the Finite Pulse Kärger model that generalizes the original Kärger model to non-narrow gradient pulses. In order to analyze the quality of the estimation in a controlled way, we generated synthetic noisy dMRI signals by including the effect of noise on the exact signal produced by the Finite Pulse Kärger model. The noisy signals were then fitted using the macroscopic model. Minimizing the least squares, we estimated the model parameters. The bias and standard deviations of the estimated model parameters as a function of the signal to noise ratio (SNR) were obtained. We discuss the choice of the b-values, the least square weights, the extension to experimentally obtained dMRI data as well noise correction.

Exploring diffusion across permeable barriers at high gradients. I. Narrow pulse approximation

The adaptive variation of the gradient intensity with the diffusion time at a constant optimal b-value is proposed to enhance the contribution of the nuclei diffusing across permeable barriers, to the pulsed-gradient spin-echo (PGSE) signal. An exact simple formula the PGSE signal is derived under the narrow pulse approximation in the case of one-dimensional diffusion across a single permeable barrier. The barrier contribution to the signal is shown to be maximal at a particular b-value. The exact formula is then extended to multiple permeable barriers, while the PGSE signal is shown to be sensitive to the permeability and to the inter-barrier distance. Potential applications of the protocol to survey diffusion in three-dimensional domains with permeable membranes are illustrated through numerical simulations.

Multimodal white matter imaging to investigate reduced fractional anisotropy and its age-related decline in schizophrenia

We hypothesized that reduced fractional anisotropy (FA) of water diffusion and its elevated aging-related decline in schizophrenia patients may be caused by elevated hyperintensive white matter (HWM) lesions, by reduced permeability-diffusivity index (PDI), or both. We tested this hypothesis in 40/30 control/patient participants. FA values for the corpus callosum were calculated from high angular resolution diffusion tensor imaging (DTI). Whole-brain volume of HWM lesions was quantified by 3D-T2w-fluid-attenuated inversion recovery (FLAIR) imaging. PDI for corpus callosum was ascertained using multi b-value diffusion imaging (15 b-shells with 30 directions per shell). Patients had significantly lower corpus callosum FA values, and there was a significant age-by-diagnosis interaction. Patients also had significantly reduced PDI but no difference in HWM volume. PDI and HWM volume were significant predictors of FA and captured the diagnosis-related variance. Separately, PDI robustly explained FA variance in schizophrenia patients, but not in controls. Conversely, HWM volume made equally significant contributions to variability in FA in both groups. The diagnosis-by-age effect of FA was explained by a PDI-by-diagnosis interaction. Post hoc testing showed a similar trend for PDI of gray mater. Our study demonstrated that reduced FA and its accelerated decline with age in schizophrenia were explained by pathophysiology indexed by PDI, rather than HWM volume.

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.

Diffusion microscopist simulator: a general Monte Carlo simulation system for diffusion magnetic resonance imaging

This article describes the development and application of an integrated, generalized, and efficient Monte Carlo simulation system for diffusion magnetic resonance imaging (dMRI), named Diffusion Microscopist Simulator (DMS). DMS comprises a random walk Monte Carlo simulator and an MR image synthesizer. The former has the capacity to perform large-scale simulations of Brownian dynamics in the virtual environments of neural tissues at various levels of complexity, and the latter is flexible enough to synthesize dMRI datasets from a variety of simulated MRI pulse sequences. The aims of DMS are to give insights into the link between the fundamental diffusion process in biological tissues and the features observed in dMRI, as well as to provide appropriate ground-truth information for the development, optimization, and validation of dMRI acquisition schemes for different applications. The validity, efficiency, and potential applications of DMS are evaluated through four benchmark experiments, including the simulated dMRI of white matter fibers, the multiple scattering diffusion imaging, the biophysical modeling of polar cell membranes, and the high angular resolution diffusion imaging and fiber tractography of complex fiber configurations. We expect that this novel software tool would be substantially advantageous to clarify the interrelationship between dMRI and the microscopic characteristics of brain tissues, and to advance the biophysical modeling and the dMRI methodologies.

