Determination of the appropriate b value and number of gradient directions for high‐angular‐resolution diffusion‐weighted imaging

High‐angular‐resolution diffusion‐weighted imaging (HARDI) is one of the most common MRI acquisition schemes for use with higher order models of diffusion. However, the optimal b value and number of diffusion‐weighted (DW) directions for HARDI are still undetermined, primarily as a result of the large number of available reconstruction methods and corresponding parameters, making it impossible to identify a single criterion by which to assess performance. In this study, we estimate the minimum number of DW directions and optimal b values required for HARDI by focusing on the angular frequency content of the DW signal itself. The spherical harmonic (SH) series provides the spherical analogue of the Fourier series, and can hence be used to examine the angular frequency content of the DW signal. Using high‐quality data acquired along 500 directions over a range of b values, we estimate that SH terms above l = 8 are negligible in practice for b values up to 5000 s/mm², implying that a minimum of 45 DW directions is sufficient to fully characterise the DW signal. l > 0 SH terms were found to increase as a function of b value, levelling off at b = 3000 s/mm², suggesting that this value already provides the highest achievable angular resolution. In practice, it is recommended to acquire more than the minimum of 45 DW directions to avoid issues with imperfections in the uniformity of the DW gradient directions and to meet signal‐to‐noise requirements of the intended reconstruction method. Copyright © 2013 John Wiley & Sons, Ltd.     

Diffusion‐weighted MRI for staging and evaluation of response in diffuse large B‐cell lymphoma: a pilot study

The aim of this study was to compare diffusion‐weighted MRI (DW‐MRI) with positron emission tomography/computed tomography (PET/CT) for the staging and evaluation of the treatment response in patients with diffuse large B‐cell lymphoma (DLBCL). Institutional review board approval was obtained for this study; all subjects gave informed consent. Twelve patients were imaged before treatment and eight of these were also imaged after two cycles of chemotherapy using both DW‐MRI and PET/CT. Up to six target lesions were selected at baseline for response assessment based on International Working Group criteria (nodes > 1.5 cm in diameter; extranodal lesions > 1 cm in diameter). For pretreatment staging, visual analysis of the numbers of nodal and extranodal lesions based on PET/CT was performed. For interim response assessment after cycle 2 of chemotherapy, residual tumor sites were assessed visually and the percentage changes in target lesion size, maximum standardized uptake value (SUV_max) and apparent diffusion coefficient (ADC) from pretreatment values were calculated. In 12 patients studied pretreatment, there were 46 nodal and 16 extranodal sites of lymphomatous involvement. Agreement between DW‐MRI and PET/CT for overall lesion detection was 97% (60/62 tumor sites; 44/46 nodal and 16/16 extranodal lesions) and, for Ann Arbor stage, it was 100%. In the eight patients who had interim assessment, five of their 49 tumor sites remained abnormal on visual analysis of both DW‐MRI and PET/CT, and there was one false positive on DW‐MRI. Of their 24 target lesions, the mean pretreatment ADC value, tumor size and SUV_max were 772 µm²/s, 21.3 cm² and 16.9 g/mL, respectively. At interim assessment of the same 24 target lesions, ADC values increased by 85%, tumor size decreased by 74% and SUV_max decreased by 83% (all p < 0.01 versus baseline). DW‐MRI provides results comparable with those of PET/CT for staging and early response assessment in patients with DLBCL. Copyright © 2014 John Wiley & Sons, Ltd.     

Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion‐weighted MRI

In this study, we evaluate whether diffusion‐weighted magnetic resonance imaging (DW‐MRI) data after denoising can provide a reliable estimation of brain intravoxel incoherent motion (IVIM) perfusion parameters. Brain DW‐MRI was performed in five healthy volunteers on a 3 T clinical scanner with 12 different b‐values ranging from 0 to 1000 s/mm². DW‐MRI data denoised using the proposed method were fitted with a biexponential model to extract perfusion fraction (PF), diffusion coefficient (D) and pseudo‐diffusion coefficient (D*). To further evaluate the accuracy and precision of parameter estimation, IVIM parametric images obtained from one volunteer were used to resimulate the DW‐MRI data using the biexponential model with the same b‐values. Rician noise was added to generate DW‐MRI data with various signal‐to‐noise ratio (SNR) levels. The experimental results showed that the denoised DW‐MRI data yielded precise estimates for all IVIM parameters. We also found that IVIM parameters were significantly different between gray matter and white matter (P < 0.05), except for D* (P = 0.6). Our simulation results show that the proposed image denoising method displays good performance in estimating IVIM parameters (both bias and coefficient of variation were <12% for PF, D and D*) in the presence of different levels of simulated Rician noise (SNR_b=0 = 20‐40). Simulations and experiments show that brain DW‐MRI data after denoising can provide a reliable estimation of IVIM parameters.     

Diffusion‐weighted MRI of the prostate without susceptibility artifacts: Undersampled multi‐shot turbo‐STEAM with rotated radial trajectories

The aim of this study was to develop and evaluate a clinically feasible approach to diffusion‐weighted (DW) MRI of the prostate without susceptibility‐induced artifacts. The proposed method relies on an undersampled multi‐shot DW turbo‐STEAM sequence with rotated radial trajectories and a multi‐step inverse reconstruction with denoised multi‐shot phase maps. The total acquisition time was below 6 min for a resolution of 1.4 × 1.4 × 3.5 mm³ and six directions at b = 600 s mm^?2. Studies of eight healthy subjects and two patients with prostate cancer were performed at 3 T employing an 18‐channel body‐array coil and elements of the spine coil. The method was compared with conventional DW echo‐planar imaging (EPI) of the prostate. The results confirm that DW STEAM MRI avoids geometric distortions and false image intensities, which were present for both single‐shot EPI (ssEPI) and readout‐segmented EPI, particularly near the intestinal wall of the prostate. Quantitative accuracy of the apparent diffusion coefficient (ADC) was validated with use of a numerical phantom providing ground truth. ADC values in the central prostate gland of healthy subjects were consistent with those measured using ssEPI and with literature data. Preliminary results for patients with prostate cancer revealed a correct anatomical localization of lesions with respect to T₂‐weighted MRI in both mean DW STEAM images and ADC maps. In summary, DW STEAM MRI of the prostate offers clinically relevant advantages for the diagnosis of prostate cancer compared with state‐of‐the‐art EPI‐based approaches. The method warrants extended clinical trials.     

A data‐driven approach to optimising the encoding for multi‐shell diffusion MRI with application to neonatal imaging

Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, noninvasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi‐shell data, with diffusion sensitisation applied along many directions over multiple b‐value shells. Such schemes are characterised by the number of shells acquired, and the specific b‐value and number of directions sampled for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi‐shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project (dHCP), which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of 20 b=0 images and diffusion‐weighted images at b = 400, 1000 and 2600 s/mm² with 64, 88 and 128 directions per shell, respectively.     

Automated pixel‐wise brain tissue segmentation of diffusion‐weighted images via machine learning

