Statistical evaluation of diffusion‐weighted imaging of the human kidney

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.

Noninvasive Assessment of Diabetic Kidney Disease With MRI: Hype or Hope?

Owing to the increasing prevalence of diabetic mellitus, diabetic kidney disease (DKD) is presently the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early identification and disease interception is of paramount clinical importance for DKD management. However, current diagnostic, disease monitoring and prognostic tools are not satisfactory, due to their low sensitivity, low specificity, or invasiveness. Magnetic resonance imaging (MRI) is noninvasive and offers a host of contrast mechanisms that are sensitive to pathophysiological changes and risk factors associated with DKD. MRI tissue characterization involves structural and functional information including renal morphology (kidney volume (TKV) and parenchyma thickness using T1- or T2-weighted MRI), renal microstructure (diffusion weighted imaging, DWI), renal tissue oxygenation (blood oxygenation level dependent MRI, BOLD), renal hemodynamics (arterial spin labeling and phase contrast MRI), fibrosis (DWI) and abdominal or perirenal fat fraction (Dixon MRI). Recent (pre)clinical studies demonstrated the feasibility and potential value of DKD evaluation with MRI. Recognizing this opportunity, this review outlines key concepts and current trends in renal MRI technology for furthering our understanding of the mechanisms underlying DKD and for supplementing clinical decision-making in DKD. Progress in preclinical MRI of DKD is surveyed, and challenges for clinical translation of renal MRI are discussed. Future directions of DKD assessment and renal tissue characterization with (multi)parametric MRI are explored. Opportunities for discovery and clinical break-through are discussed including biological validation of the MRI findings, large-scale population studies, standardization of DKD protocols, the synergistic connection with data science to advance comprehensive texture analysis, and the development of smart and automatic data analysis and data visualization tools to further the concepts of virtual biopsy and personalized DKD precision medicine. We hope that this review will convey this vision and inspire the reader to become pioneers in noninvasive assessment and management of DKD with MRI. 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.

Image downsampling expedited adaptive least-squares (IDEAL) fitting improves intravoxel incoherent motion (IVIM) analysis in the human kidney

PURPOSE

To improve the reliability of intravoxel incoherent motion (IVIM) model parameter estimation for the DWI in the kidney using a novel image downsampling expedited adaptive least-squares (IDEAL) approach.

METHODS

The robustness of IDEAL was investigated using simulated DW-MRI data corrupted with different levels of Rician noise. Subsequently, the performance of the proposed method was tested by fitting bi- and triexponential IVIM model to in vivo renal DWI data acquired on a clinical 3 Tesla MRI scanner and compared to conventional approaches (fixed D* and segmented fitting).

RESULTS

The numerical simulations demonstrated that the IDEAL algorithm provides robust estimates of the IVIM parameters in the presence of noise (SNR of 20) as indicated by relatively low absolute percentage bias (maximal sMdPB <20%) and normalized RMSE (maximal RMSE <28%). The analysis of the in vivo data showed that the IDEAL-based IVIM parameter maps were less noisy and more visually appealing than those obtained using the fixed D* and segmented methods. Further, coefficients of variation for nearly all IVIM parameters were significantly reduced in cortex and medulla for IDEAL-based biexponential (coefficients of variation: 4%-50%) and triexponential (coefficients of variation: 7.5%-75%) IVIM modelling compared to the segmented (coefficients of variation: 4%-120%) and fixed D* (coefficients of variation: 17%-174%) methods, reflecting greater accuracy of this method.

CONCLUSION

The proposed fitting algorithm yields more robust IVIM parameter estimates and is less susceptible to poor SNR than the conventional fitting approaches. Thus, the IDEAL approach has the potential to improve the reliability of renal DW-MRI analysis for clinical applications.

Evaluation of Sodium Relaxation Times and Concentrations in the Achilles Tendon Using MRI

Sodium magnetic resonance imaging (MRI) can be used to evaluate the change in the proteoglycan content in Achilles tendons (ATs) of patients with different AT pathologies by measuring the ²³Na signal-to-noise ratio (SNR). As ²³Na SNR alone is difficult to compare between different studies, because of the high influence of hardware configurations and sequence settings on the SNR, we further set out to measure the apparent tissue sodium content (aTSC) in the AT as a better comparable parameter. Ten healthy controls and one patient with tendinopathy in the AT were examined using a clinical 3 Tesla (T) MRI scanner in conjunction with a dual tuned ¹H/²³Na surface coil to measure ²³Na SNR and aTSC in their ATs. ²³Na T₁ and T₂* of the AT were also measured for three controls to correct for different relaxation behavior. The results were as follows: ²³Na SNR = 11.7 ± 2.2, aTSC = 82.2 ± 13.9 mM, ²³Na T₁ = 20.4 ± 2.4 ms, ²³Na T_2s* = 1.4 ± 0.4 ms, and ²³Na T_2l* = 13.9 ± 0.8 ms for the whole AT of healthy controls with significant regional differences. These are the first reported aTSCs and ²³Na relaxation times for the AT using sodium MRI and may serve for future comparability in different studies regarding examinations of diseased ATs with sodium MRI.

Measuring glomerular blood transfer rate in kidney using diffusion-weighted arterial spin labeling

PURPOSE

To propose a two-compartment renal perfusion model for calculating glomerular blood transfer rate ( k G $$ {k}_G $$ ) as a new measure of renal function.

THEORY

The renal perfusion signal was divided into preglomerular and postglomerular flows according to flow velocity. By analyzing perfusion signals acquired with and without diffusion gradients, we estimated k G $$ {k}_G $$ , the blood transfer rate from the afferent arterioles into the glomerulus.

METHODS

A multislice multidelay diffusion-weighted arterial spin labeling sequence was applied to subjects with no history of renal dysfunctions. In the multiple b-value experiment, images were acquired with seven b-values to validate the bi-exponential decays of the renal perfusion signal and to determine the appropriate b-value for suppressing preglomerular flow. In the caffeine challenge, six subjects were scanned twice on the caffeine day and the control day. The k G $$ {k}_G $$ values of the two dates were compared.

RESULTS

The perfusion signal showed a bi-exponential decay with b-values. There was no significant difference in renal blood flow and arterial transit time between caffeine and control days. In contrast, cortical k G $$ {k}_G $$ was significantly higher on the caffeine day (caffeine day: 106 . 0 ± 20 . 3 $$ 106.0\pm 20.3 $$ min - 1 $$ {}^{-1} $$ control day: 78 . 8 ± 22 . 9 $$ 78.8\pm 22.9 $$ min - 1 $$ {}^{-1} $$ ). These results were consistent with those from the literature.

CONCLUSION

We showed that the perfusion signal consists of two compartments of preglomerular flow and postglomerular flow. The proposed diffusion-weighted arterial spin labeling could measure the glomerular blood transfer rate ( k G $$ {k}_G $$ ), which was sensitive enough to noninvasively monitor the caffeine-induced vasodilation of afferent arterioles.

Comparative study of conventional diffusion-weighted imaging and introvoxel incoherent motion in assessment of pathological grade of clear cell renal cell carcinoma

OBJECTIVE

To quantitatively compare the diagnostic values of conventional diffusion-weighted imaging (DWI) and introvoxel incoherent motion (IVIM) analysis of microstructural differences for clear cell renal cell carcinoma (ccRCC).