Permeability-diffusivity modeling vs. fractional anisotropy on white matter integrity assessment and application in schizophrenia

INTRODUCTION

Diffusion tensor imaging (DTI) assumes a single pool of anisotropically diffusing water to calculate fractional anisotropy (FA) and is commonly used to ascertain white matter (WM) deficits in schizophrenia. At higher b-values, diffusion-signal decay becomes bi-exponential, suggesting the presence of two, unrestricted and restricted, water pools. Theoretical work suggests that semi-permeable cellular membrane rather than the presence of two physical compartments is the cause. The permeability-diffusivity (PD) parameters measured from bi-exponential modeling may offer advantages, over traditional DTI-FA, in identifying WM deficits in schizophrenia.

METHODS

Imaging was performed in N = 26/26 patients/controls (age = 20-61 years, average age = 40.5 ± 12.6). Imaging consisted of fifteen b-shells: b = 250-3800 s/mm(2) with 30 directions/shell, covering seven slices of mid-sagittal corpus callosum (CC) at 1.7 × 1.7 × 4.6 mm. 64-direction DTI was also collected. Permeability-diffusivity-index (PDI), the ratio of restricted to unrestricted apparent diffusion coefficients, and the fraction of unrestricted compartment (Mu) were calculated for CC and cingulate gray matter (GM). FA values for CC were calculated using tract-based-spatial-statistics.

RESULTS

Patients had significantly reduced PDI in CC (p ≅ 10(- 4)) and cingulate GM (p = 0.002), while differences in CC FA were modest (p ≅ .03). There was no group-related difference in Mu. Additional theoretical-modeling analysis suggested that reduced PDI in patients may be caused by reduced cross-membrane water molecule exchanges.

CONCLUSION

PDI measurements for cerebral WM and GM yielded more robust patient-control differences than DTI-FA. Theoretical work offers an explanation that patient-control PDI differences should implicate abnormal active membrane permeability. This would implicate abnormal activities in ion-channels that use water as substrate for ion exchange, in cerebral tissues of schizophrenia patients.

Analysis of multiple B-value diffusion-weighted imaging in pediatric acute encephalopathy

Acute encephalopathy is a disease group more commonly seen in children. It is often severe and has neurological sequelae. Imaging is important for early diagnosis and prompt treatment to ameliorate an unfavorable outcome, but insufficient sensitivity/specificity is a problem. To overcome this, a new value (fraction of high b-pair (F_H)) that could be processed from clinically acceptable MR diffusion-weighted imaging (DWI) with three different b-values was designed on the basis of a two-compartment model of water diffusion signal attenuation. The purpose of this study is to compare F_H with the apparent diffusion coefficient (ADC) regarding the detectability of pediatric acute encephalopathy. We retrospectively compared the clinical DWI of 15 children (1–10 years old, mean 2.34, 8 boys, 7 girls) of acute encephalopathy with another 16 children (1–11 years old, mean 4.89, 9 boys, 7 girls) as control. A comparison was first made visually by mapping F_H on the brain images, and then a second comparison was made on the basis of 10 regions of interest (ROIs) set on cortical and subcortical areas of each child. F_H map visually revealed diffusely elevated F_H in cortical and subcortical areas of the patients with acute encephalopathy; the changes seemed more diffuse in F_H compared to DWI. The comparison based on ROI revealed elevated mean F_H in the cortical and subcortical areas of the acute encephalopathy patients compared to control with significant difference (P<0.05). Similar findings were observed even in regions where the findings of DWI were slight. The reduction of mean ADC was significant in regions with severe findings in DWI, but it was not constant in the areas with slighter DWI findings. The detectability of slight changes of cortical and subcortical lesions in acute encephalopathy may be superior in F_H compared to ADC.

