Combined intravoxel incoherent motion and diffusion tensor imaging of renal diffusion and flow anisotropy

The Use of Diffusion Tensor Imaging in the Identification of Acute Rejection and Chronic Allograft Nephropathy After Renal Transplantation

BACKGROUND

Identifying the cause of renal allograft dysfunction is important for the clinical management of kidney transplant recipients.

PURPOSE

To evaluate the diagnostic efficiency of diffusion tensor imaging (DTI) for identifying allografts with acute rejection (AR) and chronic allograft nephropathy (CAN).

STUDY TYPE

Prospective.

SUBJECTS

Seventy-seven renal transplant patients (aged 42.5 ± 9.5 years), including 29 patients with well-functioning stable allografts (Control group), 25 patients diagnosed with acute rejection (AR group), and 23 patients diagnosed with chronic allograft nephropathy (CAN group).

FIELD STRENGTH/SEQUENCE

1.5 T/T2-weighted imaging and DTI.

ASSESSMENT

The serum creatinine, proteinuria, pathologic results, and fractional anisotropy (FA) values were obtained and compared among the three groups.

STATISTICAL TEST

One-way analysis of variance; correlation analysis; independent-sample t-test; intraclass correlation coefficients and receiver operating characteristic curves. Statistical significance was set to a P-value <0.05.

RESULTS

The AR and CAN groups presented with significantly elevated serum creatinine as compared with the Control group (191.8 ± 181.0 and 163.1 ± 115.8 μmol/L vs. 82.3 ± 20.9 μmol/L). FA decreased in AR group (cortical/medullary: 0.13 ± 0.02/0.31 ± 0.07) and CAN group (cortical/medullary: 0.11 ± 0.02/0.27 ± 0.06), compared with the Control group (cortical/medullary: 0.15 ± 0.02/0.35 ± 0.05). Cortical FA in the AR group was higher than in the CAN group. The area under the curve (AUC) for identifying AR from normal allografts was 0.756 and 0.744 by cortical FA and medullary FA, respectively. The AUC of cortical FA and medullary FA for differentiating CAN from normal allografts was 0.907 and 0.830, respectively. The AUC of cortical FA and medullary FA for distinguishing AR and CAN from normal allografts was 0.828 and 0.785, respectively. Cortical FA was able to distinguish between AR and CAN with an AUC of 0.728.

DATA CONCLUSION

DTI was able to detect patients with dysfunctional allografts. Cortical FA can further distinguish between AR and CAN.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Spatial profiling of in vivo diffusion-weighted MRI parameters in the healthy human kidney

OBJECTIVE

Diffusion-weighted MRI is a technique that can infer microstructural and microcirculatory features from biological tissue, with particular application to renal tissue. There is extensive literature on diffusion tensor imaging (DTI) of anisotropy in the renal medulla, intravoxel incoherent motion (IVIM) measurements separating microstructural from microcirculation effects, and combinations of the two. However, interpretation of these features and adaptation of more specific models remains an ongoing challenge. One input to this process is a whole organ distillation of corticomedullary contrast of diffusion metrics, as has been explored for other renal biomarkers.

MATERIALS AND METHODS

In this work, we probe the spatial dependence of diffusion MRI metrics with concentrically layered segmentation in 11 healthy kidneys at 3 T. The metrics include those from DTI, IVIM, a combined approach titled "REnal Flow and Microstructure AnisotroPy (REFMAP)", and a multiply encoded model titled "FC-IVIM" providing estimates of fluid velocity and branching length.

RESULTS

Fractional anisotropy decreased from the inner kidney to the outer kidney with the strongest layer correlation in both parenchyma (including cortex and medulla) and medulla with Spearman correlation coefficients and p-values (r, p) of (0.42, <0.001) and (0.37, <0.001), respectively. Also, dynamic parameters derived from the three models significantly decreased with a high correlation from the inner to the outer parenchyma or medulla with (r, p) ranges of (0.46-0.55, <0.001).

CONCLUSIONS

These spatial trends might find implications for indirect assessments of kidney physiology and microstructure using diffusion MRI.

Renal MRI: From Nephron to NMR Signal

Renal diseases pose a significant socio-economic burden on healthcare systems. The development of better diagnostics and prognostics is well-recognized as a key strategy to resolve these challenges. Central to these developments are MRI biomarkers, due to their potential for monitoring of early pathophysiological changes, renal disease progression or treatment effects. The surge in renal MRI involves major cross-domain initiatives, large clinical studies, and educational programs. In parallel with these translational efforts, the need for greater (patho)physiological specificity remains, to enable engagement with clinical nephrologists and increase the associated health impact. The ISMRM 2022 Member Initiated Symposium (MIS) on renal MRI spotlighted this issue with the goal of inspiring more solutions from the ISMRM community. This work is a summary of the MIS presentations devoted to: 1) educating imaging scientists and clinicians on renal (patho)physiology and demands from clinical nephrologists, 2) elucidating the connection of MRI parameters with renal physiology, 3) presenting the current state of leading MR surrogates in assessing renal structure and functions as well as their next generation of innovation, and 4) describing the potential of these imaging markers for providing clinically meaningful renal characterization to guide or supplement clinical decision making. We hope to continue momentum of recent years and introduce new entrants to the development process, connecting (patho)physiology with (bio)physics, and conceiving new clinical applications. We envision this process to benefit from cross-disciplinary collaboration and analogous efforts in other body organs, but also to maximally leverage the unique opportunities of renal physiology. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.

Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions

Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre-urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion-weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion-weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non-Gaussian diffusion, and hybrid IVIM-DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Preliminary Feasibility Study on Diffusion Kurtosis Imaging to Monitor the Early Functional Alterations of Kidneys in Streptozocin-Induced Diabetic Rats

RATIONALE AND OBJECTIVES

The aim of this study was to investigate the potential of diffusion kurtosis imaging (DKI) to assess the early renal functional undulation of diabetic mellitus (DM).

MATERIALS AND METHODS

Fifty-seven Sprague-Dawley (SD) rats were randomly divided into two groups and eventually 48 rats were included in this study: the normal control (CON) group and diabetic mellitus (DM) group. Weeks 0, 4, 8, and 12 after the diabetes model was successfully established, all the rats were scanned on the 3.0T MRI. The DKI derived parameters of renal parenchyma, including fractional anisotropy (FAco, FAme), mean diffusivity (MDco, MDme), and mean kurtosis (MKco, MKme) were measured. Their alteration over time was analyzed and then correlated with urine volume (UV), blood urea nitrogen (BUN), and serum creatinine (Scr) using Pearson correlation analysis. Finally, hematoxylin and eosin (H&E) staining was performed on the kidneys of the two groups.

RESULT

There was a decreasing trend in FA, MK, and MD values over time in diabetic rats. Also, the gradually worsening histological damage of kidneys was noted over time in diabetic rats. The cortical FA and MK values and medullary FA, MK and MD values of diabetic rats were significantly lower than those of controls at most time points after DM induction. In addition, negative correlations were revealed between the BUN and FAco (r = -0.43, p = 0.03) or FAme value (r = -0.49, p = 0.01). The cortical MK value was moderately correlated with UV (r = -0.46, p = 0.03) and BUN (r = -0.55, p = 0.01).

CONCLUSION

The preliminary findings suggest that DKI might be an effective and sensitive tool to assess the early changes of renal function impairment in diabetic rats. The FA values of the cortex and medulla and the MK value of the cortex are sensitive markers in detecting renal injury in diabetic rats.

Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition

The acquisition of intravoxel incoherent motion (IVIM) data and diffusion tensor imaging (DTI) data from the brain can be integrated into a single measurement, which offers the possibility to determine orientation-dependent (tensorial) perfusion parameters in addition to established IVIM and DTI parameters. The purpose of this study was to evaluate the feasibility of such a protocol with a clinically feasible scan time below 6 min and to use a model-selection approach to find a set of DTI and IVIM tensor parameters that most adequately describes the acquired data. Diffusion-weighted images of the brain were acquired at 3 T in 20 elderly participants with cerebral small vessel disease using a multiband echoplanar imaging sequence with 15 b-values between 0 and 1000 s/mm² and six non-collinear diffusion gradient directions for each b-value. Seven different IVIM-diffusion models with 4 to 14 parameters were implemented, which modeled diffusion and pseudo-diffusion as scalar or tensor quantities. The models were compared with respect to their fitting performance based on the goodness of fit (sum of squared fit residuals, chi² ) and their Akaike weights (calculated from the corrected Akaike information criterion). Lowest chi² values were found using the model with the largest number of model parameters. However, significantly highest Akaike weights indicating the most appropriate models for the acquired data were found with a nine-parameter IVIM-DTI model (with isotropic perfusion modeling) in normal-appearing white matter (NAWM), and with an 11-parameter model (IVIM-DTI with additional pseudo-diffusion anisotropy) in white matter with hyperintensities (WMH) and in gray matter (GM). The latter model allowed for the additional calculation of the fractional anisotropy of the pseudo-diffusion tensor (with a median value of 0.45 in NAWM, 0.23 in WMH, and 0.36 in GM), which is not accessible with the usually performed IVIM acquisitions based on three orthogonal diffusion-gradient directions.