METHODS

Multiple b value DWIs and IVIMs were performed in patients with 146 ccRCCs, 42 with Grade Ⅰ, 46 with Grade Ⅱ, 28 with Grade Ⅲ and 30 with Grade Ⅳ. These tumours were divided into low (Ⅰ+Ⅱ, n = 88) and high grades (Ⅲ+Ⅳ, n = 58). The diagnostic efficacy of various diffusion parameters for predicting ccRCC grades was compared.

RESULTS

The mean signal-to-noise ratios (SNRs) of IVIM images at b = 0, 800 and 1500 s/mm2 were 31.9, 12.3 and 8.4, respectively. The apparent diffusion coefficient (ADC), D and D* values correlated negatively with ccRCC grading (r = -0.786,-0.913, -0879, p < 0.05). f values correlated positively with ccRCC grading (r = 0.811, p < 0.05). The ADC, D and D* values were higher for Grade Ⅱ ccRCC than that of Grade Ⅲ ccRCC (p < 005), however, f values were higher for Grade Ⅲ ccRCC than that of Grade Ⅱ ccRCC (p < 005). Receiver operating characteristic curve analyses showed that D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. The area under the curve, sensitivity, specificity and accuracy of the D values were 0.963, 0.960; 90.9%, 89.1%; 81.0%,78.6 and 89.0%, 87.8%, respectively. For pairwise comparisons of receiver operating characteristic curves and diagnostic efficacy, ADC was worse than IVIM (all p < 0.05).

CONCLUSION

IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images.

ADVANCES IN KNOWLEDGE

1. D values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ ccRCC grading. 2. IVIM parameters have better performance than ADC in differentiating ccRCC grading, given an adequate SNR of IVIM images. 3. The ADC, D and D* values correlated negatively with ccRCC grading, however, f values correlated positively with ccRCC grading.

Quantification of Sodium Relaxation Times and Concentrations as Surrogates of Proteoglycan Content of Patellar CARTILAGE at 3T MRI

Sodium MRI has the potential to depict cartilage health accurately, but synovial fluid can influence the estimation of sodium parameters of cartilage. Therefore, this study aimed to reduce the impact of synovial fluid to render the quantitative compositional analyses of cartilage tissue technically more robust. Two dedicated protocols were applied for determining sodium T1 and T2* relaxation times. For each protocol, data were acquired from 10 healthy volunteers and one patient with patellar cartilage damage. Data recorded with multiple repetition times for T1 measurement and multi-echo data acquired with an additional inversion recovery pulse for T2* measurement were analysed using biexponential models to differentiate longitudinal relaxation components of cartilage (T1,car) and synovial fluid (T1,syn), and short (T2s*) from long (T2l*) transversal relaxation components. Sodium relaxation times and concentration estimates in patellar cartilage were successfully determined: T1,car = 14.5 ± 0.7 ms; T1,syn = 37.9 ± 2.9 ms; c(T1-protocol) = 200 ± 48 mmol/L; T2s* = 0.4 ± 0.1 ms; T2l* = 12.6 ± 0.7 ms; c(T2*-protocol) = 215 ± 44 mmol/L for healthy volunteers. In conclusion, a robust determination of sodium relaxation times is possible at a clinical field strength of 3T to quantify sodium concentrations, which might be a valuable tool to determine cartilage health.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

The role of diffusion-weighted MRI and contrast-enhanced MRI for differentiation between solid renal masses and renal cell carcinoma subtypes

PURPOSE

To assess the value of diffusion-weighted magnetic resonance imaging (DW-MRI) and contrast-enhanced MRI (CE-MRI) for differentiation between benign and malignant solid renal masses, renal cell carcinoma (RCC) subtypes, oncocytomas, and lipid-poor angiomyolipomas (LP-AML).

METHODS

Minimum or lowest 'apparent diffusion coefficient' (ADC₁) and representative ADC values (ADC₂) of 112 renal masses (n: 46 benign renal mass, n: 66 malignant renal mass) were measured on DW-MRI images (b 50, 400, 800 s/mm²). Signal intensity (SI) measurements were performed in normal renal parenchyma and most avid enhanced area of the renal masses at precontrast, corticomedullary, and nephrographic phases on CE-MRI. Contrast enhancement rate (CER) and contrast enhancement index (CEI) values of renal masses were compared between benign-malignant renal masses and RCC subtypes, oncocytomas, and LP-AMLs.

RESULTS

There was no significant difference between ADC₁, ADC₂ values, and SI of benign and malignant renal masses (p = 0.721, p = 0.255, p = 0.872). Mean ADC₁ and ADC₂ values of clear cell RCCs were significantly higher than nonclear cell RCCs (p = 0.005 p = 0.002). Mean CER value of clear cell RCCs was significantly higher than nonclear cell RCCs in nephrographic phase (p = 0.003). Mean CEI values of clear cell RCCs were significantly higher than nonclear cell RCCs in the corticomedullary and nephrographic phase (p = 0.027 vs. 0.008). LP-AMLs were differentiated from other renal masses with wash-out phenomenon.

CONCLUSION

Combined usage of ADC, SI, CER, and CEI values may be useful for discrimination between RCC subtypes, oncocytomas, and lipid-poor AMLs.

Spectral diffusion analysis of kidney intravoxel incoherent motion MRI in healthy volunteers and patients with renal pathologies

PURPOSE

To assess the feasibility of measuring tubular and vascular signal fractions in the human kidney using nonnegative least-square (NNLS) analysis of intravoxel incoherent motion data collected in healthy volunteers and patients with renal pathologies.

METHODS

MR imaging was performed at 3 Tesla in 12 healthy subjects and 3 patients with various kidney pathologies (fibrotic kidney disease, failed renal graft, and renal masses). Relative signal fractions f and mean diffusivities of the diffusion components in the cortex, medulla, and renal lesions were obtained using the regularized NNLS fitting of the intravoxel incoherent motion data. Test-retest repeatability of the NNLS approach was tested in 5 volunteers scanned twice.

RESULTS

In the healthy kidneys, the NNLS method yielded diffusion spectra with 3 distinguishable components that may be linked to the slow tissue water diffusion, intermediate tubular and vascular flow, and fast blood flow in larger vessels with the relative signal fractions, f_slow , f_interm and f_fast , respectively. In the pathological kidneys, the diffusion spectra varied substantially from those acquired in the healthy kidneys. Overall, the renal cyst showed substantially higher f_interm and lower f_slow , whereas the fibrotic kidney, failed renal graft, and renal cell carcinoma demonstrated the opposite trend.

CONCLUSION

NNLS-based intravoxel incoherent motion could potentially become a valuable tool in assessing changes in tubular and vascular volume fractions under pathophysiological conditions.

Comparison of models of diffusion in Wilms' tumours and normal contralateral renal tissue

Objective

ADC (Apparent Diffusion Coefficient) derived from Diffusion-Weighted Imaging (DWI) has shown promise as a non-invasive quantitative imaging biomarker in Wilms’ tumours. However, many non-Gaussian models could be applied to DWI. This study aimed to compare the suitability of four diffusion models (mono exponential, IVIM [Intravoxel Incoherent Motion], stretched exponential, and kurtosis) in Wilms’ tumours and the unaffected contralateral kidneys.