Sensitivities of statistical distribution model and diffusion kurtosis model in varying microstructural environments: a Monte Carlo study

The aim of this study was to investigate the microstructural sensitivity of the statistical distribution and diffusion kurtosis (DKI) models of non-monoexponential signal attenuation in the brain using diffusion-weighted MRI (DWI). We first developed a simulation of 2-D water diffusion inside simulated tissue consisting of semi-permeable cells and a variable cell size. We simulated a DWI acquisition of the signal in a volume using a pulsed gradient spin echo (PGSE) pulse sequence, and fitted the models to the simulated DWI signals using b-values up to 2500 s/mm(2). For comparison, we calculated the apparent diffusion coefficient (ADC) of the monoexponential model (b-value=1000 s/mm(2)). In separate experiments, we varied the cell size (5-10-15 μm), cell volume fraction (0.50-0.65-0.80), and membrane permeability (0.001-0.01-0.1mm/s) to study how the fitted parameters tracked simulated microstructural changes. The ADC was sensitive to all the simulated microstructural changes except the decrease in membrane permeability. The ADC increased with larger cell size, smaller cell volume fraction, and larger membrane permeability. The σstat of the statistical distribution model increased exclusively with a decrease in cell volume fraction. The Kapp of the DKI model was exclusively increased with decreased cell size and decreased with increasing membrane permeability. These results suggest that the non-monoexponential models of water diffusion have different, specific microstructural sensitivity, and a combination of the models may give insights into the microstructural underpinning of tissue pathology.

Associations among q-space MRI, diffusion-weighted MRI and histopathological parameters in meningiomas

OBJECTIVES

The purposes of this MR-based study were to calculate q-space imaging (QSI)-derived mean displacement (MDP) in meningiomas, to evaluate the correlation of MDP values with apparent diffusion coefficient (ADC) and to investigate the relationships among these diffusion parameters, tumour cell count (TCC) and MIB-1 labelling index (LI).

METHODS

MRI, including QSI and conventional diffusion-weighted imaging (DWI), was performed in 44 meningioma patients (52 lesions). ADC and MDP maps were acquired from post-processing of the data. Quantitative analyses of these maps were performed by applying regions of interest. Pearson correlation coefficients were calculated for ADC and MDP in all lesions and for ADC and TCC, MDP and TCC, ADC and MIB-1 LI, and MDP and MIB-1 LI in 17 patients who underwent subsequent surgery.

RESULTS

ADC and MDP values were found to have a strong correlation: r = 0.78 (P = <0.0001). Both ADC and MDP values had a significant negative association with TCC: r = -0.53 (p = 0.02) and -0.48 (P = 0.04), respectively. MIB-1 LI was not, however, found to have a significant association with these diffusion parameters.

CONCLUSION

In meningiomas, both ADC and MDP may be representative of cell density.

KEY POINTS

• Diffusion-weighted MRI offers possibilities to assess the aggressiveness of meningiomas. • The q-space imaging-derived mean displacement correlates strongly with apparent diffusion coefficients. • Both diffusion parameters showed a strong negative association with tumour cell counts. • Derived mean displacement may help assess the aggressiveness of meningiomas preoperatively.

The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter

Biophysical models that describe the outcome of white matter diffusion MRI experiments have various degrees of complexity. While the simplest models assume equal-sized and parallel axons, more elaborate ones may include distributions of axon diameters and axonal orientation dispersions. These microstructural features can be inferred from diffusion-weighted signal attenuation curves by solving an inverse problem, validated in several Monte Carlo simulation studies. Model development has been paralleled by microscopy studies of the microstructure of excised and fixed nerves, confirming that axon diameter estimates from diffusion measurements agree with those from microscopy. However, results obtained in vivo are less conclusive. For example, the amount of slowly diffusing water is lower than expected, and the diffusion-encoded signal is apparently insensitive to diffusion time variations, contrary to what may be expected. Recent understandings of the resolution limit in diffusion MRI, the rate of water exchange, and the presence of microscopic axonal undulation and axonal orientation dispersions may, however, explain such apparent contradictions. Knowledge of the effects of biophysical mechanisms on water diffusion in tissue can be used to predict the outcome of diffusion tensor imaging (DTI) and of diffusion kurtosis imaging (DKI) studies. Alterations of DTI or DKI parameters found in studies of pathologies such as ischemic stroke can thus be compared with those predicted by modelling. Observations in agreement with the predictions strengthen the credibility of biophysical models; those in disagreement could provide clues of how to improve them. DKI is particularly suited for this purpose; it is performed using higher b-values than DTI, and thus carries more information about the tissue microstructure. The purpose of this review is to provide an update on the current understanding of how various properties of the tissue microstructure and the rate of water exchange between microenvironments are reflected in diffusion MRI measurements. We focus on the use of biophysical models for extracting tissue-specific parameters from data obtained with single PGSE sequences on clinical MRI scanners, but results obtained with animal MRI scanners are also considered. While modelling of white matter is the central theme, experiments on model systems that highlight important aspects of the biophysical models are also reviewed.