Cardiac Phase and Flow Compensation Effects on REnal Flow and Microstructure AnisotroPy MRI in Healthy Human Kidney

BACKGROUND

Renal diffusion-weighted imaging (DWI) involves microstructure and microcirculation, quantified with diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and hybrid models. A better understanding of their contrast may increase specificity.

PURPOSE

To measure modulation of DWI with cardiac phase and flow-compensated (FC) diffusion gradient waveforms.

STUDY TYPE

Prospective.

POPULATION

Six healthy volunteers (ages: 22-48 years, five females), water phantom.

FIELD STRENGTH/SEQUENCE

3-T, prototype DWI sequence with 2D echo-planar imaging, and bipolar (BP) or FC gradients. 2D Half-Fourier Single-shot Turbo-spin-Echo (HASTE). Multiple-phase 2D spoiled gradient-echo phase contrast (PC) MRI.

ASSESSMENT

BP and FC water signal decays were qualitatively compared. Renal arteries and velocities were visualized on PC-MRI. Systolic (peak velocity), diastolic (end stable velocity), and pre-systolic (before peak velocity) phases were identified. Following mutual information-based retrospective self-registration of DWI within each kidney, and Marchenko-Pastur Principal Component Analysis (MPPCA) denoising, combined IVIM-DTI analysis estimated mean diffusivity (MD), fractional anisotropy (FA), and eigenvalues (λi) from tissue diffusivity (Dt ), perfusion fraction (fp ), and pseudodiffusivity (Dp , Dp,axial , Dp,radial ), for each tissue (cortex/medulla, segmented on b0/FA respectively), phase, and waveform (BP, FC). Monte Carlo water diffusion simulations aided data interpretation.

STATISTICAL TESTS

Mixed model regression probed differences between tissue types and pulse sequences. Univariate general linear model analysis probed variations among cardiac phases. Spearman correlations were measured between diffusion metrics and renal artery velocities. Statistical significance level was set at P < 0.05.

RESULTS

Water BP and FC signal decays showed no differences. Significant pulse sequence dependence occurred for λ1 , λ3 , FA, Dp , fp , Dp,axial , Dp,radial in cortex and medulla, and medullary λ2 . Significant cortex/medulla differences occurred with BP for all metrics except MD (systole [P = 0.224]; diastole [P = 0.556]). Significant phase dependence occurred for Dp , Dp,axial , Dp,radial for BP and medullary λ1 , λ2 , λ3 , MD for FC. FA correlated significantly with velocity. Monte Carlo simulations indicated medullary measurements were consistent with a 34 μm tubule diameter.

DATA CONCLUSION

Cardiac gating and flow compensation modulate of measurements of renal diffusion.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY STAGE: 2.

Model-based reconstructions for intravoxel incoherent motion and diffusion tensor imaging parameter map estimations

Intravoxel incoherent motion (IVIM) imaging and diffusion tensor imaging (DTI) facilitate noninvasive quantification of tissue perfusion and diffusion. Both are promising biomarkers in various diseases and a combined acquisition is therefore desirable. This comes with challenges, including noisy parameter maps and long scan times, especially for the perfusion fraction f and pseudo-diffusion coefficient D*. A model-based reconstruction has the potential to overcome these challenges. As a first step, our goal was to develop a model-based reconstruction framework for IVIM and combined IVIM-DTI parameter estimation. The IVIM and IVIM-DTI models were implemented in the PyQMRI model-based reconstruction framework and validated with simulations and in vivo data. Commonly used voxel-wise nonlinear least-squares fitting was used as the reference. Simulations with the IVIM and IVIM-DTI models were performed with 100 noise realizations to assess accuracy and precision. Diffusion-weighted data were acquired for IVIM reconstruction in the liver (n = 5), as well as for IVIM-DTI in the kidneys (n = 5) and lower-leg muscles (n = 6) of healthy volunteers. The median and interquartile range (IQR) values of the IVIM and IVIM-DTI parameters were compared to assess bias and precision. With model-based reconstruction, the parameter maps exhibited less noise, which was most pronounced in the f and D* maps, both in the simulations and in vivo. The bias values in the simulations were comparable between model-based reconstruction and the reference method. The IQR was lower with model-based reconstruction compared with the reference for all parameters. In conclusion, model-based reconstruction is feasible for IVIM and IVIM-DTI and improves the precision of the parameter estimates, particularly for f and D* maps.

Deep learning intravoxel incoherent motion modeling: Exploring the impact of training features and learning strategies

PURPOSE

The development of advanced estimators for intravoxel incoherent motion (IVIM) modeling is often motivated by a desire to produce smoother parameter maps than least squares (LSQ). Deep neural networks show promise to this end, yet performance may be conditional on a myriad of choices regarding the learning strategy. In this work, we have explored potential impacts of key training features in unsupervised and supervised learning for IVIM model fitting.

METHODS

Two synthetic data sets and one in-vivo data set from glioma patients were used in training of unsupervised and supervised networks for assessing generalizability. Network stability for different learning rates and network sizes was assessed in terms of loss convergence. Accuracy, precision, and bias were assessed by comparing estimations against ground truth after using different training data (synthetic and in vivo).

RESULTS

A high learning rate, small network size, and early stopping resulted in sub-optimal solutions and correlations in fitted IVIM parameters. Extending training beyond early stopping resolved these correlations and reduced parameter error. However, extensive training resulted in increased noise sensitivity, where unsupervised estimates displayed variability similar to LSQ. In contrast, supervised estimates demonstrated improved precision but were strongly biased toward the mean of the training distribution, resulting in relatively smooth, yet possibly deceptive parameter maps. Extensive training also reduced the impact of individual hyperparameters.

CONCLUSION

Voxel-wise deep learning for IVIM fitting demands sufficiently extensive training to minimize parameter correlation and bias for unsupervised learning, or demands a close correspondence between the training and test sets for supervised learning.

Intravoxel incoherent motion diffusion-weighted MRI of renal parenchyma and its clinical significance in patients with untreated acute leukemia: a pilot study

PURPOSE

To evaluate quantitative parameters derived from intravoxel incoherent motion diffusion-weighted MRI (IVIM) of renal parenchyma in patients with untreated acute leukemia (AL) and analyze its prognostic significance and probable pathological mechanism.

METHODS

From March 2019 to November 2021, 67 newly diagnosed AL patients and 67 healthy controls matched in age and sex were recruited. All participants underwent IVIM in the kidneys, and D, D*, f, standard ADC values were measured. The differences of all parameters between AL and controls were analyzed. The relationship between imaging parameters and estimated glomerular filtration rate (eGFR) was studied. Univariable and multivariable analyses were performed to investigate prognostic significance of possible indicators.

RESULTS

The f and D value of renal medulla and D value of renal cortex in AL patients were lower than those in the healthy control group (t = - 2.173, t = - 3.463, t = - 2.030, respectively, all P < 0.05). The cortical f, cortical standard ADC, medullary f, and medullary standard ADC were correlated with the eGFR (r = 0.524, r = 0.401, r = 0.415, r = 0.325, respectively, all P < 0.05) in patients with AL. A medullary f value ≤ 9.51% (hazard ratio: 0.282; 95% confidence interval: 0.110, 0.719; P = 0.008) was associated with overall survival in a multivariable analysis.

CONCLUSION

The f and standard ADC values in renal parenchyma were the probable imaging markers of renal function in patients with newly diagnosed de novo AL. Lower renal medullary f value was a potential independent predictor for overall survival.

Diffusion tensor imaging of anisotropic inhomogeneous turbulent flow

Inhomogeneous anisotropic turbulent flow is difficult to measure, and yet it commonly occurs in nature and in many engineering applications. This work aims to introduce a technique based on magnetic resonance imaging which can spatially map the degree of turbulence as well as the degree of anisotropy. Our interpretation relies on the eddy diffusion model of turbulence, and combines this with the technique of diffusion tensor imaging. The result is an eddy diffusion tensor, which is represented by a symmetric three-by-three matrix. This tensor contains a wealth of information about the magnitude and directions of the turbulent fluctuations; however, the correlation time must be considered before interpreting this information. In the constricted pipe flow used in this study, the turbulence is greatest in magnitude in the space surrounding the core of the turbulent jet, and the turbulence is highly anisotropic.

