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.     

Optimizing diffusion MRI acquisition efficiency of rodent brain using simultaneous multislice EPI

Diffusion tensor imaging (DTI) of the brain provides essential information on the white matter integrity and structural connectivity. However, it suffers from a low signal‐to‐noise ratio (SNR) and requires a long scan time to achieve high spatial and/or diffusion resolution and wide brain coverage. With recent advances in parallel and simultaneous multislice (multiband) imaging, the SNR efficiency has been improved by reducing the repetition time (T_R). However, due to the limited number of RF coil channels available on preclinical MRI scanners, simultaneous multislice acquisition has not been practical. In this study, we demonstrate the ability of multiband DTI to acquire high‐resolution data of the mouse brain with 84 slices covering the whole brain in 0.2 mm isotropic resolution without a coil array at 9.4 T. Hadamard‐encoding four‐band pulses were used to acquire four slices simultaneously, with the reduction in the T_R maximizing the SNR efficiency. To overcome shot‐to‐shot phase variations, Hadamard decoding with a self‐calibrated phase was developed. Compared with single‐band DTI acquired with the same scan time, the multiband DTI leads to significantly increased SNR by 40% in the white matter. This SNR gain resulted in reduced variations in fractional anisotropy, mean diffusivity, and eigenvector orientation. Furthermore, the cerebrospinal fluid signal was attenuated, leading to reduced free‐water contamination. Without the need for a high‐density coil array or parallel imaging, this technique enables highly efficient preclinical DTI that will facilitate connectome studies.     

Fast and accurate initialization of the free‐water imaging model parameters from multi‐shell diffusion MRI

Cerebrospinal fluid partial volume effect is a known bias in the estimation of Diffusion Tensor Imaging (DTI) parameters from diffusion MRI data. The Free‐Water Imaging model for diffusion MRI data adds a second compartment to the DTI model, which explicitly accounts for the signal contribution of extracellular free‐water, such as cerebrospinal fluid. As a result the DTI parameters obtained through the free‐water model are corrected for partial volume effects, and thus better represent tissue microstructure. In addition, the model estimates the fractional volume of free‐water, and can be used to monitor changes in the extracellular space. Under certain assumptions, the model can be estimated from single‐shell diffusion MRI data. However, by using data from multi‐shell diffusion acquisitions, these assumptions can be relaxed, and the fit becomes more robust. Nevertheless, fitting the model to multi‐shell data requires high computational cost, with a non‐linear iterative minimization, which has to be initialized close enough to the global minimum to avoid local minima and to robustly estimate the model parameters. Here we investigate the properties of the main initialization approaches that are currently being used, and suggest new fast approaches to improve the initial estimates of the model parameters. We show that our proposed approaches provide a fast and accurate initial approximation of the model parameters, which is very close to the final solution. We demonstrate that the proposed initializations improve the final outcome of non‐linear model fitting.     

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.     

Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation

We review, systematize and discuss models of diffusion in neuronal tissue, by putting them into an overarching physical context of coarse‐graining over an increasing diffusion length scale. From this perspective, we view research on quantifying brain microstructure as occurring along three major avenues. The first avenue focusses on transient, or time‐dependent, effects in diffusion. These effects signify the gradual coarse‐graining of tissue structure, which occurs qualitatively differently in different brain tissue compartments. We show that transient effects contain information about the relevant length scales for neuronal tissue, such as the packing correlation length for neuronal fibers, as well as the degree of structural disorder along the neurites. The second avenue corresponds to the long‐time limit, when the observed signal can be approximated as a sum of multiple nonexchanging anisotropic Gaussian components. Here, the challenge lies in parameter estimation and in resolving its hidden degeneracies. The third avenue employs multiple diffusion encoding techniques, able to access information not contained in the conventional diffusion propagator. We conclude with our outlook on future directions that could open exciting possibilities for designing quantitative markers of tissue physiology and pathology, based on methods of studying mesoscopic transport in disordered systems.     