Parametric representation of multiple white matter fascicles from cube and sphere diffusion MRI

The characterization of the complex diffusion signal arising from the brain remains an open problem. Many representations focus on characterizing the global shape of the diffusion profile at each voxel and are limited to the assessment of connectivity. In contrast, Multiple Fascicle Models (MFM) seek to represent the contribution from each white matter fascicle and may be useful in the investigation of both white matter connectivity and diffusion properties of each individual fascicle. However, the most appropriate representation of multiple fascicles remains unclear. In particular, a multiple tensor representation of multiple fascicles has frequently been reported to be numerically challenging and unstable. We provide here the first analytical demonstration that when using a diffusion MRI acquisition with only one non-zero b-value, such as in conventional single-shell HARDI acquisition, a co-linearity in model parameters makes the precise model estimation impossible. Motivated by this theoretical result, we propose the novel CUSP (CUbe and SPhere) optimal acquisition scheme to achieve multiple non-zero b-values. It combines the gradients of a single-shell HARDI with gradients in its enclosing cube, in which varying b-values can be acquired by modulation of the gradient strength, without modifying the minimum echo time. Compared to a multi-shell HARDI acquisition, our scheme has significantly increased signal-to-noise ratio. We propose a novel estimation algorithm that enables efficient, robust and accurate estimation of the parameters of a multi-tensor model. In conjunction with a CUSP acquisition, it enables full estimation of the multi-tensor model. We present an evaluation of CUSP-MFM on both synthetic phantoms and invivo data. We report qualitative and quantitative experimental evaluations which demonstrate the ability of CUSP-MFM to characterize multiple fascicles from short duration acquisitions. CUSP-MFM enables rapid and effective investigation of multiple white matter fascicles, in both normal development and in disease and injury, in research and clinical practice.

Comparisons of multi b-value DWI signal analysis with pathological specimen of breast cancer

Previous studies have reported that the signal attenuation of diffusion weighted magnetic resonance imaging for tumor tissues displays a non-monoexponential biexponential decay, and the apparent diffusion coefficients (ADCs) can be divided into a fast and slow diffusion component by using a simple biexponential decay model. The purpose of this study is to examine the non-monoexponential character of the diffusion weighted magnetic resonance imaging signal attenuations of breast cancers, estimate the fast and slow diffusion components, and compare them with the extra- and intracellular component information obtained from the pathological specimens. Twenty-two subjects having breast cancers underwent diffusion weighted magnetic resonance imaging using six b-values up to 3500 s/mm(2) and the signal attenuations were analyzed using the biexponential function. The derived slow component fraction correlated with the cellular fraction and the ADCs converged to 0.2-0.3 × 10(-3) mm(2) /s for the higher cellular fractions. The ADCs of the fast component ranged from 1.3 to 3.9 × 10(-3) mm(2) /s and showed no correlation with the extracellular components. This result suggests that the main reason for the decreasing ADC of a breast tumor is the decreasing fraction of the fast component and the increasing fraction of the slow component having a low ADC rather than the decreasing ADC of the fast component by the restricted water diffusion in the reduced extracellular spaces.

A biexponential DWI study in rat brain intracellular oedema

PURPOSE

To examine the changes in MR parameters derived from diffusion weighted imaging (DWI) biexponential analysis in an in vivo intracellular brain oedema model, and to apply electron microscopy (EM) to shed more light on the morphological background of MR-related observations.