Materials and methods

DWI data were retrospectively reviewed (110 Wilms’ tumours and 75 normal kidney datasets). The goodness of fit for each model was measured voxel-wise using Akaike Information Criteria (AIC). Mean AIC was calculated for each tumour volume (or contralateral normal kidney tissue). One-way ANOVAs with Greenhouse–Geisser correction and post hoc tests using the Bonferroni correction evaluated significant differences between AIC values; the lowest AIC indicating the optimum model.

Results

IVIM and stretched exponential provided the best fits to the Wilms’ tumour DWI data. IVIM provided the best fit for the normal kidney data. Mono exponential was the least appropriate fitting method for both Wilms’ tumour and normal kidney data.

Discussion

The diffusion weighted signal in Wilms’ tumours and normal kidney tissue does not exhibit a mono-exponential decay and is better described by non-Gaussian models of diffusion.

MRI of tumor T cell infiltration in response to checkpoint inhibitor therapy

BACKGROUND

Immune checkpoint inhibitors, the most widespread class of immunotherapies, have demonstrated unique response patterns that are not always adequately captured by traditional response criteria such as the Response Evaluation Criteria in Solid Tumors or even immune-specific response criteria. These response metrics rely on monitoring tumor growth, but an increase in tumor size and/or appearance after starting immunotherapy does not always represent tumor progression, but also can be a result of T cell infiltration and thus positive treatment response. Therefore, non-invasive and longitudinal monitoring of T cell infiltration are needed to assess the effects of immunotherapies such as checkpoint inhibitors. Here, we proposed an innovative concept that a sufficiently large influx of tumor infiltrating T cells, which have a smaller diameter than cancer cells, will change the diameter distribution and decrease the average size of cells within a volume to a degree that can be quantified by non-invasive MRI.

METHODS

We validated our hypothesis by studying tumor response to combination immune-checkpoint blockade (ICB) of anti-PD-1 and anti-CTLA4 in a mouse model of colon adenocarcinoma (MC38). The response was monitored longitudinally using Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion (IMPULSED), a diffusion MRI-based method which has been previously shown to non-invasively map changes in intracellular structure and cell sizes with the spatial resolution of MRI, in cell cultures and in animal models. Tumors were collected for immunohistochemical and flow cytometry analyzes immediately after the last imaging session.

RESULTS

Immunohistochemical analysis revealed that increased T cell infiltration of the tumors results in a decrease in mean cell size (eg, a 10% increase of CD3+ T cell fraction results a ~1 µm decrease in the mean cell size). IMPULSED showed that the ICB responders, mice with tumor volumes were less than 250 mm3 or had tumors with stable or decreased volumes, had significantly smaller mean cell sizes than both Control IgG-treated tumors and ICB non-responder tumors.

CONCLUSIONS

IMPULSED-derived cell size could potentially serve as an imaging marker for differentiating responsive and non-responsive tumors after checkpoint inhibitor therapies, a current clinical challenge that is not solved by simply monitoring tumor growth.

Mapping hepatocyte size in vivo using temporal diffusion spectroscopy MRI

PURPOSE

The goal of this study is to implement a noninvasive method for in vivo mapping of hepatocyte size. This method will have a broad range of clinical and preclinical applications, as pathological changes in hepatocyte sizes are relevant for the accurate diagnosis and assessments of treatment response of liver diseases.

METHODS

Building on the concepts of temporal diffusion spectroscopy in MRI, a clinically feasible imaging protocol named IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) has been developed, which is able to report measurements of cell sizes noninvasively. This protocol acquires a selected set of diffusion imaging data and fits them to a model of water compartments in tissues to derive robust estimates of the cellular structures that restrict free diffusion. Here, we adapt and further develop this approach to measure hepatocyte sizes in vivo. We validated IMPULSED in livers of mice and rats and implemented it to image healthy human subjects using a clinical 3T MRI scanner.

RESULTS

The IMPULSED-derived mean hepatocyte sizes for rats and mice are about 15-20 µm and agree well with histological findings. Maps of mean hepatocyte size for humans can be achieved in less than 15 minutes, a clinically feasible scan time.

CONCLUSION

Our results suggest that this method has potential to overcome major limitations of liver biopsy and provide noninvasive mapping of hepatocyte sizes in clinical applications.

A multimodal computer-aided diagnostic system for precise identification of renal allograft rejection: Preliminary results

PURPOSE

Early assessment of renal allograft function post-transplantation is crucial to minimize and control allograft rejection. Biopsy - the gold standard - is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer-assisted diagnostic (Renal-CAD) system was developed to assess kidney transplant function.

METHODS

The developed Renal-CAD system integrates data collected from two image-based sources and two clinical-based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion-weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm 2 ), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal-CAD system initially performed kidney segmentation using the level-set method, followed by estimation of the ADCs from DW-MRIs and the R2* from BOLD-MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning-based classifier, namely stacked autoencoders (SAEs) to differentiate non-rejection (NR) from acute rejection (AR) renal transplants.

RESULTS

Using a leave-one-subject-out cross-validation approach along with SAEs, the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross-validation approach, the Renal-CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88.

CONCLUSION

The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal-CAD system.

Comparison of PGSE and STEAM DTI acquisitions with varying diffusion times for probing anisotropic structures in human kidneys

PURPOSE

To evaluate the sensitivity of stimulated-echo acquisition mode (STEAM) and pulsed-gradient spin-echo (PGSE) diffusion tensor imaging (DTI) acquisitions with different diffusion times for measuring renal tissue anisotropy.

METHODS

Twelve healthy volunteers underwent an MRI examination at a 3T scanner including STEAM and PGSE DTI with variable diffusion times Δ (20.3, 37 and 125 ms). Three volunteers were scanned twice to test the reproducibility for repeated examinations. Diffusion parameters fractional anisotropy (FA) and apparent diffusion coefficient (ADC) in the automatically segmented cortical and medullary regions of interests in both kidneys were calculated and averaged over all subjects for further analysis. Moreover, 5-grade qualitative evaluation of the FA and ADC maps from each sequence was conducted by two experienced radiologists in a consensus.

RESULTS

The cortex-medulla difference in the STEAM sequence was significantly higher than that in PGSE with short ∆ = 20.3 ms (P < 0.001) and in PGSE with intermediate ∆ = 37 ms (P < 0.05) diffusion times. Reproducibility of the FA/ADC measurements was very good and comparable for all acquisition modes investigated. For the FA maps, the PGSE sequence with intermediate diffusion time scored highest in the subjective visual assessment of radiologists.

CONCLUSION

The delineation of anisotropy in renal tissue is depending on the used diffusion time of the DTI sequence. A PGSE acquisition at a diffusion time of about 37 ms provides reproducible results with optimal corticomedullary contrast in FA and ADC maps and good image quality.

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.