The diffusion‐weighted (DW) MR signal sampled over a wide range of b‐values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T₂ relaxivity. This study aimed to implement a machine learning algorithm for automatic brain tissue segmentation from DW‐MRI datasets, and to determine the optimal sub‐set of features for accurate segmentation. DWI was performed at 3 T in eight healthy volunteers using 15 b‐values and 20 diffusion‐encoding directions. The pixel‐wise signal attenuation, as well as the trace and fractional anisotropy (FA) of the diffusion tensor, were used as features to train a support vector machine classifier for gray matter, white matter, and cerebrospinal fluid classes. The datasets of two volunteers were used for validation. For each subject, tissue classification was also performed on 3D T₁‐weighted data sets with a probabilistic framework. Confusion matrices were generated for quantitative assessment of image classification accuracy in comparison with the reference method. DWI‐based tissue segmentation resulted in an accuracy of 82.1% on the validation dataset and of 82.2% on the training dataset, excluding relevant model over‐fitting. A mean Dice coefficient (DSC) of 0.79 ± 0.08 was found. About 50% of the classification performance was attributable to five features (i.e. signal measured at b‐values of 5/10/500/1200 s/mm² and the FA). This reduced set of features led to almost identical performances for the validation (82.2%) and the training (81.4%) datasets (DSC = 0.79 ± 0.08). Machine learning techniques applied to DWI data allow for accurate brain tissue segmentation based on both morphological and functional information.     

Diffusion‐weighted imaging of the abdomen: Impact of b‐values on texture analysis features

The purpose of this work was to systematically assess the impact of the b‐value on texture analysis in MR diffusion‐weighted imaging (DWI) of the abdomen. In eight healthy male volunteers, echo‐planar DWI sequences at 16 b‐values ranging between 0 and 1000 s/mm² were acquired at 3 T. Three different apparent diffusion coefficient (ADC) maps were computed (0, 750/100, 390, 750 s/mm²/all b‐values). Texture analysis of rectangular regions of interest in the liver, kidney, spleen, pancreas, paraspinal muscle and subcutaneous fat was performed on DW images and the ADC maps, applying 19 features computed from the histogram, grey‐level co‐occurrence matrix (GLCM) and grey‐level run‐length matrix (GLRLM). Correlations between b‐values and texture features were tested with a linear and an exponential model; the best fit was determined by the smallest sum of squared residuals. Differences between the ADC maps were assessed with an analysis of variance. A Bonferroni‐corrected p‐value less than 0.008 (=0.05/6) was considered statistically significant. Most GLCM and GLRLM‐derived texture features (12–18 per organ) showed significant correlations with the b‐value. Four texture features correlated significantly with changing b‐values in all organs (p < 0.008). Correlation coefficients varied between 0.7 and 1.0. The best fit varied across different structures, with fat exhibiting mostly exponential (17 features), muscle mostly linear (12 features) and the parenchymatous organs mixed feature alterations. Two GLCM features showed significant variability in the different ADC maps. Several texture features vary systematically in healthy tissues at different b‐values, which needs to be taken into account if DWI data with different b‐values are analyzed. Histogram and GLRLM‐derived texture features are stable on ADC maps computed from different b‐values.     

Diffusion‐weighted MRI monitoring of pancreatic cancer response to radiofrequency heat‐enhanced intratumor chemotherapy

The aim of this study was to evaluate the feasibility of using diffusion‐weighted MRI to monitor the early response of pancreatic cancers to radiofrequency heat (RFH)‐enhanced chemotherapy. Human pancreatic carcinoma cells (PANC‐1) in different groups and 24 mice with pancreatic cancer xenografts in four groups were treated with phosphate‐buffered saline (PBS) as a control, RFH at 42 °C, gemcitabine and gemcitabine plus RFH at 42 °C. One day before and 1, 7 and 14 days after treatment, diffusion‐weighted MRI and T₂‐weighted imaging were applied to monitor the apparent diffusion coefficients (ADCs) of tumors and tumor growth. MRI findings were correlated with the results of tumor apoptosis analysis. In the in vitro experiments, the quantitative viability assay showed lower relative cell viabilities for treatment with gemcitabine plus RFH at 42 °C relative to treatment with RFH only and gemcitabine only (37 ± 5% versus 65 ± 4% and 58 ± 8%, respectively, p < 0.05). In the in vivo experiments, the combination therapy resulted in smaller relative tumor volumes than RFH only and chemotherapy only (0.82 ± 0.17 versus 2.23 ± 0.90 and 1.64 ± 0.44, respectively, p = 0.003). In vivo, 14‐T MRI demonstrated a remarkable decrease in ADCs at day 1 and increased ADCs at days 7 and 14 in the combination therapy group. The apoptosis index in the combination therapy group was significantly higher than those in the chemotherapy‐only, RFH‐only and PBS treatment groups (37 ± 6% versus 20 ± 5%, 8 ± 2% and 3 ± 1%, respectively, p < 0.05). This study confirms that it is feasible to use MRI to monitor RFH‐enhanced chemotherapy in pancreatic cancers, which may present new options for the efficient treatment of pancreatic malignancies using MRI/RFH‐integrated local chemotherapy. Copyright © 2013 John Wiley & Sons, Ltd.     