Multi‐center evaluation of stability and reproducibility of quantitative MRI measures in healthy calf muscles

The purpose of this study was to evaluate temporal stability, multi‐center reproducibility and the influence of covariates on a multimodal MR protocol for quantitative muscle imaging and to facilitate its use as a standardized protocol for evaluation of pathology in skeletal muscle. Quantitative T2, quantitative diffusion and four‐point Dixon acquisitions of the calf muscles of both legs were repeated within one hour. Sixty‐five healthy volunteers (31 females) were included in one of eight 3‐T MR systems. Five traveling subjects were examined in six MR scanners. Average values over all slices of water‐T2 relaxation time, proton density fat fraction (PDFF) and diffusion metrics were determined for seven muscles. Temporal stability was tested with repeated measured ANOVA and two‐way random intraclass correlation coefficient (ICC). Multi‐center reproducibility of traveling volunteers was assessed by a two‐way mixed ICC. The factors age, body mass index, gender and muscle were tested for covariance. ICCs of temporal stability were between 0.963 and 0.999 for all parameters. Water‐T2 relaxation decreased significantly (P < 10^?3) within one hour by ~ 1 ms. Multi‐center reproducibility showed ICCs within 0.879–0.917 with the lowest ICC for mean diffusivity. Different muscles showed the highest covariance, explaining 20–40% of variance for observed parameters. Standardized acquisition and processing of quantitative muscle MRI data resulted in high comparability among centers. The imaging protocol exhibited high temporal stability over one hour except for water T2 relaxation times. These results show that data pooling is feasible and enables assembling data from patients with neuromuscular diseases, paving the way towards larger studies of rare muscle disorders.     

The impact of edema and fiber crossing on diffusion MRI metrics assessed in an ex vivo nerve phantom: Multi‐tensor model vs. diffusion orientation distribution function

Diffusion tensor imaging (DTI) has been employed for over 2 decades to noninvasively quantify central nervous system diseases/injuries. However, DTI is an inadequate simplification of diffusion modeling in the presence of coexisting inflammation, edema and crossing nerve fibers. We employed a tissue phantom using fixed mouse trigeminal nerves coated with various amounts of agarose gel to mimic crossing fibers in the presence of vasogenic edema. Diffusivity measures derived by DTI and diffusion basis spectrum imaging (DBSI) were compared at increasing levels of simulated edema and degrees of fiber crossing. Furthermore, we assessed the ability of DBSI, diffusion kurtosis imaging (DKI), generalized q‐sampling imaging (GQI), q‐ball imaging (QBI) and neurite orientation dispersion and density imaging to resolve fiber crossing, in reference to the gold standard angles measured from structural images. DTI‐computed diffusivities and fractional anisotropy were significantly confounded by gel‐mimicked edema and crossing fibers. Conversely, DBSI calculated accurate diffusivities of individual fibers regardless of the extent of simulated edema and degrees of fiber crossing angles. Additionally, DBSI accurately and consistently estimated crossing angles in various conditions of gel‐mimicked edema when compared with the gold standard (r² = 0.92, P = 1.9 × 10^?9, bias = 3.9°). Small crossing angles and edema significantly impact the diffusion orientation distribution function, making DKI, GQI and QBI less accurate in detecting and estimating fiber crossing angles. Lastly, we used diffusion tensor ellipsoids to demonstrate that DBSI resolves the confounds of edema and crossing fibers in the peritumoral edema region from a patient with lung cancer metastasis, while DTI failed. In summary, DBSI is able to separate two crossing fibers and accurately recover their diffusivities in a complex environment characterized by increasing crossing angles and amounts of gel‐mimicked edema. DBSI also indicated better angular resolution compared with DKI, QBI and GQI.     