MATERIALS AND METHODS

Intracellular oedema was induced in ten male Wistar rats (380-450g) by way of water load, using a 20% body weight intraperitoneal injection of 140mmol/L dextrose solution. A 3T MRI instrument was used to perform serial DWI, and MR specroscopy (water signal) measurements. Following the MR examination the brains of the animals were analyzed for EM.

RESULTS

Following the water load induction, apparent diffusion coefficient (ADC) values started declining from 724±43μm(2)/s to 682±26μm(2)/s (p<0.0001). ADC-fast values dropped from 948±122 to 840±66μm(2)/s (p<0.001). ADC-slow showed a decrease from 226±66 to 191±74μm(2)/s (p<0.05). There was a shift from the slow to the fast component at 110min time point. The percentage of the fast component demonstrated moderate, yet significant increase from 76.56±7.79% to 81.2±7.47% (p<0.05). The water signal was increasing by 4.98±3.52% compared to the base line (p<0.01). The results of the E.M. revealed that water was detected intracellularly, within astrocytic preivascular end-feet and cell bodies.

CONCLUSION

The unexpected volume fraction changes (i.e. increase in fast component) detected in hypotonic oedema appear to be substantially different from those observed in stroke. It may suggest that ADC decrease in stroke, in contrast to general presumptions, cannot be explained only by water shift from extra to intracellular space (i.e. intracellular oedema).

[Analyses of restricted diffusion of water molecules using trabecular bone phantom]

We have reported that the apparent diffusion coefficient (ADC) was correlated with bone mineral density, but the relation between the restricted diffusion of the water molecules and the trabecular bone structure was unclear. The purpose of our study is to clarify this relationship using two component analyses with an original phantom. With an increase in the interspace area of the simulated trabecular bone, the ADC of the fast component was increased, and the fraction of the fast component was also increased. On the other hand, with an increase in the interspace area of the simulated trabecular bone, the ADC of the slow component was decreased, and the fraction of the slow component was increased. Moreover, the ADC and fraction of the dry vertebral bone agreed with those of the simulated trabecular bone. This result means that our phantoms can reproduce the actual trabecular bone structure, which induces the restricted diffusion. The diffusion of the water molecules was separated into fast and slow components because of the restricted diffusion of the trabecular bone structure. Our original phantom enables analyzing restricted diffusion, and this analytical method obtains more detailed information on trabecular bone structure.

Assessment of multiexponential diffusion features as MRI cancer therapy response metrics

The aim of this study was to empirically test the effect of chemotherapy-induced tissue changes in a glioma model as measured by several diffusion indices calculated from nonmonoexponential formalisms over a wide range of b-values. We also compared these results to the conventional two-point apparent diffusion coefficient calculation using nominal b-values. Diffusion-weighted imaging was performed over an extended range of b-values (120-4000 sec/mm(2) ) on intracerebral rat 9L gliomas before and after a single dose of 1,3-bis(2-chloroethyl)-1-nitrosourea. Diffusion indices from three formalisms of diffusion-weighted signal decay [(a) two-point analytical calculation using either low or high b-values, (b) a stretched exponential formalism, and (c) a biexponential fit] were tested for responsiveness to therapy-induced differences between control and treated groups. Diffusion indices sensitive to "fast diffusion" produced the largest response to treatment, which resulted in significant differences between groups. These trends were not observed for "slow diffusion" indices. Although the highest rate of response was observed from the biexponential formalism, this was not found to be significantly different from the conventional monoexponential apparent diffusion coefficient method. In conclusion, parameters from the more complicated nonmonoexponential formalisms did not provide additional sensitivity to treatment response in this glioma model beyond that observed from the two-point conventional monoexponential apparent diffusion coefficient method.