Simultaneous Quantification of Anisotropic Microcirculation and Microstructure in Peripheral Nerve

Peripheral nerve injury is a significant public health challenge, and perfusion in the nerve is a potential biomarker for assessing the injury severity and prognostic outlook. Here, we applied a novel formalism that combined intravoxel incoherent motion (IVIM) and diffusion tensor imaging (DTI) to simultaneously characterize anisotropic microcirculation and microstructure in the rat sciatic nerve. Comparison to postmortem measurements revealed that the in vivo IVIM-DTI signal contained a fast compartment (2.32 ± 0.04 × 10−3 mm2/s mean diffusivity, mean ± sem, n = 6, paired t test p < 0.01) that could be attributed to microcirculation in addition to a slower compartment that had similar mean diffusivity as the postmortem nerve (1.04 ± 0.01 vs. 0.96 ± 0.05 × 10−3 mm2/s, p > 0.05). Although further investigation and technical improvement are warranted, this preliminary study demonstrates both the feasibility and potential for applying the IVIM-DTI methodology to peripheral nerves for quantifying perfusion in the presence of anisotropic tissue microstructure.

Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches

BACKGROUND

 Until today, assessment of renal function has remained a challenge for modern medicine. In many cases, kidney diseases accompanied by a decrease in renal function remain undetected and unsolved, since neither laboratory tests nor imaging diagnostics provide adequate information on kidney status. In recent years, developments in the field of functional magnetic resonance imaging with application to abdominal organs have opened new possibilities combining anatomic imaging with multiparametric functional information. The multiparametric approach enables the measurement of perfusion, diffusion, oxygenation, and tissue characterization in one examination, thus providing more comprehensive insight into pathophysiological processes of diseases as well as effects of therapeutic interventions. However, application of multiparametric fMRI in the kidneys is still restricted mainly to research areas and transfer to the clinical routine is still outstanding. One of the major challenges is the lack of a standardized protocol for acquisition and postprocessing including efficient strategies for data analysis. This article provides an overview of the most common fMRI techniques with application to the kidney together with new approaches regarding data analysis with deep learning.

METHODS

 This article implies a selective literature review using the literature database PubMed in May 2021 supplemented by our own experiences in this field.

RESULTS AND CONCLUSION

 Functional multiparametric MRI is a promising technique for assessing renal function in a more comprehensive approach by combining multiple parameters such as perfusion, diffusion, and BOLD imaging. New approaches with the application of deep learning techniques could substantially contribute to overcoming the challenge of handling the quantity of data and developing more efficient data postprocessing and analysis protocols. Thus, it can be hoped that multiparametric fMRI protocols can be sufficiently optimized to be used for routine renal examination and to assist clinicians in the diagnostics, monitoring, and treatment of kidney diseases in the future.

KEY POINTS

  · Multiparametric fMRI is a technique performed without the use of radiation, contrast media, and invasive methods.. · Multiparametric fMRI provides more comprehensive insight into pathophysiological processes of kidney diseases by combining functional and structural parameters.. · For broader acceptance of fMRI biomarkers, there is a need for standardization of acquisition, postprocessing, and analysis protocols as well as more prospective studies.. · Deep learning techniques could significantly contribute to an optimization of data acquisition and the postprocessing and interpretation of larger quantities of data..

CITATION FORMAT

· Zhang C, Schwartz M, Küstner T et al. Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1775-8633.

Non-invasive investigation of early kidney damage in streptozotocin-induced diabetic rats by intravoxel incoherent motion diffusion-weighted (IVIM) MRI

Background

The current study investigated the performance of intravoxel incoherent motion diffusion (IVIM) technology in monitoring early renal injury in streptozotocin rats.

Methods

Forty-eight Sprague-Dawley (SD) rats were divided into a control group and a diabetic mellitus (DM) group. Six rats in each group were randomly selected for MR scans at four different time points (0, 4, 8, and 12 weeks). The IVIM-derived parameters (D, D*, f and ADC values) of the renal cortex (CO), outer and inner stripe of the outer medulla (OS, IS), and internal medulla (IM) were acquired. Changes in each IVIM-derived parameter over time were analyzed, and differences between the two groups at each point were assessed. The associations between the IVIM parameters and IV collagen expression, urine volume (UV), blood urea nitrogen (BUN), and serum creatinine (Scr) were investigated.

Results

The D and D* values of CO and the ADC values of CO, OS, IS and IM displayed significantly different trends between the two groups over time (P<0.05). In addition, significant correlations were discovered between the D* value of CO and UV and BUN (r=0.527, P=0.033; r=0.617, P=0.005), between the ADC value of IM and BUN (r=0.557, P=0.019) and between the f value of IM and BUN (r=0.527, P=0.033). No correlation was found between IVIM parameters and IV collagen expression and Scr.

Conclusions

IVIM is a potential sensitive and noninvasive technology for the simultaneous assessment of early renal cortical and medullary injuries induced by diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02530-8.

Intravoxel Incoherent Motion Magnetic Resonance Imaging in Skeletal Muscle: Review and Future Directions

Throughout the body, muscle structure and function can be interrogated using a variety of noninvasive magnetic resonance imaging (MRI) methods. Recently, intravoxel incoherent motion (IVIM) MRI has gained momentum as a method to evaluate components of blood flow and tissue diffusion simultaneously. Much of the prior research has focused on highly vascularized organs, including the brain, kidney, and liver. Unique aspects of skeletal muscle, including the relatively low perfusion at rest and large dynamic range of perfusion between resting and maximal hyperemic states, may influence the acquisition, postprocessing, and interpretation of IVIM data. Here, we introduce several of those unique features of skeletal muscle; review existing studies of IVIM in skeletal muscle at rest, in response to exercise, and in disease states; and consider possible confounds that should be addressed for muscle-specific evaluations. Most studies used segmented nonlinear least squares fitting with a b-value threshold of 200 sec/mm² to obtain IVIM parameters of perfusion fraction (f), pseudo-diffusion coefficient (D*), and diffusion coefficient (D). In healthy individuals, across all muscles, the average ± standard deviation of D was 1.46 ± 0.30 × 10^-3  mm² /sec, D* was 29.7 ± 38.1 × 10^-3  mm² /sec, and f was 11.1 ± 6.7%. Comparisons of reported IVIM parameters in muscles of the back, thigh, and leg of healthy individuals showed no significant difference between anatomic locations. Throughout the body, exercise elicited a positive change of all IVIM parameters. Future directions including advanced postprocessing models and potential sequence modifications are discussed. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

On the use of multicompartment models of diffusion and relaxation for placental imaging

Multi-compartment models of diffusion and relaxation are ubiquitous in magnetic resonance research especially applied to neuroimaging applications. These models are increasingly making their way into the world of placental imaging. This review provides a framework for their motivation and implementation and describes some of the outstanding questions that need to be answered before they can be routinely adopted.

A simulation study investigating potential diffusion-based MRI signatures of microstrokes

Recent studies suggested that cerebrovascular micro-occlusions, i.e. microstokes, could lead to ischemic tissue infarctions and cognitive deficits. Due to their small size, identifying measurable biomarkers of these microvascular lesions remains a major challenge. This work aims to simulate potential MRI signatures combining arterial spin labeling (ASL) and multi-directional diffusion-weighted imaging (DWI). Driving our hypothesis are recent observations demonstrating a radial reorientation of microvasculature around the micro-infarction locus during recovery in mice. Synthetic capillary beds, randomly- and radially-oriented, and optical coherence tomography (OCT) angiograms, acquired in the barrel cortex of mice (n = 5) before and after inducing targeted photothrombosis, were analyzed. Computational vascular graphs combined with a 3D Monte-Carlo simulator were used to characterize the magnetic resonance (MR) response, encompassing the effects of magnetic field perturbations caused by deoxyhemoglobin, and the advection and diffusion of the nuclear spins. We quantified the minimal intravoxel signal loss ratio when applying multiple gradient directions, at varying sequence parameters with and without ASL. With ASL, our results demonstrate a significant difference (p < 0.05) between the signal-ratios computed at baseline and 3 weeks after photothrombosis. The statistical power further increased (p < 0.005) using angiograms measured at week 4. Without ASL, no reliable signal change was found. We found that higher ratios, and accordingly improved significance, were achieved at lower magnetic field strengths (e.g., B0 = 3T) and shorter echo time TE (< 16 ms). Our simulations suggest that microstrokes might be characterized through ASL-DWI sequence, providing necessary insights for posterior experimental validations, and ultimately, future translational trials.

Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain: Analysis of cancer and acute stroke

The intravoxel incoherent motion (IVIM) model allows to map diffusion (D) and perfusion-related parameters (F and D*). Parameter estimation is, however, error-prone due to the non-linearity of the signal model, the limited signal-to-noise ratio (SNR) and the small volume fraction of perfusion in the in-vivo brain. In the present work, the performance of Bayesian inference was examined in the presence of brain pathologies characterized by hypo- and hyperperfusion. In particular, a hierarchical and a spatial prior were combined. Performance was compared relative to conventional segmented least squares regression, hierarchical prior only (non-segmented and segmented data likelihoods) and a deep learning approach. Realistic numerical brain IVIM simulations were conducted to assess errors relative to ground truth. In-vivo, data of 11 central nervous system cancer patients and 9 patients with acute stroke were acquired. The proposed method yielded reduced error in simulations for both the cancer and acute stroke scenarios compared to other methods across the whole investigated SNR range. The contrast-to-noise ratio of the proposed method was better or on par compared to the other techniques in-vivo. The proposed Bayesian approach hence improves IVIM parameter estimation in brain cancer and acute stroke.

Advanced diffusion imaging of abdominal organs in different hydration states of the human body: stability of biomarkers

BACKGROUND

MR diffusion weighted imaging (DWI) may provide important information regarding the pathophysiology of parenchymal abdominal organs. The purpose of our study was to investigate the stability of imaging biomarkers of diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in abdominal parenchymal organs regarding two body hydration states.

METHODS

Ten healthy volunteers twice underwent DWI of abdominal organs using a double-refocused spin-echo echo-planar imaging sequences with 11 different b-values (ranging from 0 to 1,500 s/mm2): after 4 h of fluid deprivation; 45 min following 1000 ml of water intake. Four different diffusion models were evaluated and compared: standard DWI, DKI with mono-exponential fitting, multistep algorithm with variable b-value threshold for IVIM, combined IVIM-Kurtosis; in four abdominal organs: kidneys, liver, spleen and psoas muscle.

RESULTS

Diffusion parameters from all four models remained similar for the renal parenchyma before and after the water challenge. Significant differences were found for the liver, spleen, and psoas muscle. The largest effects were seen for: the liver parenchyma after the water challenge by means of IVIM model's true diffusion (p < 0.02); the spleen, for IVIM's perfusion fraction (p < 0.03), the psoas muscle for the ADC value (p < 0.02).

CONCLUSIONS

Herein, we showed that diffusion parameters of the kidney remain remarkably stable regarding the hydration status. This may be attributed to the kidney-specific compensatory mechanisms. For the liver, spleen and psoas muscle the diffusion parameters were sensitive to changes of the hydration. This phenomenon needs to be considered when evaluating diffusion data of these organs.

Diffusion tensor imaging in renal artery stenosis: a preliminary report

Objective:

To investigate the diffusion properties in the kidneys affected by renal artery stenosis (RAS) using diffusion tensor imaging (DTI).

Methods:

In this prospective study, 35 patients with RAS and 15 patients without renal abnormalities were enrolled and examined using DTI. Cortical and medullary regions of interest (ROIs) were located to obtain the corresponding values of the apparent diffusion coefficient (ADC) and fractional anisotropy (FA). The cortical and medullary ADC and FA were compared in the kidney affected by variable degrees of stenosis (RAS 50–75% and >75%) vs controls, using the one-way ANOVA and Student’s t-test. The Spearman correlation test was used to correlate the mean ADC and FA values in the cortex and medulla with the estimate glomerular filtration rate (eGFR).

Results:

For the controls, the ADC value was significantly (p = 0.03) higher in the cortex than in the medulla; the FA value was significantly (p = 0.001) higher in the medulla than in the cortex. Compared with the controls, a significant reduction in the cortical ADC was present with a RAS of 50–75% and >75% (p = 0.001 and 0.041, respectively); a significant reduction in the medullary FA was verified only for RAS >75% (p = 0.023). The Spearman correlation test did not show a statistically significant correlation between the cortical and medullary ADC and FA, and the eGFR.

Conclusion:

The alterations of the diffusional parameters caused by RAS can be detected by DTI and could be useful in the diagnostic evaluation of these patients.

Advances in knowledge:

1. Magnetic resonance DTI could provide useful information about renal involvement in RAS. 2. Magnetic resonance DTI allows non-invasive repeatable evaluation of the renal parenchyma, without contrast media.

Differentiating atypical hemangiomas and vertebral metastases: a field-of-view (FOV) and FOCUS intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) study

PURPOSE

Some atypical vertebral hemangiomas (VHs) may mimic metastases on routine MRI and can result in misdiagnosis and ultimately to additional imaging, biopsy and unnecessary costs. The purpose of this study is to assess the utility of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) on account of field-of-view optimized and constrained undistorted single shot (FOCUS) in distinguishing atypical VHs and vertebral metastases.

METHODS

A total of 25 patients with vertebral metastases and 25 patients with atypical VHs were confirmed by clinical follow-up or pathology. IVIM-DWI imaging was performed at different b values (0, 30, 50, 100, 150, 200, 400, 600, 800, 1000 mm²/s). IVIM parameters [the true diffusion coefficient (D), pseudodiffusion coefficient (D*), standard apparent diffusion coefficient (ADC), and perfusion fraction (f)] were calculated and compared between two groups by using Student's t test. A receiver operating characteristic analysis was performed.

RESULTS

Quantitative analysis of standard ADC and D parameters showed significantly lower values in vertebral metastases when compared to atypical hemangiomas [ADC value: (0.70 ± 0.12) × 10^-3 mm²/s vs (1.14 ± 0.28) × 10^-3 mm²/s; D value: (0.47 ± 0.07) × 10^-3 mm²/s vs (0.76 ± 0.14) × 10^-3 mm²/s, all P < 0.01]. The sensitivity and specificity of D value were 93.8% and 92.3%, respectively.

CONCLUSION

The standard ADC value and D value may be used as an indicator to distinguish vertebral metastases from atypical VHs. FOCUS IVIM-derived parameters provide potential value in the quantitatively differentiating vertebral metastases from vertebral atypical hemangiomas.

Pattern recognition analysis of directional intravoxel incoherent motion MRI in ischemic rodent brains

This study aimed to demonstrate a reliable automatic segmentation method for independently separating reduced diffusion and decreased perfusion areas in ischemic stroke brains using constrained nonnegative matrix factorization (cNMF) pattern recognition in directional intravoxel incoherent motion MRI (IVIM-MRI). First, the feasibility of cNMF-based segmentation of IVIM signals was investigated in both simulations and in vivo experiments. The cNMF analysis was independently performed for S₀ -normalized and scaled (by the difference between the maximum and minimum) IVIM signals, respectively. Segmentations of reduced diffusion (from S₀ -normalized IVIM signals) and decreased perfusion (from scaled IVIM signals) areas were performed using the corresponding cNMF pattern weight maps. Second, Monte Carlo simulations were performed for directional IVIM signals to investigate the relationship between the degree of vessel alignment and the direction of the diffusion gradient. Third, directional IVIM-MRI experiments (x, y and z diffusion-gradient directions, 20 b values at 7 T) were performed for normal (n = 4), sacrificed (n = 1, no flow) and ischemic stroke models (n = 4, locally reduced flow). The results showed that automatic segmentation of the hypoperfused lesion using cNMF analysis was more accurate than segmentation using the conventional double-exponential fitting. Consistent with the simulation, the double-exponential pattern of the IVIM signals was particularly strong in white matter and ventricle regions when the directional flows were aligned with the applied diffusion-gradient directions. cNMF analysis of directional IVIM signals allowed robust automated segmentation of white matter, ventricle, vascular and hypoperfused regions in the ischemic brain. In conclusion, directional IVIM signals were simultaneously sensitive to diffusion and aligned flow and were particularly useful for automatically segmenting ischemic lesions via cNMF-based pattern recognition.

On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data

Radiotherapy (RT) is commonly applied for the treatment of glioblastoma multiforme (GBM). Following the planning target volume (PTV) definition procedure standardized in guidelines, a 20% risk of missing non-local recurrences is present. Purpose of this study was to evaluate whether diffusion tensor imaging (DTI)-based fiber tracking may be beneficial for PTV definition taking into account the prediction of distant recurrences. 56 GBM patients were examined with magnetic resonance imaging (MRI) including DTI performed before RT after resection of the primary tumor. Follow-up MRIs were acquired in three month intervals. For the seven patients with a distant recurrence, fiber tracking was performed with three algorithms and it was evaluated whether connections existed from the primary tumor region to the distant recurrence. It depended strongly on the used tracking algorithm and the used tracking parameters whether a connection was observed. Most of the connections were weak and thus not usable for PTV definition. Only in one of the seven patients with a recurring tumor, a clear connection was present. It seems unlikely that DTI-based fiber tracking can be beneficial for predicting distant recurrences in the planning of PTVs for glioblastoma multiforme.