Noninvasive Monitoring and Evaluation of the Renal Structure and Function in a Mouse Model of Unilateral Ureteral Occlusion Using Microcomputed Tomography

Mouse unilateral ureteral occlusion (UUO) is widely used as a model of renal experimental obstructive nephropathy with interstitial fibrosis. Microcomputed tomography (micro-CT) imaging has the potential to produce quantitative images. The aim of this study was to establish standard images of micro-CT for renal anatomic and functional evaluations in a mouse model of UUO. UUO was induced in adult male mice BALB/c. In total, 27 mice were used in this study. Three mice per group (a total of 6 groups) were examined with contrast-enhanced micro-CT prior to UUO (day 0) and on days 1, 3, 5, 7, 10, and 14 after UUO. In order to determine the histopathologic correlations at each point in time, contrast-enhanced micro-CT imaging was performed in the 18 remaining mice. All animals were sacrificed, and both kidneys were harvested after the final micro-CT examination. UUO resulted in hydronephrosis and changes in the renal parenchyma. The predominant alteration was substantial changes in the hemodynamics of the renal vascular system after ureteral obstruction for 24 hours or longer, which may be resulting from increased action of vasoconstrictors versus vasodilators. The renal parenchyma was significantly reduced after 1 week, and the features of the histologic changes supported the findings of the micro-CT images. In the contralateral unobstructed kidneys, the images showed a normal structure and function and the pathohistology revealed a normal histoarchitecture. Micro-CT is a useful tool for providing noninvasive monitoring and evaluating the renal structure and function.

A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction

This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.

Perfusion-weighted and diffusion-weighted magnetic resonance imaging of the liver, spleen, and kidneys of healthy adult male cats

OBJECTIVE To describe perfusion and diffusion characteristics of the liver, spleen, and kidneys of healthy adult male cats as determined by morphological, perfusion-weighted, and diffusion-weighted MRI. ANIMALS 12 healthy adult male cats. PROCEDURES Each cat was anesthetized. Morphological, perfusion-weighted, and diffusion-weighted MRI of the cranial aspect of the abdomen was performed. A region of interest (ROI) was established on MRI images for each of the following structures: liver, spleen, cortex and medulla of both kidneys, and skeletal muscle. Signal intensity was determined, and a time-intensity curve was generated for each ROI. The apparent diffusion coefficient (ADC) was calculated for the hepatic and splenic parenchyma and kidneys on diffusion-weighted MRI images. The normalized ADC for the liver was calculated as the ratio of the ADC for the hepatic parenchyma to the ADC for the splenic parenchyma. RESULTS Perfusion-weighted MRI variables differed among the 5 ROIs. Median ADC of the hepatic parenchyma was 1.38 × 10^-3 mm²/s, and mean ± SD normalized ADC for the liver was 1.86 ± 0.18. Median ADC of the renal cortex and renal medulla was 1.65 × 10^-3 mm²/s and 1.93 × 10^-3 mm²/s, respectively. CONCLUSIONS AND CLINICAL RELEVANCE Results provided preliminary baseline information about the diffusion and perfusion characteristics of structures in the cranial aspect of the abdomen of healthy adult male cats. Additional studies of cats of different sex and age groups as well as with and without cranial abdominal pathological conditions are necessary to validate and refine these findings.

Age-Related Changes in Tissue Value Properties in Children: Simultaneous Quantification of Relaxation Times and Proton Density Using Synthetic Magnetic Resonance Imaging

OBJECTIVES

The properties of brain tissue undergo dynamic changes during maturation. T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) are now simultaneously quantifiable within a clinically acceptable time, using a synthetic magnetic resonance imaging (MRI) sequence. This study aimed to provide age-specific reference values for T1, T2, and PD in children, using synthetic MRI.

MATERIALS AND METHODS

We included 89 children (median age, 18 months; range, 34 weeks of gestational age to 17 years) who underwent quantitative MRI, using a multidynamic, multiecho sequence on 3 T MRI, between December 2015 and November 2016, and had no abnormal MRI/neurologic assessment findings. T1, T2, and PD were simultaneously measured in each of the 22 defined white matter and gray matter regions of interest. The measured values were plotted against age, and a curve fitting model that best explained the age dependence of tissue values was identified. Age-specific regional tissue values were calculated using a fit equation.

RESULTS

The tissue values of all brain regions, except cortical PD, decreased with increasing age, and the robust negative association was best explained by modified biexponential model of the form Tissue values = T1 × exp (-C1 × age) + T2 × exp (-C2 × age). The quality of fit to the modified biexponential model was high in white matter and deep gray matter (white matter, R = 97%-99% [T1], 88%-95% [T2], 88%-97% [PD]; deep gray matter, R = 96%-97% [T1], 96% [T2], 49%-88% [PD]; cortex, 70%-83% [T1], 87%-90% [T2], 5%-27% [PD]). The white matter and deep gray matter changed the most dynamically within the first year of life.

CONCLUSIONS

Our study provides age-specific regional reference values, from the neonate to adolescent, of T1, T2, and PD, which could be objective tools for assessment of normal/abnormal brain development using synthetic MRI.

Functional MRI in transplanted kidneys

Renal transplantation is the therapy of choice for patients with end-stage renal diseases. Improvement of immunosuppressive therapy has significantly increased the half-life of renal allografts over the past decade. Nevertheless, complications can still arise. An early detection of allograft dysfunction is mandatory for a good outcome. New advances in magnetic resonance imaging (MRI) have enabled the noninvasive assessment of different functional renal parameters in addition to anatomic imaging. Most of these techniques were widely tested on renal allografts in past decades and a lot of clinical data are available. The following review summarizes the comprehensive, functional MRI techniques for the noninvasive assessment of renal allograft function and highlights their potential for the investigations of different etiologies of graft dysfunction.

Assessment of time-resolved renal diffusion parameters over the entire cardiac cycle

OBJECT

To assess changes diffusion properties of renal cortex over the entire cardiac cycle using electrocardiogram-gated respiratory-triggered dynamic diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

20 healthy volunteers were investigated on a 1.5 T MR scanner. Blood flow velocity within the renal arteries was determined by electrocardiogram-gated phase-contrast measurements. For dynamic renal DWI, an electrocardiogram-gated respiratory-triggered coronal single-slice EPI sequence was acquired at 14 times at 20, 70, 120, 170, …, 570, 620, 720 ms after the R-wave over the cardiac cycle. ROI measurements were performed by two authors in the renal cortex on apparent diffusion coefficient (ADC) maps. A pulsatility index was calculated for ADC as maximal percentage change. Five subjects were measured twice to assess scan-rescan reproducibility.

RESULTS

Flow measurements exhibited a minimum velocity of 15.7 ± 4.3 cm/s during the R-wave and a maximum of 43.2 ± 10.4 cm/s at 182.5 ± 48.3 ms after the R-wave. A minimal mean ADC of 2.19 ± 0.09 × 10^-3 mm²/s was observed during the R-wave. A maximum mean ADC of 2.85 ± 0.20 × 10^-3 mm²/s was measured 193 ± 57 ms after the R-wave. The mean ADC pulsatility index in the renal cortex was 29.9 ± 5.8%. ADC variation exhibited a significant correlation with pulsatile blood flow velocity. The scan-rescan reproducibility in this study had a low deviation of 0.3 ± 0.1%. The inter-reader reproducibility was 2.9 ± 0.6%.

CONCLUSION

Renal ADCs exhibit pulsatile characteristics. Due to the significant difference of systolic and diastolic ADCs, the pulsatility index can be calculated.