Diffusion‐prepared stimulated‐echo turbo spin echo (DPsti‐TSE): An eddy current‐insensitive sequence for three‐dimensional high‐resolution and undistorted diffusion‐weighted imaging

In this study, we present a new three‐dimensional (3D), diffusion‐prepared turbo spin echo sequence based on a stimulated‐echo read‐out (DPsti‐TSE) enabling high‐resolution and undistorted diffusion‐weighted imaging (DWI). A dephasing gradient in the diffusion preparation module and rephasing gradients in the turbo spin echo module create stimulated echoes, which prevent signal loss caused by eddy currents. Near to perfect agreement of apparent diffusion coefficient (ADC) values between DPsti‐TSE and diffusion‐weighted echo planar imaging (DW‐EPI) was demonstrated in both phantom transient signal experiments and phantom imaging experiments. High‐resolution and undistorted DPsti‐TSE was demonstrated in vivo in prostate and carotid vessel wall. 3D whole‐prostate DWI was achieved with four b values in only 6 min. Undistorted ADC maps of the prostate peripheral zone were obtained at low and high imaging resolutions with no change in mean ADC values [(1.60 ± 0.10) × 10^?3 versus (1.60 ± 0.02) × 10^?3 mm²/s]. High‐resolution 3D DWI of the carotid vessel wall was achieved in 12 min, with consistent ADC values [(1.40 ± 0.23) × 10^?3 mm²/s] across different subjects, as well as slice locations through the imaging volume. This study shows that DPsti‐TSE can serve as a robust 3D diffusion‐weighted sequence and is an attractive alternative to the traditional two‐dimensional DW‐EPI approaches.     

Improvement of accuracy of diffusion MRI using real‐time self‐gated data acquisition

Diffusion‐weighted and diffusion tensor MR imaging (DWI, DTI) techniques are generally performed with signal averaging of multiple measurements to improve the signal‐to‐noise ratio (SNR) and the accuracy of the diffusion measurement. Any discrepancy in the images between different averages causes errors which reduce the accuracy of the diffusion MRI measurements. In this report, a motion artifact reduction scheme with a real‐time self‐gated (RTSG) data acquisition for diffusion MRI using two‐dimensional echo planar imaging (2D EPI) is described. A subject's translational and rotational motions during application of the diffusion gradients induce an additional phase term and a shift of the echo‐peak position in the k‐space, respectively. These motions also reduce the magnitude of the echo‐peak. Based on these properties, we present a new scheme which monitors the position and the magnitude of the largest echo‐peak in the k‐space. The position and the magnitude of each average is compared to those of early averaging shot to determine if the differences are within or beyond the given threshold values. Motion corrupted data are reacquired in real time. Our preliminary results using RTSG indicate an improvement of both SNR and the accuracy of diffusion MRI measurements. Copyright © 2009 John Wiley & Sons, Ltd.     