Diffusion-weighted Renal MRI at 9.4 Tesla Using RARE to Improve Anatomical Integrity

Diffusion-weighted magnetic resonance imaging (DWI) is a non-invasive imaging technique sensitive to tissue water movement. By enabling a discrimination between tissue properties without the need of contrast agent administration, DWI is invaluable for probing tissue microstructure in kidney diseases. DWI studies commonly make use of single-shot Echo-Planar Imaging (ss-EPI) techniques that are prone to suffering from geometric distortion. The goal of the present study was to develop a robust DWI technique tailored for preclinical magnetic resonance imaging (MRI) studies that is free of distortion and sensitive to detect microstructural changes. Since fast spin-echo imaging techniques are less susceptible to B₀ inhomogeneity related image distortions, we introduced a diffusion sensitization to a split-echo Rapid Acquisition with Relaxation Enhancement (RARE) technique for high field preclinical DWI at 9.4 T. Validation studies in standard liquids provided diffusion coefficients consistent with reported values from the literature. Split-echo RARE outperformed conventional ss-EPI, with ss-EPI showing a 3.5-times larger border displacement (2.60 vs. 0.75) and a 60% higher intra-subject variability (cortex = 74%, outer medulla = 62% and inner medulla = 44%). The anatomical integrity provided by the split-echo RARE DWI technique is an essential component of parametric imaging on the way towards robust renal tissue characterization, especially during kidney disease.

Diffusion tensor imaging of the abdominal organs: Influence of oriented intravoxel flow compartments

Water flow in partially oriented intravoxel compartments mimics an anisotropic fast-diffusion regime, which contributes to the signal attenuation in diffusion-weighted images. In the abdominal organs, this flow may reflect physiological fluid movements (eg, tubular urine flow in kidneys, or bile flow through the liver) and have a clinical relevance. This study investigated the influence of anisotropic intravoxel water flow on diffusion tensor imaging (DTI) of the abdominal organs. Diffusion-weighted images were acquired in five healthy volunteers using an EPI sequence with diffusion preparation (TR/TE: 1000 ms/71 ms; b-values: 0, 10, 20, 40, 70, 120, 250, 450, 700, 1000 s/mm² ; 12 noncollinear diffusion-encoding directions). DTI of liver and kidneys was performed assuming (i) monoexponential decay of the diffusion-weighted signal, and (ii) accounting for potential anisotropy of the fast-diffusion compartments using a tensorial generalization of the IVIM model. Additionally, potential dependency of the metrics of the tensors from the anatomical location was evaluated. Significant differences in the metrics of the diffusion tensor (DT) were found in both liver and kidneys when comparing the two models. In both organs, the trace and the fractional anisotropy of the DT were significantly higher in the monoexponential model than when accounting for perfusion. The comparison of areas of the liver proximal to the hilum with distal regions and of renal cortex with the medulla also proved a location dependency of the size of the fast-diffusion compartments. Pseudo-diffusion correction in DTI enables the assessment of the solid parenchyma regardless of the organ perfusion or other pseudo-diffusive fluid movements. This may have a clinical relevance in the assessment of parenchymal pathologies (eg, liver fibrosis). The fast pseudo-diffusion components present a detectable anisotropy, which may reflect the hepatic microcirculation or other sources of mesoscopic fluid movement in the abdominal organs.

Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI

Objectives

Standardization is an important milestone in the validation of DWI-based parameters as imaging biomarkers for renal disease. Here, we propose technical recommendations on three variants of renal DWI, monoexponential DWI, IVIM and DTI, as well as associated MRI biomarkers (ADC, D, D*, f, FA and MD) to aid ongoing international efforts on methodological harmonization.

Materials and methods

Reported DWI biomarkers from 194 prior renal DWI studies were extracted and Pearson correlations between diffusion biomarkers and protocol parameters were computed. Based on the literature review, surveys were designed for the consensus building. Survey data were collected via Delphi consensus process on renal DWI preparation, acquisition, analysis, and reporting. Consensus was defined as ≥ 75% agreement.

Results

Correlations were observed between reported diffusion biomarkers and protocol parameters. Out of 87 survey questions, 57 achieved consensus resolution, while many of the remaining questions were resolved by preference (65–74% agreement). Summary of the literature and survey data as well as recommendations for the preparation, acquisition, processing and reporting of renal DWI were provided.

Discussion

The consensus-based technical recommendations for renal DWI aim to facilitate inter-site harmonization and increase clinical impact of the technique on a larger scale by setting a framework for acquisition protocols for future renal DWI studies. We anticipate an iterative process with continuous updating of the recommendations according to progress in the field.

Electronic supplementary material

The online version of this article (10.1007/s10334-019-00790-y) contains supplementary material, which is available to authorized users.

Acute renal impairment characterization using diffusion magnetic resonance imaging: Validation by histology

Diffusion magnetic resonance imaging has been demonstrated to be a simple, noninvasive and accurate method for the detection of renal microstructure and microcirculation, which are closely linked to renal function. Moreover, serum endothelin-1 (ET-1) was also reported as a good indicator of early renal injury. The aim of this study was to evaluate the feasibility and capability of diffusion MRI and ET-1 to detect acute kidney injury by an operation simulating high-pressure renal pelvic perfusion, which is commonly used during ureteroscopic lithotripsy. Histological findings were used as a reference. Fourteen New Zealand rabbits in an experimental group and 14 in a control group were used in this study. Diffusion tensor imaging and intravoxel incoherent motion diffusion-weighted imaging were acquired by a 3.0 T MRI scanner. Significant corticomedullary differences were found in the values of the apparent diffusion coefficient (ADC), pure tissue diffusion, volume fraction of pseudo-diffusion (fp) and fractional anisotropy (FA) (P < 0.05 for all) in both preoperation and postoperation experimental groups. Compared with the control group, the values of cortical fp_mean , medullary ADC_mean and FA_mean decreased significantly (P < 0.05) after the operation in the experimental group. Also, the change rate of medullary ADC_mean in the experimental group was more pronounced than that in the control group (P = 0.018). No significant change was found in serum ET-1 concentration after surgery in either the experimental (P = 0.80) or control (P = 0.17) groups. In the experimental group, histological changes were observed in the medulla, while no visible change was found in the cortex. This study demonstrated the feasibility of diffusion MRI to detect the changes of renal microstructure and microcirculation in acute kidney injury, with the potential to evaluate renal function. Moreover, the sensitivity of diffusion MRI to acute kidney injury appears to be superior to that of serum ET-1.

Detecting impaired function of renal allografts at the early stage after transplantation using intravoxel incoherent motion imaging

Background

Detecting renal allografts with impaired function early after renal transplantation and timely intervention are important to ensure a successful outcome.

Purpose

To detect impaired function of renal allografts at the early stage after renal transplantation using intravoxel incoherent motion imaging (IVIM).

Material and Methods

Forty-six recipients with good allograft function and 32 recipients with impaired function were included in this study. All participants were scanned with IVIM using 11 b-values on a 3-T magnetic resonance (MR) scanner; the apparent diffusion coefficient (ADC), ADC of slow diffusion (ADCslow), pseudo-diffusion (ADCfast), and perfusion fraction (f) values were calculated using a full bi-exponential model. Correlations between estimated glomerular filtration rate (eGFR) and the IVIM parameters were assessed by using Spearman correlation analysis. Receiver operating characteristics were used to assess the diagnostic utilities for detecting allografts with impaired function.

Results

The ADC, ADCslow, ADCfast, and f values of the renal cortex and the ADC and ADCslow values of the renal medulla were significantly higher in allografts with good function compared to those with impaired function (all P < 0.05). There was a significant corticomedullary difference in ADCslow, ADC, and f in all allografts. ADCfast values were higher in the cortex than in the medulla for allografts with good function but no differences were seen in allografts with impaired function ( P > 0.05). Combined use of all cortical IVIM parameters has higher efficacy in detecting renal allograft dysfunction than any single parameter (sensitivity = 90.62%; specificity = 78.26%).

Conclusion

IVIM technique may be useful for detecting renal allograft dysfunction, especially combined use of cortical parameters.

The Diffusion Tensor Imaging Properties of the Normal Testicles at 3 Tesla Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

The testicles are structured in a well-defined microtubular network formation, which is expected to be reflected in high anisotropic diffusivity. However, preliminary studies reported on low values of fractional-anisotropy (FA) in the normal testicles. Our aim was to design and apply a diffusion-tensor imaging (DTI) protocol in order to elucidate the diffusivity properties of the testicles and their determining factors.

MATERIALS AND METHODS

16 healthy volunteers were prospectively scanned at 3T. The protocol included T₂-weighted and DTI sequences, the latter using 24 directional diffusion gradients and 3 b-values (0, 100, and 700 s/mm²) that were separated for analysis based on the reference b-value of 0 or 100 s/mm². Image processing of the two DTI datasets yielded the diffusion vector maps and parametric maps of their corresponding principal diffusion coefficients λ₁, λ₂, λ₃, mean diffusivity and FA.