Diffusion-weighted magnetic resonance imaging to assess diffuse renal pathology: a systematic review and statement paper

Diffusion-weighted magnetic resonance imaging (DWI) is a non-invasive method sensitive to local water motion in the tissue. As a tool to probe the microstructure, including the presence and potentially the degree of renal fibrosis, DWI has the potential to become an effective imaging biomarker. The aim of this review is to discuss the current status of renal DWI in diffuse renal diseases. DWI biomarkers can be classified in the following three main categories: (i) the apparent diffusion coefficient-an overall measure of water diffusion and microcirculation in the tissue; (ii) true diffusion, pseudodiffusion and flowing fraction-providing separate information on diffusion and perfusion or tubular flow; and (iii) fractional anisotropy-measuring the microstructural orientation. An overview of human studies applying renal DWI in diffuse pathologies is given, demonstrating not only the feasibility and intra-study reproducibility of DWI but also highlighting the need for standardization of methods, additional validation and qualification. The current and future role of renal DWI in clinical practice is reviewed, emphasizing its potential as a surrogate and monitoring biomarker for interstitial fibrosis in chronic kidney disease, as well as a surrogate biomarker for the inflammation in acute kidney diseases that may impact patient selection for renal biopsy in acute graft rejection. As part of the international COST (European Cooperation in Science and Technology) action PARENCHIMA (Magnetic Resonance Imaging Biomarkers for Chronic Kidney Disease), aimed at eliminating the barriers to the clinical use of functional renal magnetic resonance imaging, this article provides practical recommendations for future design of clinical studies and the use of renal DWI in clinical practice.

Renal Adiposity Confounds Quantitative Assessment of Markers of Renal Diffusion With MRI: A Proposed Correction Method

OBJECTIVES

Recent studies have indicated that excessive fat may confound assessment of diffusion in organs with high fat content, such as the liver and breast. However, the extent of this effect in the kidney, which is not considered a major fat deposition site, remains unclear. This study tested the hypothesis that renal fat may impact diffusion-weighted imaging (DWI) parameters, and proposes a 3-compartment model (TCM) to circumvent this effect.

METHODS

Using computer simulations, we investigated the effect of fat on assessment of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and TCM-derived pure-diffusivity. We also investigated the influence of magnetic resonance repetition (TR) and echo time (TE) on DWI parameters as a result of variation in the relative contribution of the fat signal. Apparent diffusion coefficient, IVIM and TCM DWI parameters were calculated in domestic pigs fed a high-cholesterol (obese group) or normal diet (lean group), and correlated to renal histology. Intravoxel incoherent motion-derived pure-diffusivity was also compared among 15 essential hypertension patients classified by body mass index (BMI) (high vs normal). Finally, pure-diffusivity was calculated and compared in 8 patients with atherosclerotic renal artery stenosis (ARAS) and 5 healthy subjects using IVIM and TCM.

RESULTS

Simulations showed that unaccounted fat results in the underestimation of IVIM-derived pure diffusivity. The underestimation increases as the fat fraction increases, with higher pace at lower fat contents. The underestimation was larger for shorter TR and longer TE values due to the enhancement of the relative contribution of the fat signal. Moreover, TCM, which incorporates highly diffusion-weighted images (b > 2500 s/mm), could correct for fat-dependent underestimation. Animal studies in the lean and obese groups confirmed lower ADC and IVIM pure-diffusivity in obese versus lean pigs with otherwise healthy kidneys, whereas pure-diffusivity calculated using TCM were not different between the 2 groups. Similarly, essential hypertension patients with high BMI had lower ADC (1.9 vs 2.1 × 10 mm/s) and pure-diffusivity (1.7 vs 1.9 × 10 mm/s) than those with normal BMI. Pure-diffusivity calculated using IVIM was not different between the ARAS and healthy subjects, but TCM revealed significantly lower diffusivity in ARAS.

CONCLUSIONS

Excessive renal fat may cause underestimation of renal ADC and IVIM-derived pure-diffusivity, which may hinder detection of renal pathology. Models accounting for fat contribution may help reduce the variability of diffusivity calculated using DWI.

Performance of U-net based pyramidal lucas-kanade registration on free-breathing multi-b-value diffusion MRI of the kidney

OBJECTIVE

In free-breathing multi-b-value diffusion-weighted imaging (DWI), a series of images typically requires several minutes to collect. During respiration the kidney is routinely displaced and may also undergo deformation. These respiratory motion effects generate artifacts and these are the main sources of error in the quantification of intravoxel incoherent motion (IVIM) derived parameters. This work proposes a fully automated framework that combines a kidney segmentation to improve the registration accuracy.

METHODS

10 healthy subjects were recruited to participate in this experiment. For the segmentation, U-net was adopted to acquire the kidney's contour. The segmented kidney then served as a region of interest (ROI) for the registration method, known as pyramidal Lucas-Kanade. Our proposed framework confines the kidney's solution range, thus increasing the pyramidal Lucas-Kanade's accuracy. To demonstrate the feasibility of our presented framework, eight regions of interest were selected in the cortex and medulla, and data stability was estimated by comparing the normalized root-mean-square error (NRMSE) values of the fitted data from the bi-exponential intravoxel incoherent motion model pre- and post- registration.

RESULTS

The results show that the NRMSE was significantly lower after registration both in the cortex (p < 0.05) and medulla (p < 0.01) during free-breathing measurements. In addition, expert visual scoring of the derived apparent diffusion coefficient (ADC), f, D and D* maps indicated there were significant improvements in the alignment of the kidney in the post-registered image.

CONCLUSION

The proposed framework can effectively reduce the motion artifacts of misaligned multi-b-value DWIs and the inaccuracies of the ADC, f, D and D* estimations. Advances in knowledge: This study demonstrates the feasibility of our proposed fully automated framework combining U-net based segmentation and pyramidal Lucas-Kanade registration method for improving the alignment of multi-b-value diffusion-weighted MRIs and reducing the inaccuracy of parameter estimation during free-breathing.

Model selection for high b-value diffusion-weighted MRI of the prostate

PURPOSE

To assess the abilities of the standard mono-exponential (ME), bi-exponential (BE), diffusion kurtosis (DK) and stretched exponential (SE) models to characterize diffusion signal in malignant and prostatic tissues and determine which of the four models best characterizes these tissues on a per-voxel basis.

MATERIALS AND METHODS

This institutional-review-board-approved, HIPAA-compliant, retrospective study included 55 patients (median age, 61years; range, 42-77years) with untreated, biopsy-proven PCa who underwent endorectal coil MRI at 3-Tesla, diffusion-weighted MRI acquired at eight b-values from 0 to 2000s/mm². Estimated parameters were apparent diffusion coefficent (ME model); diffusion coefficients for the fast (D_fast) and slow (D_slow) components and fraction of fast component, f_fast (BE model); diffusion coefficient D, and kurtosis K (DK model); distributed diffusion coefficient DDC and α for (SE model). For one region-of-interest (ROI) in PZ and another in PCa in each patient, the corrected Akaike information criterion (AICc) and the Akaike weight (w) were calculated for each voxel.