Diffusion-weighted imaging of normal fibroglandular breast tissue: influence of microperfusion and fat suppression technique on the apparent diffusion coefficient

The influence of microperfusion and fat suppression technique on the apparent diffusion coefficient (ADC) values obtained with diffusion weighted imaging (DWI) of normal fibroglandular breast tissue was investigated. Seven volunteers (14 breasts) were scanned using diffusion weighting factors (b values) up to 1600 s/mm(2) and the four different fat suppression techniques: STIR, fat saturation, SPAIR, and Water Excitation. The relationship between the logarithmic DW attenuation curves and b was linear for b values up to 600 s/mm(2) (R(2) > 0.999). Small differences were noted between the ADC values obtained with the various fat suppression methods, especially at the higher b values. Water Excitation had the highest mean SNR, exceeding STIR (p = 0.03) though not significantly different from fat saturation and SPAIR. In conclusion, the ADC of fibroglandular breast tissue is not influenced by microperfusion and Water Excitation is recommended because it yielded the best SNR values. These factors may be crucial in the differentiation between benign and malignant lesions.

Effect of diffusion-sensitizing gradient timings on the exponential, biexponential and diffusional kurtosis model parameters: in-vivo measurements in the rat thalamus

OBJECT

To investigate whether spacing (Delta) and duration (delta) of the diffusion-sensitizing gradient pulses differentially affect exponential (D'), biexponential (D (slow), D (fast) and f (slow)) and diffusional kurtosis (D and K) model parameters.

METHODS

Measurements were performed in the rat thalamus for b = 200-3,200 s mm(-2), sweeping Delta between 20 and 100 ms at delta = 15 ms, and delta between 15 and 50 ms at Delta = 60 ms. Linear regressions were performed for each model parameter vs. Delta or delta.

RESULTS

Increasing Delta from 20 to 100 ms increases D' (from 0.64 to 0.70 x 10(-3) mm(2)s(-1)) and D (slow) (from 0.26 to 0.33 x 10(-3) mm(2)s(-1)), reduces K (from 0.57 to 0.53), and has no effects on D (fast), f (slow) or D. Increasing delta from 15 to 50 ms increases D (from 0.80 to 0.88 x 10(-3) mm(2)s(-1)), and has no effects on the other parameters.

CONCLUSION

The parameters of the biexponential and diffusional kurtosis models are more sensitive than the exponential model to Delta and delta; however, observed effects are too small to account for the discrepancies found in literature.

O-GlcNAc modification of proteins affects volume regulation in Jurkat cells

An increasing amount of recent research has demonstrated that the hexosamine biosynthesis pathway (HBP) plays a significant role in the modulation of intracellular signaling transduction pathways, and affects cellular processes via modification of protein by O-linked beta-N-acetylglucosamine (O-GlcNAc). Besides the many known and postulated effects of protein O-GlcNAc modifications, there is little available data on the role of O-GlcNAc in cellular volume regulation. Our objective was to test the effect of increased O-GlcNAc levels on hypotonia-induced volume changes in Jurkat cells. We pretreated Jurkat cells for 1 h with glucosamine (GlcN), PUGNAc (O-(2-acetamido-2-deoxy-D-glucopyranosylidene)-amino-N-phenylcarbamate) an inhibitor of O-GlcNAcase, or a high level of glucose to induce elevated levels of O-GlcNAc. We found that the response of Jurkat cells to hypotonic stress was significantly altered. The hypotonia induced cell-swelling was augmented in both GlcN and PUGNAc-treated cells and, to a lesser extent, in high glucose concentration-treated cells. Evaluated by NMR measurements, GlcN and PUGNAc treatment also significantly reduced intracellular water diffusion. Taken together, increased cell swelling and reduced water diffusion caused by elevated O-GlcNAc show notable analogy to the regulatory volume changes seen by magnetic resonance methods in nervous and other tissues in different pathological states. In conclusion, we demonstrate for the first time that protein O-GlcNAc could modulate cell volume regulation.