Multi‐center reproducibility of neurochemical profiles in the human brain at 7 T

The purpose of this work was to harmonize data acquisition and post‐processing of single voxel proton MRS (¹H‐MRS) at 7 T, and to determine metabolite concentrations and the accuracy and reproducibility of metabolite levels in the adult human brain. This study was performed in compliance with local institutional human ethics committees. The same seven subjects were each examined twice using four different 7 T MR systems from two different vendors using an identical semi‐localization by adiabatic selective refocusing spectroscopy sequence. Neurochemical profiles were obtained from the posterior cingulate cortex (gray matter, GM) and the corona radiata (white matter, WM). Spectra were analyzed with LCModel, and sources of variation in concentrations (‘subject’, ‘institute’ and ‘random’) were identified with a variance component analysis. Concentrations of 10–11 metabolites, which were corrected for T₁, T₂, magnetization transfer effects and partial volume effects, were obtained with mean Cramér–Rao lower bounds below 20%. Data variances and mean concentrations in GM and WM were comparable for all institutions. The primary source of variance for glutamate, myo‐inositol, scyllo‐inositol, total creatine and total choline was between subjects. Variance sources for all other metabolites were associated with within‐subject and system noise, except for total N‐acetylaspartate, glutamine and glutathione, which were related to differences in signal‐to‐noise ratio and in shimming performance between vendors. After multi‐center harmonization of acquisition and post‐processing protocols, metabolite concentrations and the sizes and sources of their variations were established for neurochemical profiles in the healthy brain at 7 T, which can be used as guidance in future studies quantifying metabolite and neurotransmitter concentrations with ¹H‐MRS at ultra‐high magnetic field. Copyright © 2015 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.     

Multi‐chromatic magnetic resonance imaging using frequency lock‐in suppression

This study developed a multi‐chromatic MR contrast using the frequency lock‐in technique. An electronic feedback device that generates a specific narrow‐frequency‐bandwidth RF field is presented. The effects of this RF field on MR images are assessed both theoretically and experimentally. Spectroscopy and imaging experiments were performed. Frequency tuning allowed the selected spectral peak to be suppressed. Phantom tests using methanol, ethanol, and water showed different contrasts using different feedback RF field frequencies. The frequency lock‐in was also found to help differentiate among the small structural variations in biological tissues. The contrast achieved in in vivo mouse brain imaging using the lock‐in suppressed technique indicated a better spatial discrimination when compared with that achieved using conventional imaging methods, especially in the hippocampus region. Selective lock‐in suppressed imaging is a new approach to provide frequency information in MRI; rather than determining the evolution of image contrast over time, this approach allows small susceptibility variations to be distinguished by tuning the frequency of the narrow‐bandwidth lock‐in RF field. A new and enhanced contrast can be achieved using this technique. Copyright © 2015 John Wiley & Sons, Ltd.     

Time‐dependent diffusion MRI as a probe of microstructural changes in a mouse model of Duchenne muscular dystrophy

Dystrophic muscles show a high variability of fibre sizes and altered sarcolemmal integrity, which are typically assessed by histology. Time‐dependent diffusion MRI is sensitive to tissue microstructure and its investigation through age‐related changes in dystrophic and healthy muscles may help the understanding of the onset and progression of Duchenne muscular dystrophy (DMD). We investigated the capability of time‐dependent diffusion MRI to quantify age and disease‐related changes in hind‐limb muscle microstructure between dystrophic (mdx) and wild‐type (WT) mice of three age groups (7.5, 22 and 44 weeks). Diffusion time‐dependent apparent diffusion coefficients (ADCs) of the gastrocnemius and tibialis anterior muscles were determined versus age and diffusion‐gradient orientation at six diffusion times (Δ; range: 25–350 ms). Mean muscle ADCs were compared between groups and ages, and correlated with T₂, using Student's t test, one‐way analysis of variance and Pearson correlation, respectively. Muscle fibre sizes and sarcolemmal integrity were evaluated by histology and compared with diffusion measurements. Hind‐limb muscle ADC showed characteristic restricted diffusion behaviour in both mdx and WT animals with decreasing ADC values at longer Δ. Significant differences in ADC were observed at long Δ values (≥ 250 ms; p < 0.05, comparison between groups; p < 0.01, comparison between ages) with ADC increased by 5–15% in dystrophic muscles, indicative of reduced diffusion restriction. No significant correlation was found between T₂ and ADC. Additionally, muscle fibre size distributions showed higher variability and lower mean fibre size in mdx than WT animals (p < 0.001). The extensive Evans Blue Dye uptake shown in dystrophic muscles revealed substantial sarcolemmal damage, suggesting diffusion measurements as more consistent with altered permeability rather than changes in muscle fibre sizes. This study shows the potential of diffusion MRI to non‐invasively discriminate between dystrophic and healthy muscles with enhanced sensitivity when using long Δ.     