RESULTS

Based on AICc and w, all non-monoexponential models outperformed the ME model in PZ and PCa. The DK model in PZ and SE model in PCa ROIs best fit the greatest average percentages of voxels (39% and 43%, respectively) and had the highest mean w (35±16×10^-2 and 41±22×10^-2, respectively).

CONCLUSION

DK and SE models best fit DWI data in PZ and PCa, and non-ME models consistently outperformed the ME model. Voxel-wise mapping of the preferential model demonstrated that the vast majority of voxels in either tissue type were best fit with one of the non-monoexponential models. At the given SNR levels, the maximum b-value of 2000s/mm² is not sufficiently high to identify the preferred non-monoexponential model.

Multiparametric Renal Magnetic Resonance Imaging: Validation, Interventions, and Alterations in Chronic Kidney Disease

Background: This paper outlines a multiparametric renal MRI acquisition and analysis protocol to allow non-invasive assessment of hemodynamics (renal artery blood flow and perfusion), oxygenation (BOLD T₂^*), and microstructure (diffusion, T₁ mapping). Methods: We use our multiparametric renal MRI protocol to provide (1) a comprehensive set of MRI parameters [renal artery and vein blood flow, perfusion, T₁, T₂^*, diffusion (ADC, D, D^*, f_p), and total kidney volume] in a large cohort of healthy participants (127 participants with mean age of 41 ± 19 years) and show the MR field strength (1.5 T vs. 3 T) dependence of T₁ and T₂^* relaxation times; (2) the repeatability of multiparametric MRI measures in 11 healthy participants; (3) changes in MRI measures in response to hypercapnic and hyperoxic modulations in six healthy participants; and (4) pilot data showing the application of the multiparametric protocol in 11 patients with Chronic Kidney Disease (CKD). Results: Baseline measures were in-line with literature values, and as expected, T₁-values were longer at 3 T compared with 1.5 T, with increased T₁ corticomedullary differentiation at 3 T. Conversely, T₂^* was longer at 1.5 T. Inter-scan coefficients of variation (CoVs) of T₁ mapping and ADC were very good at <2.9%. Intra class correlations (ICCs) were high for cortex perfusion (0.801), cortex and medulla T₁ (0.848 and 0.997 using SE-EPI), and renal artery flow (0.844). In response to hypercapnia, a decrease in cortex T₂^* was observed, whilst no significant effect of hyperoxia on T₂^* was found. In CKD patients, renal artery and vein blood flow, and renal perfusion was lower than for healthy participants. Renal cortex and medulla T₁ was significantly higher in CKD patients compared to healthy participants, with corticomedullary T₁ differentiation reduced in CKD patients compared to healthy participants. No significant difference was found in renal T₂^*. Conclusions: Multiparametric MRI is a powerful technique for the assessment of changes in structure, hemodynamics, and oxygenation in a single scan session. This protocol provides the potential to assess the pathophysiological mechanisms in various etiologies of renal disease, and to assess the efficacy of drug treatments.

Intravoxel incoherent motion modeling in the kidneys: Comparison of mono-, bi-, and triexponential fit

PURPOSE

To evaluate if a three-component model correctly describes the diffusion signal in the kidney and whether it can provide complementary anatomical or physiological information about the underlying tissue.

MATERIALS AND METHODS

Ten healthy volunteers were examined at 3T, with T2 -weighted imaging, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM). Diffusion tensor parameters (mean diffusivity [MD] and fractional anisotropy [FA]) were obtained by iterative weighted linear least squares fitting of the DTI data and mono-, bi-, and triexponential fit parameters (D1 , D2 , D3 , ffast2 , ffast3 , and finterm ) using a nonlinear fit of the IVIM data. Average parameters were calculated for three regions of interest (ROIs) (cortex, medulla, and rest) and from fiber tractography. Goodness of fit was assessed with adjusted R2 ( Radj2) and the Shapiro-Wilk test was used to test residuals for normality. Maps of diffusion parameters were also visually compared.

RESULTS

Fitting the diffusion signal was feasible for all models. The three-component model was best able to describe fast signal decay at low b values (b < 50), which was most apparent in Radj2 of the ROI containing high diffusion signals (ROIrest ), which was 0.42 ± 0.14, 0.61 ± 0.11, 0.77 ± 0.09, and 0.81 ± 0.08 for DTI, one-, two-, and three-component models, respectively, and in visual comparison of the fitted and measured S0 . None of the models showed significant differences (P > 0.05) between the diffusion constant of the medulla and cortex, whereas the ffast component of the two and three-component models were significantly different (P < 0.001).

CONCLUSION

Triexponential fitting is feasible for the diffusion signal in the kidney, and provides additional information.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:228-239.

Non-gaussian diffusion evaluation of the human kidney by Padé exponent model

PURPOSE

To evaluate the feasibility of renal diffusion quantification using the Padé exponent model (PEM) in healthy subjects.

MATERIALS AND METHODS

Diffusion measurements were completed in 10 healthy subjects (mean age, 32.4 ± 8.9 years) on a 3T MRI scanner (Magnetom Trio, Siemens AG, Germany). A respiratory-triggered echo planar imaging sequence (15 slices with 6 mm thickness; 16 b-values [0-750 s/mm² ]; three diffusion directions; field of view: 400 × 375 mm; Matrix 192 × 192; repetition time/echo time: 3000/74 ms) was acquired in the coronal direction. Parameter maps were calculated for the monoexponential, biexponential, kurtosis models, and the PEM. A regression analysis using an R² -test and corrected Akaike information criterion (AICc) was performed to identify the best mathematical fitting to the measured diffusion-weighted imaging signal decay.

RESULTS

The mathematical accuracy of the PEM was significantly higher than for the other three-parameter and the monoexponential model (P < 0.05), which enables more precise information about the deviation of the Gaussian behavior of the diffusion signal by the PEM. The biexponential model showed better fitting to the diffusion signal (medullar Rbi2 0.989 ± 0.008, AICc_bi 113.3 ± 6.6; cortical Rbi2 0.992 ± 0.006, AICc_bi 113.3 ± 5.2) than the three-parameter models (medullar RPadé2 0.965 ± 0.016, AICc_Padé 122.6 ± 6.4, RK2 0.954 ± 0.019, AICc_K 128.5 ± 6.0; cortical RPadé2 0.989 ± 0.005, AICc_Padé 116.3 ± 4.4, RK2 0.985 ± 0.007, AICc_K 120.4 ± 4.8). The monoexponential model fits least to the diffusion signal in the kidney (medullar Rmono2 0.898 ± 0.039, AICc_mono 141.4 ± 5.6; cortical Rmono2 0.961 ± 0.013, AICc_mono 135.4 ± 4.8).

CONCLUSION

The PEM is a novel promising approach to quantify diffusion properties in the human kidney and might further improve functional renal MR imaging.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:160-167.

Optimization of intra-voxel incoherent motion measurement in diffusion-weighted imaging of breast cancer

Purpose

The purpose of this study was to optimize intra‐voxel incoherent motion (IVIM) measurement in diffusion‐weighted imaging (DWI) of breast cancer by separating perfusion and diffusion effects through the determination of an optimal threshold b‐value, thus benign and cancerous breast tissues can be accurately differentiated using IVIM‐derived diffusion and perfusion parameters.