RESULTS

The results demonstrated the feasibility of DTI to provide parametric maps of the testicles. The diffusion tensor parameters obtained using the pair of 0 and 700 s/mm² b-values, exhibited relatively low diffusivity, with mean λ₁ values of 1.36 ± 0.21 × 10^-3 mm²/s and low anisotropy, with mean FA values of 0.13 ± 0.05. Analysis of DTI using the 100 and 700 s/mm² b-values yielded a slight decrease in the diffusivity of 4%-5%, whereas FA remained similar.

CONCLUSION

The diffusivity of the normal testicles is relatively slow, closed-to isotropic and hardly affected by the low b-values regime exclusion. Thus, DTI parameters of the normal testicles are neither dictated by the underlying architectural anisotropy nor microperfusion effects.

Noninvasive assessment of renal fibrosis by magnetic resonance imaging and ultrasound techniques

Renal fibrosis is a useful biomarker for diagnosis and guidance of therapeutic interventions of chronic kidney disease (CKD), a worldwide disease that affects more than 10% of the population and is one of the major causes of death. Currently, tissue biopsy is the gold standard for assessment of renal fibrosis. However, it is invasive, and prone to sampling error and observer variability, and may also result in complications. Recent advances in diagnostic imaging techniques, including magnetic resonance imaging (MRI) and ultrasonography, have shown promise for noninvasive assessment of renal fibrosis. These imaging techniques measure renal fibrosis by evaluating its impacts on the functional, mechanical, and molecular properties of the kidney, such as water mobility by diffusion MRI, tissue hypoxia by blood oxygenation level dependent MRI, renal stiffness by MR and ultrasound elastography, and macromolecule content by magnetization transfer imaging. Other MR techniques, such as T1/T2 mapping and susceptibility-weighted imaging have also been explored for measuring renal fibrosis. Promising findings have been reported in both preclinical and clinical studies using these techniques. Nevertheless, limited specificity, sensitivity, and practicality in these techniques may hinder their immediate application in clinical routine. In this review, we will introduce methodologies of these techniques, outline their applications in fibrosis imaging, and discuss their limitations and pitfalls.

Value of intravoxel incoherent motion for differential diagnosis of renal tumors

BACKGROUND

Few studies have reported on the use of intravoxel incoherent motion (IVIM) for renal tumors.

PURPOSE

To investigate the value of IVIM for distinguishing renal tumors.

MATERIAL AND METHODS

Thirty-one patients with clear cell renal cell carcinomas (CCRCCs), 13 patients with renal angiomyolipomas with minimal fat (RAMFs), eight patients with chromophobe renal cell carcinomas (ChRCCs), and ten patients with papillary renal cell carcinomas (PRCCs) were examined. The tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f) were calculated.

RESULTS

The D and f values were highest for CCRCCs, lowest for PRCCs, and intermediate for ChRCCs and RAMFs ( P < 0.05). The D values of CCRCCs differed significantly from those of ChRCCs and PRCCs ( P < 0.05). The D* values were highest for RAMFs, lowest for ChRCCs, and intermediate for CCRCCs and PRCCs ( P < 0.05). Statistically significant differences were observed between the D* values of CCRCCs and RAMFs ( P < 0.05). The D* values of the CCRCCs differed significantly from the D* values of the ChRCCs ( P < 0.05). Using the D and f values of 1.10 and 0.41, respectively, as the threshold values for differentiating CCRCCs from RAMFs, ChRCCs, and PRCCs, the best results had sensitivities of 81.0% and 66.8% and specificities of 85.7% and 81.0%, respectively. Using the D* value of 0.038 as the threshold value for differentiating RAMFs from CCRCCs, ChRCCs, and PRCCs, the best result obtained had a sensitivity of 90.5% and specificity of 76.2%.

CONCLUSION

IVIM may provide information for differentiating renal tumor types.

Generalized Mean Apparent Propagator MRI to Measure and Image Advective and Dispersive Flows in Medicine and Biology

Water transport in biological systems spans different regimes with distinct physical behaviors: diffusion, advection, and dispersion. Identifying these regimes is of paramount importance in many in vivo applications, among them, measuring microcirculation of blood in capillary networks and cerebrospinal fluid transport in the glymphatic system. Diffusion magnetic resonance imaging (dMRI) can be used to encode water displacements, and a Fourier transform of the acquired signal furnishes a displacement probability density function known as the propagator. This transformation normally requires the use of a fast Fourier transform (FFT), which presents major feasibility challenges when scanning in vivo, mainly because of dense signal sampling, resulting in long acquisition times. A second approach to reconstruct the propagator is by using analytical representation of the signal, overcoming many of the FFT's limitations. In all analytical implementations of dMRI to date, the translational motion of water has been assumed to be exclusively diffusive, which is the case only in the absence of flow. However, retaining the phase information from the diffusion signal provides the ability to measure both mean coherent velocity and random diffusion from a single experiment. We implement and extend an analytical framework, mean apparent propagator (MAP), which can account for non-zero flow conditions. We call this method generalized MAP or GMAP. We describe a numerical optimization scheme and implement it on data from an MRI flow phantom constructed from a pack of 10- [Formula: see text] beads. The advantages of GMAP over the FFT-based method in the context of sampling density and low-flow detection were demonstrated, and analytically derived propagator moments were shown to agree with theoretical values even after data subsampling. GMAP would enable the detection of microflow in vivo that could help elucidate many important biological processes.

Enhancing intravoxel incoherent motion parameter mapping in the brain using k-b PCA

Intravoxel incoherent motion (IVIM) imaging of diffusion and perfusion parameters in the brain using parallel imaging suffers from local noise amplification. To address the issue, signal correlations in space and along the diffusion encoding dimension are exploited jointly using a constrained image reconstruction approach. IVIM imaging was performed on a clinical 3 T MR system with diffusion weighting along six gradient directions and 16 b-values encoded per direction across a range of 0-900 s/mm² . Data were collected in 11 subjects, retrospectively undersampled in k-space with net factors ranging from 2 to 6 and reconstructed using CG-SENSE and the proposed k-b PCA approach. Results of k-b PCA and CG-SENSE from retrospectively undersampled data were compared with those from the fully sampled reference. In addition, prospective single-shot k-b undersampling was implemented and data were acquired in five additional volunteers. IVIM parameter maps were derived using a segmented least-squares method. The proposed k-b PCA method outperformed CG-SENSE in terms of reconstruction errors for effective undersampling factors of 3 and beyond. Undersampling artifacts were effectively removed with k-b PCA up to sixfold undersampling. At net sixfold undersampling, relative errors (compared with the fully sampled reference) of image magnitude and IVIM parameters (D, f and D^* ) were (median ± interquartile range): 3.5 ± 3.7 versus 25.3 ± 25.8%, 2.7 ± 3.6 versus 14.2 ± 20.4%, 15.1 ± 26.1 versus 96.6 ± 67.4% and 14.8 ± 26.6 versus 100 ± 195.1% for k-b PCA versus CG-SENSE, respectively. Acquisition with sixfold prospective undersampling yielded average IVIM parameters in the brain of 0.79 ± 0.18 × 10^-3  mm² /s for D, 7.35 ± 7.27% for f and 7.11 ± 2.39 × 10^-3  mm² /s for D^* . Constrained reconstruction using k-b PCA improves IVIM parameter mapping from undersampled data when compared with CG-SENSE reconstruction. Prospectively undersampled single-shot echo planar imaging acquisition was successfully employed using k-b PCA, demonstrating a reduction of image artifacts and noise relative to parallel imaging.

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

Using magnetic resonance diffusion tensor imaging to evaluate renal function changes in diabetic patients with early-stage chronic kidney disease

AIM

To investigate the clinical value of diffusion tensor imaging (DTI) in assessing renal function changes in diabetic patients with early-stage chronic kidney disease (CKD), and the relationship of DTI parameters with estimated glomerular filtration rate (eGFR) and urinary biomarkers.

MATERIALS AND METHODS

Thirty-six patients with diabetes mellitus (DM; 30 CKD stage 1 and 6 CKD stage 2) and 26 healthy control subjects were enrolled. DTI was performed using a clinical 3 T MRI system. Apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values were calculated from the renal cortex and medulla. The correlation of the DTI parameters with eGFR and urinary biomarkers was evaluated.

RESULTS

FA values were significantly reduced in the renal cortex and medulla of DM group compared with the control group (cortical FA, Z=-2.834, p=0.005; medullary FA, t=2.768, p=0.007). In the DM group, FA values in the renal cortex and medulla were positively correlated with eGFR, while FA values in the medulla were negatively correlated with the urinary albumin/creatinine ratio, urinary alpha-1 microglobulin/creatinine ratio, and urinary transferring/creatinine ratio. ADC values in the renal cortex and medulla showed a trend towards an increase in the DM group compared with the control group.