Comparison of reproducibility of single voxel spectroscopy and whole‐brain magnetic resonance spectroscopy imaging at 3T

To date, single voxel spectroscopy (SVS) is the most commonly used MRS technique. SVS is relatively easy to use and provides automated and immediate access to the resulting spectra. However, it is also limited in spatial coverage. A new and very promising MRS technique allows for whole‐brain MR spectroscopic imaging (WB‐MRSI) with much improved spatial resolution. Establishing the reproducibility of data obtained using SVS and WB‐MRSI is an important first step for using these techniques to evaluate longitudinal changes in metabolite concentration. The purpose of this study was to assess and directly compare the reproducibility of metabolite quantification at 3T using SVS and WB‐MRSI in ‘hand‐knob’ areas of motor cortices and hippocampi in healthy volunteers. Ten healthy adults were scanned using both SVS and WB‐MRSI on three occasions one week apart. N‐acetyl aspartate (NAA), creatine (Cr), choline (Cho) and myo‐inositol (mI) were quantified using SVS and WB‐MRSI with reference to both Cr and H₂O. The reproducibility of each technique was evaluated using the coefficient of variation (CV), and the correspondence between the two techniques was assessed using Pearson correlation analysis. The measured mean (range) intra‐subject CVs for SVS were 5.90 (2.65‐10.66)% for metabolites (i.e. NAA, Cho, mI) relative to Cr, and 8.46 (4.21‐21.07)% for metabolites (NAA, Cr, Cho, mI) relative to H₂O. The mean (range) CVs for WB‐MRSI were 7.56 (2.78‐11.41)% for metabolites relative to Cr, and 7.79 (4.57‐14.11)% for metabolites relative to H₂O. Significant positive correlations were observed between metabolites quantified using SVS and WB‐MRSI techniques when the Cr but not H₂O reference was used. The results demonstrate that reproducibilities of SVS and WB‐MRSI are similar for quantifying the four major metabolites (NAA, Cr, Cho, mI); both SVS and WB‐MRSI exhibited good reproducibility. Our findings add reference information for choosing the appropriate ¹H‐MRS technique in future studies.     

Multi‐slice MRI with the dynamic multi‐coil technique

To date, spatial encoding for MRI is based on linear X, Y and Z field gradients generated by dedicated X, Y and Z wire patterns. We recently introduced the dynamic multi‐coil technique (DYNAMITE) for the generation of magnetic field shapes for biomedical MR applications from a set of individually driven localized coils. The benefits for B₀ magnetic field homogenization have been shown, as well as proof of principle of radial and algebraic MRI. In this study the potential of DYNAMITE MRI is explored further and the first multi‐slice MRI implementation in which all gradient fields are purely DYNAMITE based is presented. The obtained image fidelity is shown to be virtually identical to that of a conventional MRI system with dedicated X, Y and Z gradient coils. Comparable image quality is a milestone towards the establishment of fully functional DYNAMITE MRI (and shim) systems. Copyright © 2015 John Wiley & Sons, Ltd.     

Three‐dimensional inversion recovery manganese‐enhanced MRI of mouse brain using super‐resolution reconstruction to visualize nuclei involved in higher brain function

The visualization of activity in mouse brain using inversion recovery spin echo (IR‐SE) manganese‐enhanced MRI (MEMRI) provides unique contrast, but suffers from poor resolution in the slice‐encoding direction. Super‐resolution reconstruction (SRR) is a resolution‐enhancing post‐processing technique in which multiple low‐resolution slice stacks are combined into a single volume of high isotropic resolution using computational methods. In this study, we investigated, first, whether SRR can improve the three‐dimensional resolution of IR‐SE MEMRI in the slice selection direction, whilst maintaining or improving the contrast‐to‐noise ratio of the two‐dimensional slice stacks. Second, the contrast‐to‐noise ratio of SRR IR‐SE MEMRI was compared with a conventional three‐dimensional gradient echo (GE) acquisition. Quantitative experiments were performed on a phantom containing compartments of various manganese concentrations. The results showed that, with comparable scan times, the signal‐to‐noise ratio of three‐dimensional GE acquisition is higher than that of SRR IR‐SE MEMRI. However, the contrast‐to‐noise ratio between different compartments can be superior with SRR IR‐SE MEMRI, depending on the chosen inversion time. In vivo experiments were performed in mice receiving manganese using an implanted osmotic pump. The results showed that SRR works well as a resolution‐enhancing technique in IR‐SE MEMRI experiments. In addition, the SRR image also shows a number of brain structures that are more clearly discernible from the surrounding tissues than in three‐dimensional GE acquisition, including a number of nuclei with specific higher brain functions, such as memory, stress, anxiety and reward behavior. Copyright © 2014 John Wiley & Sons, Ltd.     