Materials and Methods

Twenty‐eight patients, with biopsy‐confirmed breast cancers, were studied with a 3T MRI scanner, using T1‐weighted dynamic contrast‐enhanced MRI images, and diffusion‐weighted images with nine b‐values, ranging from 0 to 1000 s/mm². IVIM‐derived parameter maps for tissue diffusion coefficients D, perfusion fraction f, and pseudo‐diffusion coefficients D* were computed using the segmented fitting method with optimized threshold b‐value, and the sum of squared residuals (SSR) were calculated for IVIM‐derived parameters in different breast lesions.

Results

The IVIM analysis method developed in this work can separate perfusion and diffusion effects with the optimal threshold b‐value of 300 s/mm², and the results of diffusion and perfusion parameters from IVIM analysis can be used to differentiate pathological changes in breast tissues. It was found that the averages and standard deviations of the diffusion and perfusion parameters, D, f, D*, are the following, for malignant, benign and normal breast tissues respectively: D (0.813 ± 0.225 × 10⁻³ mm²/s, 1.437 ± 0.538 × 10⁻³ mm²/s, 1.838 ± 0.213 × 10⁻³ mm²/s), f (10.73 ± 3.44%, 7.86 ± 3.70%, 8.92 ± 3.72%), D* (15.23 ± 12.17×10⁻³ mm²/s, 12.02 ± 3.19 × 10⁻³ mm²/s, 12.03 ± 7.21 × 10⁻³ mm²/s).

Conclusion

IVIM‐derived diffusion and perfusion parameter maps depend highly on the choice of threshold b‐value. Using the methodology developed in this work, and with the optimized threshold b‐value, the diffusion and perfusion parameters of breast tissues can be accurately assessed, making IVIM MRI a technique of choice for differential diagnosis of breast cancer.

Diffusion tensor imaging in abdominal organs

Initially, diffusion tensor imaging (DTI) was mainly applied in studies of the human brain to analyse white matter tracts. As DTI is outstanding for the analysis of tissue´s microstructure, the interest in DTI for the assessment of abdominal tissues has increased continuously in recent years. Tissue characteristics of abdominal organs differ substantially from those of the human brain. Further peculiarities such as respiratory motion and heterogenic tissue composition lead to difficult conditions that have to be overcome in DTI measurements. Thus MR measurement parameters have to be adapted for DTI in abdominal organs. This review article provides information on the technical background of DTI with a focus on abdominal imaging, as well as an overview of clinical studies and application of DTI in different abdominal regions. Copyright © 2015 John Wiley & Sons, Ltd.

Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors

BACKGROUND

The purpose of this study is to statistically assess whether bi-exponential intravoxel incoherent motion (IVIM) model better characterizes diffusion weighted imaging (DWI) signal of malignant breast tumor than mono-exponential Gaussian diffusion model.

METHODS

3 T DWI data of 29 malignant breast tumors were retrospectively included. Linear least-square mono-exponential fitting and segmented least-square bi-exponential fitting were used for apparent diffusion coefficient (ADC) and IVIM parameter quantification, respectively. F-test and Akaike Information Criterion (AIC) were used to statistically assess the preference of mono-exponential and bi-exponential model using region-of-interests (ROI)-averaged and voxel-wise analysis.

RESULTS

For ROI-averaged analysis, 15 tumors were significantly better fitted by bi-exponential function and 14 tumors exhibited mono-exponential behavior. The calculated ADC, D (true diffusion coefficient) and f (pseudo-diffusion fraction) showed no significant differences between mono-exponential and bi-exponential preferable tumors. Voxel-wise analysis revealed that 27 tumors contained more voxels exhibiting mono-exponential DWI decay while only 2 tumors presented more bi-exponential decay voxels. ADC was consistently and significantly larger than D for both ROI-averaged and voxel-wise analysis.

CONCLUSIONS

Although the presence of IVIM effect in malignant breast tumors could be suggested, statistical assessment shows that bi-exponential fitting does not necessarily better represent the DWI signal decay in breast cancer under clinically typical acquisition protocol and signal-to-noise ratio (SNR). Our study indicates the importance to statistically examine the breast cancer DWI signal characteristics in practice.

New Magnetic Resonance Imaging Index for Renal Fibrosis Assessment: A Comparison between Diffusion-Weighted Imaging and T1 Mapping with Histological Validation

A need exists to noninvasively assess renal interstitial fibrosis, a common process to all kidney diseases and predictive of renal prognosis. In this translational study, Magnetic Resonance Imaging (MRI) T1 mapping and a new segmented Diffusion-Weighted Imaging (DWI) technique, for Apparent Diffusion Coefficient (ADC), were first compared to renal fibrosis in two well-controlled animal models to assess detection limits. Validation against biopsy was then performed in 33 kidney allograft recipients (KARs). Predictive MRI indices, ΔT1 and ΔADC (defined as the cortico-medullary differences), were compared to histology. In rats, both T1 and ADC correlated well with fibrosis and inflammation showing a difference between normal and diseased kidneys. In KARs, MRI indices were not sensitive to interstitial inflammation. By contrast, ΔADC outperformed ΔT1 with a stronger negative correlation to fibrosis (R² = 0.64 against R² = 0.29 p < 0.001). ΔADC tends to negative values in KARs harboring cortical fibrosis of more than 40%. Using a discriminant analysis method, the ΔADC, as a marker to detect such level of fibrosis or higher, led to a specificity and sensitivity of 100% and 71%, respectively. This new index has potential for noninvasive assessment of fibrosis in the clinical setting.

Characterization of clear cell renal cell carcinoma with diffusion kurtosis imaging: correlation between diffusion kurtosis parameters and tumor cellularity

The aim of this study was to evaluate the role of diffusion kurtosis imaging (DKI) in the characterization of clear cell renal cell carcinoma (ccRCC) and to correlate DKI parameters with tumor cellularity. Fifty-nine patients with pathologically diagnosed ccRCCs were evaluated by DKI on a 3-T scanner. Regions of interest were drawn on the maps of the mean diffusion coefficient (MD) and mean diffusion kurtosis (MK). All ccRCCs were histologically graded according to the Fuhrman classification system. Tumor cellularity was measured by the nuclear-to-cytoplasm (N/C) ratio and the number of tumor cell nuclei (NTCN). ccRCCs were classified as grade 1 (n = 23), grade 2 (n = 24), grade 3 (n = 10) and grade 4 (n = 3). Both MD and MK could readily discriminate between normal renal parenchyma and ccRCCs (p < 0.001), and receiver operating characteristic (ROC) curve analysis showed that MK exhibited a better performance with an area under the ROC curve of 0.874 and sensitivity/specificity of 68.33%/100% (p < 0.001). Further, MD and MK were significantly different between grade 1 and grades 3 and 4 (p = 0.01, p < 0.001) and between grade 2 and grades 3 and 4 (p = 0.015, p < 0.005), respectively. However, no significant difference was found between grade 1 and grade 2 (p > 0.05) for both MD and MK. With regard to NTCN, no significant difference was found between any two grades (p > 0.05), and the N/C ratio changed significantly with grade (p < 0.01, between any two grades). Negative correlations were found between MK and MD (r = -0.56, p < 0.001), and between MD and N/C ratio (r = -0.36, p < 0.005), whereas MK and the N/C ratio were positively correlated (r = 0.45, p = 0.003). DKI could quantitatively characterize ccRCC with different grades by probing non-Gaussian diffusion properties related to changes in the tumor microenvironment or tissue complexities in the tumor. Copyright © 2016 John Wiley & Sons, Ltd.