CONCLUSIONS

Renal DTI is a promising method for assessing early renal function changes in DM patients.

Diffusion-MR in kidney transplant recipients: is diuretic stimulation a useful diagnostic tool for improving differentiation between functioning and non-functioning kidneys?

OBJECTIVES

To evaluate the effects of diuretic stimulation on Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI) techniques in transplanted kidneys.

METHODS

33 transplanted kidney recipients underwent DWI and DTI sequences before and after furosemide. Cortical and medullary Apparent Diffusion Coefficient (ADC) and Fractional Anisotropy (FA) values were calculated in transplanted kidneys. Patients were divided into two groups according to their estimated glomerular rate filtration (Group A ≥ 60 ml/min and Group B < 60 ml/min). Wilcoxon matched pairs signed rank test was applied to compare pre- and post-furosemide values. ADC and FA values were compared between the 2 groups using a Mann-Whitney U test. Receiver Operating Curves (ROC) analysis was performed to predict normal renal function.

RESULTS

Wilcoxon test revealed a statistically significant difference for all pre- and post- ADC and FA values in group B. For group A, a significant difference was found comparing pre- and post-medullary ADC and FA values (p = 0.0151 and p = 0.0054). In the comparison between group A and group B, cortical and medullary mean ADC values were significantly different before and after furosemide. With regard to medullary FA values, a significant difference was found between groups before and after diuretic stimulation (p respectively of 0.004 and 0.042). Comparing cortical FA mean values, no statistical difference was observed between groups before and after furosemide. The highest Area Under Curve values were reported for cortical ADC (0.878) and medullary ADC (0.863) before diuretic bolus.

CONCLUSIONS

In transplanted kidneys, furosemide did not improve the differentiation between normal and reduced function.

Intravoxel incoherent motion (IVIM) at 3.0 T: evaluation of early renal function changes in type 2 diabetic patients

PURPOSE

The purpose of this study was to evaluate the utility of intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) parameters in identifying early renal function changes in diabetics.

METHODS

A total of 40 patients with type 2 diabetes mellitus and 20 healthy control subjects underwent multiple b value DWI. The diabetic patients were stratified into two groups based on albuminuria category: NAU (normal to mildly increased albuminuria; ACR < 30 mg/g) and MAU (moderately increased albuminuria; 30 ≤ ACR < 300 mg/g). The mean cortical and medullary IVIM parameters (D, D*, f, and ADC) were calculated and compared among the different groups, and the correlation of ACR and eGFR was also calculated.

RESULTS

The present study revealed the limited water molecule diffusion and hyperperfusion of renal cortex and medulla in diabetic patients before proteinuria detection. Mean cortical and medullary D values negatively correlated with the ACR values in diabetics with 30 ≤ ACR < 300 mg/g, whereas no correlation was found between ACR values and other IVIM parameters.

CONCLUSION

IVIM DWI might be helpful in noninvasively identifying early-stage DN. The IVIM parametric values are more sensitive than the ACR in detecting early-stage kidney changes.

Use of intravoxel incoherent motion diffusion-weighted imaging to detect early changes in diabetic kidneys

OBJECTIVE

The purpose of the study was to examine differences in kidney intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) parameters in early-stage diabetic patients versus healthy controls.

MATERIALS AND METHODS

Nineteen type 2 diabetic patients (group A) with a urinary albumin-to-creatinine ratio (ACR) < 30 mg/g and an estimated glomerular filtration rate (eGFR) of 80-120 mL/(min 1.73 m²) and twelve healthy volunteers (group B) were recruited. Kidneys were scanned with 1.5-Tesla IVIM-DWI. Nine b values (0, 50, 100, 150, 200, 300, 400, 600, and 800 s/mm²) were used. The parameters derived from IVIM-DWI were calculated for each kidney by two radiologists and included the perfusion fraction (f), diffusion coefficient (D), and pseudo-diffusion coefficient (D*). The mean values of f, D, and D* were calculated by selecting multiple regions of interest in the kidney. The diagnostic performance of the f, D, and D* values for the diagnosis of early diabetic kidney changes was determined by receiver operating characteristic analysis. Three radiologists independently measured the parameters derived from IVIM-DWI in the two groups by free-hand placing regions of interest, and the interclass coefficients (ICCs) were analyzed by SPSS.16.0 software.

RESULTS

The f values of the kidneys were significantly higher in diabetic patients than in healthy volunteers. The D value of the kidneys was significantly lower in diabetic patients than in healthy volunteers. No significant differences in the D* values of the kidneys were observed between diabetic patients and healthy volunteers. The D values of the right kidneys were significantly higher than those of the left kidneys in both groups. The results of the receiver operating characteristic analysis were as follows: left kidney-f value AUC = 0.650 (cutoff point ≥ 27.49%) and D value AUC = 0.752 (cutoff point ≤ 1.68 × 10^-3 mm²/s); and right kidney-f value AUC = 0.650 (cutoff point ≥ 28.24%) and D value AUC = 0.752 (cutoff point ≤ 1.81 × 10^-3 mm²/s). The diagnostic performance of the D* value was very low (AUC < 0.6). No significant differences were present between the areas under the curves of the f and D values (P > 0.05). The ICCs of the f value and D value were between 0.637 and 0.827. The ICC of the D* value was less than 0.3.

CONCLUSION

The results of our study suggest that changes in kidneys detected by IVIM-DWI may serve as indicators of early diabetic kidney disease.

Separating fetal and maternal placenta circulations using multiparametric MRI

PURPOSE

The placenta is a vital organ for the exchange of oxygen, nutrients, and waste products between fetus and mother. The placenta may suffer from several pathologies, which affect this fetal-maternal exchange, thus the flow properties of the placenta are of interest in determining the course of pregnancy. In this work, we propose a new multiparametric model for placental tissue signal in MRI.

METHODS

We describe a method that separates fetal and maternal flow characteristics of the placenta using a 3-compartment model comprising fast and slowly circulating fluid pools, and a tissue pool is fitted to overlapping multiecho T2 relaxometry and diffusion MRI with low b-values. We implemented the combined model and acquisition on a standard 1.5 Tesla clinical system with acquisition taking less than 20 minutes.

RESULTS

We apply this combined acquisition in 6 control singleton placentas. Mean myometrial T2 relaxation time was 123.63 (±6.71) ms. Mean T2 relaxation time of maternal blood was 202.17 (±92.98) ms. In the placenta, mean T2 relaxation time of the fetal blood component was 144.89 (±54.42) ms. Mean ratio of maternal to fetal blood volume was 1.16 (±0.6), and mean fetal blood saturation was 72.93 (±20.11)% across all 6 cases.

CONCLUSION

The novel acquisition in this work allows the measurement of histologically relevant physical parameters, such as the relative proportions of vascular spaces. In the placenta, this may help us to better understand the physiological properties of the tissue in disease.

Placenta microstructure and microcirculation imaging with diffusion MRI

PURPOSE

To assess which microstructural models best explain the diffusion-weighted MRI signal in the human placenta.

METHODS

The placentas of nine healthy pregnant subjects were scanned with a multishell, multidirectional diffusion protocol at 3T. A range of multicompartment biophysical models were fit to the data, and ranked using the Bayesian information criterion.

RESULTS

Anisotropic extensions to the intravoxel incoherent motion model, which consider the effect of coherent orientation in both microvascular structure and tissue microstructure, consistently had the lowest Bayesian information criterion values. Model parameter maps and model selection results were consistent with the physiology of the placenta and surrounding tissue.

CONCLUSION

Anisotropic intravoxel incoherent motion models explain the placental diffusion signal better than apparent diffusion coefficient, intravoxel incoherent motion, and diffusion tensor models, in information theoretic terms, when using this protocol. Future work will aim to determine if model-derived parameters are sensitive to placental pathologies associated with disorders, such as fetal growth restriction and early-onset pre-eclampsia. Magn Reson Med 80:756-766, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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.

Simultaneous magnetic resonance diffusion and pseudo-diffusion tensor imaging

PURPOSE

An emerging topic in diffusion magnetic resonance is imaging blood microcirculation alongside water diffusion using the intravoxel incoherent motion (IVIM) model. Recently, a combined IVIM diffusion tensor imaging (IVIM-DTI) model was proposed, which accounts for both anisotropic pseudo-diffusion due to blood microcirculation and anisotropic diffusion due to tissue microstructures. In this article, we propose a robust IVIM-DTI approach for simultaneous diffusion and pseudo-diffusion tensor imaging.