The effects of ageing on mouse muscle microstructure: a comparative study of time‐dependent diffusion MRI and histological assessment

The investigation of age‐related changes in muscle microstructure between developmental and healthy adult mice may help us to understand the clinical features of early‐onset muscle diseases, such as Duchenne muscular dystrophy. We investigated the evolution of mouse hind‐limb muscle microstructure using diffusion imaging of in vivo and in vitro samples from both actively growing and mature mice. Mean apparent diffusion coefficients (ADCs) of the gastrocnemius and tibialis anterior muscles were determined as a function of diffusion time (Δ), age (7.5, 22 and 44 weeks) and diffusion gradient direction, applied parallel or transverse to the principal axis of the muscle fibres. We investigated a wide range of diffusion times with the goal of probing a range of diffusion lengths characteristic of muscle microstructure. We compared the diffusion time‐dependent ADC of hind‐limb muscles with histology. ADC was found to vary as a function of diffusion time in muscles at all stages of maturation. Muscle water diffusivity was higher in younger (7.5 weeks) than in adult (22 and 44 weeks) mice, whereas no differences were observed between the older ages. In vitro data showed the same diffusivity pattern as in vivo data. The highlighted differences in diffusion properties between young and mature muscles suggested differences in underlying muscle microstructure, which were confirmed by histological assessment. In particular, although diffusion was more restricted in older muscle, muscle fibre size increased significantly from young to adult age. The extracellular space decreased with age by only ~1%. This suggests that the observed diffusivity differences between young and adult muscles may be caused by increased membrane permeability in younger muscle associated with properties of the sarcolemma.     

Relationship between diffusion parameters derived from intravoxel incoherent motion MRI and perfusion measured by dynamic contrast‐enhanced MRI of soft tissue tumors

Our aim was to evaluate the link between diffusion parameters measured by intravoxel incoherent motion (IVIM) diffusion‐weighted imaging (DWI) and the perfusion metrics obtained with dynamic contrast‐enhanced (DCE) MRI in soft tissue tumors (STTs). Twenty‐eight patients affected by histopathologically confirmed STT were included in a prospective study. All patients underwent both DCE MRI and IVIM DWI. The perfusion fraction f, diffusion coefficient D and perfusion‐related diffusion coefficient D* were estimated using a bi‐exponential function to fit the DWI data. DCE MRI was acquired with a temporal resolution of 3–5 s. Maps of the initial area under the gadolinium concentration curve (IAUGC), time to peak (TTP) and maximum slope of increase (MSI) were derived using commercial software. The relationships between the DCE MRI and IVIM DWI measurements were assessed by Spearman's test. To exclude false positive results under multiple testing, the false discovery rate (FDR) procedure was applied. The Mann–Whitney test was used to evaluate the differences between all variables in patients with non‐myxoid and myxoid STT. No significant relationship was found between IVIM parameters and any DCE MRI parameters. Higher f and D*f values were found in non‐myxoid tumors compared with myxoid tumors (p = 0.004 and p = 0.003, respectively). MSI was significantly higher in non‐myxoid tumors than in myxoid tumors (p = 0.029). From the visual assessments of single clinical cases, both f and D*f maps were in satisfactory agreement with DCE maps in the extreme cases of an avascular mass and a highly vascularized mass, whereas, for tumors with slight vascularity or with a highly heterogeneous perfusion pattern, this association was not straightforward. Although IVIM DWI was demonstrated to be feasible in STT, our data did not support evident relationships between perfusion‐related IVIM parameters and perfusion measured by DCE MRI. Copyright © 2015 John Wiley & Sons, Ltd.     