Comparison of different MRI sequences in lesion detection and early response evaluation of diffuse large B‐cell lymphoma – a whole‐body MRI and diffusion‐weighted imaging study

To compare different MRI sequences for the detection of lesions and the evaluation of response to chemotherapy in patients with diffuse large B‐cell lymphoma (DLBCL), 18 patients with histology‐confirmed DLBCL underwent 3‐T MRI scanning prior to and 1 week after chemotherapy. The MRI sequences included T₁‐weighted pre‐ and post‐contrast, T₂‐weighted with and without fat suppression, and a single‐shot echo‐planar diffusion‐weighted imaging (DWI) with two b values (0 and 800 s/mm²). Conventional MRI sequence comparisons were performed using the contrast ratio between tumor and normal vertebral body instead of signal intensity. The apparent diffusion coefficient (ADC) of the tumor was measured directly on the parametric ADC map. The tumor volume was used as a reference for the evaluation of chemotherapy response. The mean tumor volume was 374 mL at baseline, and decreased by 65% 1 week after chemotherapy (p < 0.01). The T₂‐weighted image with fat suppression showed a significantly higher contrast ratio compared with images from all other conventional MRI sequences, both before and after treatment (p < 0.01, respectively). The contrast ratio of the T₂‐weighted image with fat suppression decreased significantly (p < 0.01), and that of the T₁‐weighted pre‐contrast image increased significantly (p < 0.01), after treatment. However, there was no correlation between the change in contrast ratio and tumor volume. The mean ADC value was 0.68 × 10^–3 mm²/s at baseline; it increased by 89% after chemotherapy (p < 0.001), and the change in ADC value correlated with the change in tumor volume (r = 0.66, p < 0.01). The baseline ADC value also correlated inversely with the percentage change in ADC after treatment (r = ?0.62, p < 0.01). In conclusion, this study indicates that T₂‐weighted imaging with fat suppression is the best conventional sequence for the detection of lesions and evaluation of the efficacy of chemotherapy in DLBCL. DWI with ADC mapping is an imaging modality with both diagnostic and prognostic value that could complement conventional MRI. Copyright © 2013 John Wiley & Sons, Ltd.     

Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi‐group comparison

A large number of mathematical models have been proposed to describe the measured signal in diffusion‐weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the ‘White Matter Modeling Challenge’ during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three‐quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion‐based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non‐Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal‐predicting strategies, such as bootstrapping or cross‐validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.     

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. T₂‐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. Copyright © 2009 John Wiley & Sons, Ltd.     

Assessment of non‐Gaussian diffusion with singly and doubly stretched biexponential models of diffusion‐weighted MRI (DWI) signal attenuation in prostate tissue

Non‐Gaussian diffusion dynamics was investigated in the two distinct water populations identified by a biexponential model of diffusion in prostate tissue. Diffusion‐weighted MRI (DWI) signal attenuation was measured ex vivo in two formalin‐fixed prostates at 9.4 T with diffusion times Δ = 10, 20 and 40 ms, and b values in the range 0.017–8.2 ms/µm². A conventional biexponential model was compared with models in which either the lower diffusivity component or both of the components of the biexponential were stretched. Models were compared using Akaike's Information Criterion (AIC) and a leave‐one‐out (LOO) test of model prediction accuracy. The doubly stretched (SS) model had the highest LOO prediction accuracy and lowest AIC (highest information content) in the majority of voxels at Δ = 10 and 20 ms. The lower diffusivity stretching factor (α₂) of the SS model was consistently lower (range ~0.3–0.9) than the higher diffusivity stretching factor (α₁, range ~0.7–1.1), indicating a high degree of diffusion heterogeneity in the lower diffusivity environment, and nearly Gaussian diffusion in the higher diffusivity environment. Stretched biexponential models demonstrate that, in prostate tissue, the two distinct water populations identified by the simple biexponential model individually exhibit non‐Gaussian diffusion dynamics. Copyright © 2015 John Wiley & Sons, Ltd.     