On high b diffusion imaging in the human brain: ruminations and experimental insights

Interest in the manner in which brain tissue signal decays with b factor in diffusion imaging schemes has grown in recent years following the observation that the decay curves depart from purely monoexponential decay behavior. Regardless of the model or fitting function proposed for characterizing sufficiently sampled decay curves (vide infra), the departure from monoexponentiality spells increased tissue characterization potential. The degree to which this potential can be harnessed to improve specificity, sensitivity and spatial localization of diseases in brain, and other tissues, largely remains to be explored. Furthermore, the degree to which currently popular diffusion tensor imaging methods, including visually impressive white matter fiber "tractography" results, have almost completely ignored the nonmonoexponential nature of the basic signal decay with b factor is worthy of communal introspection. Here we limit our attention to a review of the basic experimental features associated with brain water signal diffusion decay curves as measured over extended b-factor ranges, the simple few parameter fitting functions that have been proposed to characterize these decays and the more involved models, e.g.,"ruminations," which have been proposed to account for the nonmonoexponentiality to date.

A multi-compartmental SE-BOLD interpretation for stimulus-related signal changes in diffusion-weighted functional MRI

A new interpretation is proposed for stimulus-induced signal changes in diffusion-weighted functional MRI. T(2)-weighted spin-echo echo-planar images were acquired at different diffusion-weightings while visual stimulation was presented to human volunteers. The amplitudes of the positive stimulus-correlated response and post-stimulus undershoot (PSU) in the functional time-courses were found to follow different trends as a function of b-value. Data were analysed using a three-compartment signal model, with one compartment being purely vascular and the other two dominated by fast- and slow-diffusing molecules in the brain tissue. The diffusion coefficients of the tissue were assumed to be constant throughout the experiments. It is shown that the stimulus-induced signal changes can be decomposed into independent contributions originating from each of the three compartments. After decomposition, the fast-diffusion phase displays a substantial PSU, while the slow-diffusion phase demonstrates a highly reproducible and stimulus-correlated time-course with minimal undershoot. The decomposed responses are interpreted in terms of the spin-echo blood oxygenation level dependent (SE-BOLD) effect, and it is proposed that the signal produced by fast- and slow-diffusing molecules reflect a sensitivity to susceptibility changes in arteriole/venule- and capillary-sized vessels, respectively. This interpretation suggests that diffusion-weighted SE-BOLD imaging may provide subtle information about the haemodynamic and neuronal responses.

Imaging parameter effects in apparent diffusion coefficient determination of magnetic resonance imaging

PURPOSE

Although an apparent diffusion coefficient (ADC) value is often used for differential diagnosis of tumours, it varies with scanning parameters. The present study was performed to investigate the influence of imaging parameters, i.e., b value, repetition time (TR) and echo time (TE), on ADC value.

METHODS

The phantoms were scanned using diffusion weighted imaging (DWI) with changing b values (b=0-3000 s/mm(2)), TR and TE to determine the influence on ADC. Moreover, ADC of the brain in normal volunteers was determined with varying b values (b=0-1000 s/mm(2)).

RESULTS

Diffusion decay curves were obtained by biexponential fitting in all phantoms. The points where fast and slow components of the biexponential decay crossed were called turning points. The b values of turning points that crossed from the biexponential curve were different in each phantom. The b values of turning points depended on ADC of fast diffusion component. When ADC is calculated using two b values of front and back for the turning point, the ADC value may be different. Therefore, it was necessary to perform calculations by b value until the turning point to obtain the ADC value of the fast component. In addition, b≥100 was recommended to avoid the influence of perfusion by blood. Furthermore, the choice of long TR and short TE was effective for accurate measurement of ADC.

CONCLUSION

It is important to determine the turning point for measuring ADC.

Diffusion-weighted MRI measurements on stroke patients reveal water-exchange mechanisms in sub-acute ischaemic lesions

The aim of this study was to investigate the diffusion time dependence of signal-versus-b curves obtained from diffusion-weighted magnetic resonance imaging (DW-MRI) of sub-acute ischaemic lesions in stroke patients. In this case series study, 16 patients with sub-acute ischaemic stroke were examined with DW-MRI using two different diffusion times (60 and 260 ms). Nine of these patients showed sufficiently large lesions without artefacts to merit further analysis. The signal-versus-b curves from the lesions were plotted and analysed using a two-compartment model including compartmental exchange. To validate the model and to aid the interpretation of the estimated model parameters, Monte Carlo simulations were performed. In eight cases, the plotted signal-versus-b curves, obtained from the lesions, showed a signal-curve split-up when data for the two diffusion times were compared, revealing effects of compartmental water exchange. For one of the patients, parametric maps were generated based on the extracted model parameters. These novel observations suggest that water exchange between different water pools is measurable and thus potentially useful for clinical assessment. The information can improve the understanding of the relationship between the DW-MRI signal intensity and the microstructural properties of the lesions.