Diffusion‐weighted MRI of breast lesions: a prospective clinical investigation of the quantitative imaging biomarker characteristics of reproducibility,repeatability, and diagnostic accuracy

Diffusion‐weighted MRI (DWI) provides insights into tissue microstructure by visualization and quantification of water diffusivity. Quantitative evaluation of the apparent diffusion coefficient (ADC) obtained from DWI has been proven helpful for differentiating between malignant and benign breast lesions, for cancer subtyping in breast cancer patients, and for prediction of response to neoadjuvant chemotherapy. However, to further establish DWI of breast lesions it is important to evaluate the quantitative imaging biomarker (QIB) characteristics of reproducibility, repeatability, and diagnostic accuracy. In this intra‐individual prospective clinical study 40 consecutive patients with suspicious findings, scheduled for biopsy, underwent an identical 3T breast MRI protocol of the breast on two consecutive days (>24 h). Mean ADC of target lesions was assessed (two independent readers) in four separate sessions. Reproducibility, repeatability, and diagnostic accuracy between examinations (E1, E2), readers (R1, R2), and measurements (M1, M2) were assessed with intraclass correlation coefficients (ICCs), coefficients of variation (CVs), Bland–Altman plots, and receiver operating characteristic (ROC) analysis with calculation of the area under the ROC curve (AUC). The standard of reference was either histopathology (n = 38) or imaging follow‐up of up to 24 months (n = 2). Eighty breast MRI examinations (median E1–E2, 2 ± 1.7 days, 95% confidence interval (CI) 1–2 days, range 1–11 days) in 40 patients (mean age 56, standard deviation (SD) ±14) were evaluated. In 55 target lesions (mean size 25.2 ± 20.8 (SD) mm, range 6–106 mm), mean ADC values were significantly (P < 0.0001) higher in benign (1.38, 95% CI 1.27–1.49 × 10^?3 mm²/s) compared with malignant (0.86, 95% CI 0.81–0.91 × 10^?3 mm²/s) lesions. Reproducibility and repeatability showed high agreement for repeated examinations, readers, and measurements (all ICCs >0.9, CVs 3.2–8%), indicating little variation. Bland–Altman plots demonstrated no systematic differences, and diagnostic accuracy was not significantly different in the two repeated examinations (all ROC curves >0.91, P > 0.05). High reproducibility, repeatability, and diagnostic accuracy of DWI provide reliable characteristics for its use as a potential QIB, to further improve breast lesion detection, characterization, and treatment monitoring of breast lesions.     

Evaluating kurtosis‐based diffusion MRI tissue models for white matter with fiber ball imaging

In order to quantify well‐defined microstructural properties of brain tissue from diffusion MRI (dMRI) data, tissue models are typically employed that relate biological features, such as cell morphology and cell membrane permeability, to the diffusion dynamics. A variety of such models have been proposed for white matter, and their validation is a topic of active interest. In this paper, three different tissue models are tested by comparing their predictions for a specific microstructural parameter to a value measured independently with a recently proposed dMRI method known as fiber ball imaging (FBI). The three tissue models are all constructed with the diffusion and kurtosis tensors, and they are hence compatible with diffusional kurtosis imaging. Nevertheless, the models differ significantly in their details and predictions. For voxels with fractional anisotropies (FAs) exceeding 0.5, all three are reasonably consistent with FBI. However, for lower FA values, one of these, called the white matter tract integrity (WMTI) model, is found to be in much better accord with FBI than the other two, suggesting that the WMTI model has a broader range of applicability.     

Imaging brain microstructure with diffusion MRI: practicality and applications

This article gives an overview of microstructure imaging of the brain with diffusion MRI and reviews the state of the art. The microstructure‐imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. Imaging techniques of this type are just starting to make the transition from the technical research domain to wide application in biomedical studies. We focus here on the practicalities of both implementing such techniques and using them in applications. Specifically, the article summarizes the relevant aspects of brain microanatomy and the range of diffusion‐weighted MR measurements that provide sensitivity to them. It then reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure, as well as the expanding areas of application. Next we focus on practicalities of designing a working microstructure imaging technique: model selection, experiment design, parameter estimation, validation, and the pipeline of development of this class of technique. The article concludes with some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short‐to‐medium term.