Stroke assessment with intravoxel incoherent motion diffusion‐weighted MRI

Intravoxel incoherent motion (IVIM) diffusion‐weighted MRI can simultaneously measure diffusion and perfusion characteristics in a non‐invasive way. This study aimed to determine the potential utility of IVIM in characterizing brain diffusion and perfusion properties for clinical stroke. The multi‐b‐value diffusion‐weighted images of 101 patients diagnosed with acute/subacute ischemic stroke were retrospectively evaluated. The diffusion coefficient D, representing the water apparent diffusivity, was obtained by fitting the diffusion data with increasing high b‐values to a simple mono‐exponential model. The IVIM‐derived perfusion parameters, pseudodiffusion coefficient D*, vascular volume fraction f and blood flow‐related parameter fD*, were calculated with the bi‐exponential model. Additionally, the apparent diffusion coefficient (ADC) was fitted according to the mono‐exponential model using all b‐values. The diffusion parameters for the ischemic lesion and normal contralateral region were measured in each patient. Statistical analysis was performed using the paired Student t‐test and Pearson correlation test. Diffusion data in both the ischemic lesion and normal contralateral region followed the IVIM bi‐exponential behavior, and the IVIM model showed better goodness of fit than the mono‐exponential model with lower Akaike information criterion values. The paired Student t‐test revealed significant differences for all diffusion parameters (all P < 0.001) except D* (P = 0.218) between ischemic and normal areas. For all patients in both ischemic and normal regions, ADC was significantly positively correlated with D (both r = 1, both P < 0.001) and f (r = 0.541, P < 0.001; r = 0.262, P = 0.008); significant correlation was also found between ADC and fD* in the ischemic region (r = 0.254, P = 0.010). For all pixels within the region of interest from a representative subject in both ischemic and normal regions, ADC was significantly positively correlated with D (both r = 1, both P < 0.001), f (r = 0.823, P < 0.001; r = 0.652, P < 0.001) and fD* (r = 0.294, P < 0.001; r = 0.340, P < 0.001). These findings may have clinical implications for the use of IVIM imaging in the assessment and management of acute/subacute stroke patients. Copyright © 2016 John Wiley & Sons, Ltd.     

Investigation of field and diffusion time dependence of the diffusion‐weighted signal at ultrahigh magnetic fields

Over the last decade, there has been a significant increase in the number of high‐magnetic‐field MRI magnets. However, the exact effect of a high magnetic field strength (B₀) on diffusion‐weighted MR signals is not yet fully understood. The goal of this study was to investigate the influence of different high magnetic field strengths (9.4 T and 14.1 T) and diffusion times (9, 11, 13, 15, 17 and 24 ms) on the diffusion‐weighted signal in rat brain white matter. At a short diffusion time (9 ms), fractional anisotropy values were found to be lower at 14.1 T than at 9.4 T, but this difference disappeared at longer diffusion times. A simple two‐pool model was used to explain these findings. The model describes the white matter as a first hindered compartment (often associated with the extra‐axonal space), characterized by a faster orthogonal diffusion and a lower fractional anisotropy, and a second restricted compartment (often associated with the intra‐axonal space), characterized by a slower orthogonal diffusion (i.e. orthogonal to the axon direction) and a higher fractional anisotropy. Apparent T₂ relaxation time measurements of the hindered and restricted pools were performed. The shortening of the pseudo‐T₂ value from the restricted compartment with B₀ is likely to be more pronounced than the apparent T₂ changes in the hindered compartment. This study suggests that the observed differences in diffusion tensor imaging parameters between the two magnetic field strengths at short diffusion time may be related to differences in the apparent T₂ values between the pools. Copyright © 2013 John Wiley & Sons, Ltd.     