Reduced encoding diffusion spectrum imaging implemented with a bi-Gaussian model

Diffusion spectrum imaging (DSI) can map complex fiber microstructures in tissues by characterizing their 3-D water diffusion spectra. However, a long acquisition time is required for adequate q-space sampling to completely reconstruct the 3-D diffusion probability density function. Furthermore, to achieve a high q-value encoding for sufficient spatial resolution, the diffusion gradient duration and the diffusion time are usually lengthened on a clinical scanner, resulting in a long echo time and low signal-to-noise ratio of diffusion-weighted images. To bypass long acquisition times and strict gradient requirements, the reduced-encoding DSI (RE-DSI) with a bi-Gaussian diffusion model is presented in this study. The bi-Gaussian extrapolation kernel, based on the assumption of the bi-Gaussian diffusion signal curve across biological tissue, is applied to the reduced q-space sampling data in order to fulfill the high q-value requirement. The crossing phantom model and the manganese-enhanced rat model served as standards for accuracy assessment in RE-DSI. The errors of RE-DSI in estimating fiber orientations were close to the noise limit. Meanwhile, evidence from a human study demonstrated that RE-DSI significantly decreased the acquisition time required to resolve complex fiber orientations. The presented method facilitates the application of DSI analysis on a clinical magnetic resonance imaging system.

Effects of intra- and extracellular space properties on diffusion and T(2) relaxation in a tissue model

The purpose of this study was to investigate the effects of biophysical factors on the diffusion and the relaxation time T(2) independently. Certain properties of the extracellular and the intracellular space may change radically in pathological conditions resulting in water diffusion changes. A tissue model consisting of red blood cells was studied. The extra- and intracellular spaces were modified osmotically and by suspending medium concentration. Diffusion measurements were evaluated with regard to the effective medium theory. Neither the nature of the protein in the extracellular space nor an increased level of intracellular hydration caused a significant net water diffusion change in the cell suspension. The relaxation time T(2) exhibited very little dependence on the extracellular volume fraction or the concentration or the nature of the protein in the extracellular space. An increased level of intracellular hydration resulted in systematically larger T(2) values. It seems probable that increases in extracellular protein concentrations or in the extent of intracellular hydration do not play a significant role in the diffusion changes detected in pathological conditions. T(2) appears to depend on the level of hydration or the total water content but is seemingly less dependent of the concentration and the nature of the extracellular protein in our model solutions.

Biexponential and diffusional kurtosis imaging, and generalised diffusion-tensor imaging (GDTI) with rank-4 tensors: a study in a group of healthy subjects

OBJECT

Clinical diffusion imaging is based on two assumptions of limited validity: that the radial projections of the diffusion propagator are Gaussian, and that a single directional diffusivity maximum exists in each voxel. The former can be removed using the biexponential and diffusional kurtosis models, the latter using generalised diffusion-tensor imaging. This study provides normative data for these three models.

MATERIALS AND METHODS

Eighteen healthy subjects were imaged. Maps of the biexponential parameters D (fast), D (slow) and f (slow), of D and K from the diffusional kurtosis model, and of diffusivity D' were obtained. Maps of generalised anisotropy (GA) and scaled entropy(SE) were also generated, for second and fourth rank tensors. Normative values were obtained for 26 regions.

RESULTS

In grey versus white matter, D (slow) and D' were higher and D (fast), f (slow) and K were lower. With respect to maps of D', anatomical contrast was stronger in maps of D (slow) and K. Elevating tensor rank increased SE, generally more significantly than GA, in: anterior limb of internal capsule, corpus callosum, deep frontal and subcortical white matter, along superior longitudinal fasciculus and cingulum.

CONCLUSION

The values reported herein can be used for reference in future studies and in clinical settings.