METHODS

Conventional IVIM estimation methods can be broadly divided into two-step (diffusion and pseudo-diffusion estimated separately) and one-step (diffusion and pseudo-diffusion estimated simultaneously) methods. Here, both methods were applied on the IVIM-DTI model. An improved one-step method based on damped Gauss-Newton algorithm and a Gaussian prior for the model parameters was also introduced. The sensitivities of these methods to different parameter initializations were tested with realistic in silico simulations and experimental in vivo data.

RESULTS

The one-step damped Gauss-Newton method with a Gaussian prior was less sensitive to noise and the choice of initial parameters and delivered more accurate estimates of IVIM-DTI parameters compared to the other methods.

CONCLUSION

One-step estimation using damped Gauss-Newton and a Gaussian prior is a robust method for simultaneous diffusion and pseudo-diffusion tensor imaging using IVIM-DTI model. Magn Reson Med 79:2367-2378, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Application of noninvasive functional imaging to monitor the progressive changes in kidney diffusion and perfusion in contrast-induced acute kidney injury rats at 3.0 T

OBJECTIVES

Contrast-induced acute kidney injury is a prevalent cause of renal failure, and the noninvasive tools to monitor its progress are lacking. We applied intravoxel incoherent motion (IVIM) DWI to measure the progressive changes in kidney diffusion and perfusion of CI-AKI.

METHODS

Twenty-four rats received Iopromide (370 mg/ml, 1600 mg iodine/kg) to induce CI-AKI. IVIM DWI was performed on rats (n = 6) at 24 h prior to and 12, 24, 48, 72, and 96 h after the injection using a 3.0 T MRI scanner. The progressive changes in the diffusion (D) and perfusion parameters (D* and f) were studied in the cortex (CO), outer medulla (OM), and inner medulla (IM). For the histology group (n = 18), three rats were sacrificed at each time point.

RESULTS

In the CO, D reduced progressively from 24 to 48 h (P < 0.001) and increased starting from 72 h (P < 0.001). However, D decreased until to 72 h in the medulla (P < 0.001) and increased starting from 96 h (P < 0.001). D* decreased to the bottom at 24 h in the cortex and medulla (P = 0.037) and started to recover at 48 h (P = 0.007). f decreased in the cortex and medulla in an early stage (12 h) (P = 0.035) of CI-AKI and then ascended in the later stage (72 h) (P = 0.017). The H & E staining showed different degrees of serial pathological change including cloudy swelling, atrophy, even necrosis, and interstitial vasodilation of tubule epithelial cells and glomerulus cells.

CONCLUSION

Our study demonstrates the feasibility of using IVIM DWI to monitor the progress of CI-AKI, implying that IVIM DWI is a useful biomarker in the staging of CI-AKI.

Renal parenchyma impairment characterization in partial unilateral ureteral obstruction in mice with intravoxel incoherent motion-MRI

Ureteropelvic junction obstruction constitutes a major cause of progressive pediatric renal disease. The biological mechanisms underlying the renal response to obstruction can be investigated using a clinically relevant mouse model of partial unilateral ureteral obstruction (pUUO). Renal function and kidney morphology data can be evaluated using renal ultrasound, scintigraphy and uro-magnetic resonance imaging (uro-MRI), but these methods are poorly linked to histological change and not all are quantitative. Here, we propose to investigate pUUO for the first time using an intravoxel incoherent motion diffusion sequence. The aim of this study was to quantitatively characterize impairment of the kidney parenchyma in the pUUO model. This quantitative MRI method was able to assess the perfusion and microstructure of the kidney without requiring the injection of a contrast agent. The results suggest that a perfusion fraction (f) reduction is associated with a decrease in the volume of the renal parenchyma, which could be related to decreased renal vascularization. The latter may occur before impairment by fibrosis and the findings are in accordance with the literature using the UUO mice model and, more specifically, on pUUO. Further investigation is required before this technique can be made available for the diagnosis and management of children with antenatal hydronephrosis and to select the optimal timing of surgery if required.

REnal Flow and Microstructure AnisotroPy (REFMAP) MRI in Normal and Peritumoral Renal Tissue

BACKGROUND

Diffusion-weighted imaging (DWI) provides insight into the pathophysiology underlying renal dysfunction. Variants of DWI include intravoxel incoherent motion (IVIM), which differentiates between microstructural diffusion and vascular or tubular flow, and diffusion tensor imaging (DTI), which quantifies diffusion directionality.

PURPOSE

To investigate the reproducibility of joint IVIM-DTI and compare controls to presurgical renal mass patients.

STUDY TYPE

Prospective cross-sectional.

SUBJECTS

Thirteen healthy controls and ten presurgical renal mass patients were scanned. Ten controls were scanned twice to investigate reproducibility.

FIELD STRENGTH/SEQUENCE

Subjects were scanned on a 3T system using 10 b-values and 20 diffusion directions for IVIM-DTI in a study approved by the local Institutional Review Board.

ASSESSMENT

Retrospective coregistration and measurement of joint IVIM-DTI parameters were performed.

STATISTICAL ANALYSIS

Parameter reproducibility was defined as intraclass correlation coefficient (ICC) >0.7 and coefficient of variation (CV) <30%. Patient data were stratified by lesion side (contralateral/ipsilateral) for comparison with controls. Corticomedullary differentiation was evaluated.

RESULTS

In controls, the reproducible subset of REnal Flow and Microstructure AnisotroPy (REFMAP) parameters had average ICC = 0.82 and CV = 7.5%. In renal mass patients, medullary fractional anisotropy (FA) was significantly lower than in controls (0.227 ± 0.072 vs. 0.291 ± 0.044, P = 0.016 for the kidney contralateral to the mass and 0.228 ± 0.070 vs. 0.291 ± 0.044, P = 0.018 for the kidney ipsilateral). In the kidney ipsilateral to the mass, cortical D_p,radial was significantly higher than in controls (P = 0.012). Conversely, medullary D_p,axial was significantly lower in contralateral than ipsilateral kidneys (P = 0.027) and normal controls (P = 0.044).

DATA CONCLUSION

REFMAP-MRI parameters provide unique information regarding renal dysfunction. In presurgical renal mass patients, directional flow changes were noted that were not identified with IVIM analysis alone. Both contralateral and ipsilateral kidneys in patients show reductions in structural diffusivities and anisotropy, while flow metrics showed opposing changes in contralateral vs. ipsilateral kidneys.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Value of intravoxel incoherent motion in assessment of pathological grade of clear cell renal cell carcinoma

Background Intravoxel incoherent motion (IVIM) can provide a unique view of tissue perfusion without the use of exogenous contrast agents. Purpose To investigate the value of IVIM in assessing grades of clear cell renal cell carcinoma (CRCC). Material and Methods A total of 107 patients with pathologically proven CRCC were included, 26 with grade I, 27 with grade II, 25 with grade III, and 29 with grade IV. These tumors were divided into low (I + II) and high grades (III + IV). Nine b values (0, 30, 50, 80, 150, 300, 500, 800, and 1500 s/mm²) were used in diffusion-weighted imaging (DWI). The tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f) were calculated using bi-exponential fitting of the diffusion data. Results The D values of the four groups were 1.83 ± 0.38, 1.23 ± 0.19, 1.07 ± 0.26, and 0.37 ± 0.11 × 10^-3 mm²/s ( P < 0.05). The D* values of the four groups were 0.079 ± 0.021, 0.053 ± 0.019, 0.047 ± 0.022, and 0.033 ± 0.017 ( P < 0.05). The f values of the four groups were 0.208 ± 0.09, 0.341 ± 0.12, 0.373 ± 0.15, and 0.461 ± 0.17 ( P < 0.05). Both the D and D* values correlated negatively with CRCC grading ( r = -0.677 and -0.693, P < 0.05). The f values correlated positively with CRCC grading (r = 0.699, P < 0.05). The areas of the D, D*, and f values under the ROC curves to diagnose low and high CRCC grades were 0.934, 0.837, and 0.793, respectively. The cutoff values of D, D*, and f were 1.13, 0.056, and 0.376, respectively; the diagnostic performance for low and high CRCC grading had a sensitivity of 82.0%, 80.7%, and 83.2% and a specificity of 90.8%, 86.3%, and 82.6%. Conclusion IVIM may provide information for differentiating CRCC grades.

What can we see with IVIM MRI?

Intravoxel Incoherent Motion (IVIM) refers to translational movements which within a given voxel and during the measurement time present a distribution of speeds in orientation and/or amplitude. The IVIM concept has been used to estimate perfusion in tissues as blood flow in randomly oriented capillaries mimics a pseudo-diffusion process. IVIM-based perfusion MRI, which does not require contrast agents, has gained momentum recently, especially in the field oncology. In this introductory review the basic concepts, models, technical requirements and limitations inherent to IVIM-based perfusion MRI are outlined, as well as new, non-perfusion applications of IVIM MRI, such as virtual MR Elastography.