Characterizing intra‐axonal water diffusion with direction‐averaged triple diffusion encoding MRI

For large diffusion weightings, the direction‐averaged diffusion MRI (dMRI) signal from white matter is typically dominated by the contribution of water confined to axons. This fact can be exploited to characterize intra‐axonal diffusion properties, which may be valuable for interpreting the biophysical meaning of diffusion changes associated with pathology. However, using just the classic Stejskal‐Tanner pulse sequence, it has proven challenging to obtain reliable estimates for both the intrinsic intra‐axonal diffusivity and the intra‐axonal water fraction. Here we propose to apply a modification of the Stejskal‐Tanner sequence designed for achieving such estimates. The key feature of the sequence is the addition of a set of extra diffusion encoding gradients that are orthogonal to the direction of the primary gradients, which corresponds to a specific type of triple diffusion encoding (TDE) MRI sequence. Given direction‐averaged dMRI data for this TDE sequence, it is shown how the intra‐axonal diffusivity and the intra‐axonal water fraction can be determined by applying simple, analytic formulae. The method is illustrated with numerical simulations, which suggest that it should be accurate for b‐values of about 4000 s/mm² or higher.     

Correlation of tumor characteristics derived from DCE‐MRI and DW‐MRI with histology in murine models of breast cancer

The purpose of this work was to determine the relationship between the apparent diffusion coefficient (ADC, from diffusion‐weighted (DW) MRI), the extravascular, extracellular volume fraction (v_e, from dynamic contrast‐enhanced (DCE) MRI), and histological measurement of the extracellular space fraction. Athymic nude mice were injected with either human epidermal growth factor receptor 2 positive (HER2+) BT474 (n = 15) or triple negative MDA‐MB‐231 (n = 20) breast cancer cells, treated with either Herceptin (n = 8), Abraxane (low dose n = 7, high dose n = 6), or saline (n = 7 for each cell line), and imaged using DW‐ and DCE‐MRI before, during, and after treatment. After the final imaging acquisition, the tissue was resected and evaluated by histological analysis. H&E‐stained central slices were scanned using a digital brightfield microscope and evaluated with thresholding techniques to calculate the extracellular space. For both BT474 and MDA‐MB‐231, the median ADC of the central slice exhibited a significantly positive correlation with the corresponding central slice extracellular space as measured by H&E (p = 0.03, p < 0.01, respectively). Median v_e calculated from the central slice showed differing results between the two cell lines. For BT474, a significant correlation between v_e and extracellular space was calculated (p = 0.02), while MDA‐MB‐231 tumors did not demonstrate a significant correlation (p = 0.64). Additionally, there was no correlation discovered between ADC and v_e with either whole tumor analysis or central slice analysis (p > 0.05). While ADC correlates well with the histologically determined fraction of extracellular space, these data add to the growing body of literature that suggests that v_e derived from DCE‐MRI is not a reliable biomarker of extracellular space for a range of physiological conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

An intravoxel oriented flow model for diffusion‐weighted imaging of the kidney

By combining intravoxel incoherent motion (IVIM) and diffusion tensor imaging (DTI) we introduce a new diffusion model called intravoxel oriented flow (IVOF) that accounts for anisotropy of diffusion and the flow‐related signal. An IVOF model using a simplified apparent flow fraction tensor (IVOF_f) is applied to diffusion‐weighted imaging of human kidneys. The kidneys of 13 healthy volunteers were examined on a 3 T scanner. Diffusion‐weighted images were acquired with six b values between 0 and 800 s/mm² and 30 diffusion directions. Diffusivity and flow fraction were calculated for different diffusion models. The Akaike information criterion was used to compare the model fit of the proposed IVOF_f model to IVIM and DTI. In the majority of voxels the proposed IVOF_f model with a simplified apparent flow fraction tensor performs better than IVIM and DTI. Mean diffusivity is significantly higher in DTI compared with models that account for the flow‐related signal. The fractional anisotropy of diffusion is significantly reduced when flow fraction is considered to be anisotropic. Anisotropy of the apparent flow fraction tensor is significantly higher in the renal medulla than in the cortex region. The IVOF_f model describes diffusion‐weighted data in the human kidney more accurately than IVIM or DTI. The apparent flow fraction in the kidney proved to be anisotropic.