Diffusion-weighted MR imaging of pancreatic cancer: A comparison of mono-exponential, bi-exponential and non-Gaussian kurtosis models

OBJECTIVES

To compare two Gaussian diffusion-weighted MRI (DWI) models including mono-exponential and bi-exponential, with the non-Gaussian kurtosis model in patients with pancreatic ductal adenocarcinoma.

MATERIALS AND METHODS

After written informed consent, 15 consecutive patients with pancreatic ductal adenocarcinoma underwent free-breathing DWI (1.5T, b-values: 0, 50, 150, 200, 300, 600 and 1000 s/mm²). Mean values of DWI-derived metrics ADC, D, D*, f, K and D_K were calculated from multiple regions of interest in all tumours and non-tumorous parenchyma and compared. Area under the curve was determined for all metrics.

RESULTS

Mean ADC and D_K showed significant differences between tumours and non-tumorous parenchyma (both P < 0.001). Area under the curve for ADC, D, D*, f, K, and D_K were 0.77, 0.52, 0.53, 0.62, 0.42, and 0.84, respectively.

CONCLUSION

ADC and D_K could differentiate tumours from non-tumorous parenchyma with the latter showing a higher diagnostic accuracy. Correction for kurtosis effects has the potential to increase the diagnostic accuracy of DWI in patients with pancreatic ductal adenocarcinoma.

Assessment of renal dysfunction with diffusion-weighted imaging: comparing intra-voxel incoherent motion (IVIM) with a mono-exponential model

BACKGROUND

Because renal dysfunction is a worldwide problem, accurate assessment is required for planning treatment and follow-up. Intra-voxel incoherent motion (IVIM) can isolate fast from slow water motion in well-perfused organs and may be helpful in assessing renal dysfunction.

PURPOSE

To determine the clinical value of IVIM in the assessment of renal dysfunction compared with a mono-exponential model.

MATERIAL AND METHODS

Fifty-four consecutive participants (mean age, 53.13 ± 13.96 years) were recruited into this study. The estimated glomerular filtration rate (eGFR) was calculated to classify the participants as having severe renal injury (sRI, eGFR ≤ 30 mL/min/1.73 m(2)) or not (non-sRI). DWI with seven b-factors was performed. Image analysis was performed by a radiologist to generate an apparent diffusion coefficient map (ADCmon) by mono-exponential model, diffusion coefficient (Dslow and Dfast), and fraction of fast diffusion (Ffast) maps by IVIM. The circular regions of interest were placed at the interface between the cortex and medulla for parameter measurements.

RESULTS

The ADCmon, Dslow, Dfast, and Ffast were less in sRI than non-sRI (P < 0.05). ADCmon and Dslow were positively related with eGFR (P < 0.05). For differentiating sRI from non-sRI, receiver operating characteristic curve indicated no significant difference between the two methods (P > 0.05). Furthermore, the correlation was 0.93 between ADCmon and Dslow, followed by 0.57 between Dfast and Ffast, 0.48 between ADCmon and Dfast, and 0.34 between ADCmon and Ffast (P < 0.05).

CONCLUSION

The IVIM model contributed little to improving the assessment of renal dysfunction compared with a mono-exponential model.

Stroke assessment with intravoxel incoherent motion diffusion-weighted MRI

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

Diffusion tensor imaging of the human kidney: Does image registration permit scanning without respiratory triggering?

PURPOSE

To investigate if image registration of diffusion tensor imaging (DTI) allows omitting respiratory triggering for both transplanted and native kidneys

MATERIALS AND METHODS

Nine kidney transplant recipients and eight healthy volunteers underwent renal DTI on a 3T scanner with and without respiratory triggering. DTI images were registered using a multimodal nonrigid registration algorithm. Apparent diffusion coefficient (ADC), the contribution of perfusion (FP ), and the fractional anisotropy (FA) were determined. Relative root mean square errors (RMSE) of the fitting and the standard deviations of the derived parameters within the regions of interest (SDROI ) were evaluated as quality criteria.

RESULTS

Registration significantly reduced RMSE in all DTI-derived parameters of triggered and nontriggered measurements in cortex and medulla of both transplanted and native kidneys (P < 0.05 for all). In addition, SDROI values were lower with registration for all 16 parameters in transplanted kidneys (14 of 16 SDROI values were significantly reduced, P < 0.04) and for 15 of 16 parameters in native kidneys (9 of 16 SDROI values were significantly reduced, P < 0.05). Comparing triggered versus nontriggered DTI in transplanted kidneys revealed no significant difference for RMSE (P > 0.14) and for SDROI (P > 0.13) of all parameters. In contrast, in native kidneys relative RMSE from triggered scans were significantly lower than those from nontriggered scans (P < 0.02), while SDROI was slightly higher in triggered compared to nontriggered measurements in 15 out of 16 comparisons (significantly for two, P < 0.05).

CONCLUSION

Registration improves the quality of DTI in native and transplanted kidneys. Diffusion parameters in renal allografts can be measured without respiratory triggering. In native kidneys, respiratory triggering appears advantageous. J. Magn. Reson. Imaging 2016;44:327-334.

Diffusion-Weighted imaging and diffusion tensor imaging detect delayed graft function and correlate with allograft fibrosis in patients early after kidney transplantation

PURPOSE

To combine diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) for detection of allograft dysfunction in patients early after kidney transplantation and to correlate diffusion parameters with renal function and renal histology of allograft biopsies.

MATERIALS AND METHODS

Between day 4 and 11 after kidney transplantation 33 patients with initial graft function and 31 patients with delayed graft function (DGF) were examined with a 1.5T magnetic resonance imaging (MRI) scanner. DTI and DWI sequences were acquired and fractional anisotropy (FA), apparent diffusion coefficient (ADCmono), pure diffusion (ADCdiff ), and the perfusion fraction (Fp) were calculated. Kidney biopsies in 26 patients were analyzed for allograft pathology, ie, acute tubular injury, inflammation, edema, renal fibrosis, and rejection. Histological results were correlated with MRI parameters.

RESULTS

In the renal medulla FA (0.25 ± 0.06 vs. 0.29 ± 0.06, P < 0.01) and ADCmono (1.73 ± 0.13*10(-3) vs. 1.93 ± 0.16*10(-3) mm(2) /s, P < 0.001) were significantly reduced in DGF patients compared with patients with initial function. For ADCdiff and Fp similar reductions were observed. FA and ADCmono significantly correlated with renal function (r = 0.53 and r = 0.57, P < 0.001) and were inversely correlated with the amount of renal fibrosis (r = -0.63 and r = -0.65, P < 0.05).

CONCLUSION

Combined DTI and DWI detected allograft dysfunction early after kidney transplantation and correlated with allograft fibrosis. J. Magn. Reson. Imaging 2016;44:112-121.

[Functional magnetic resonance imaging of the kidneys]

Interest in functional renal magnetic resonance imaging (MRI) has significantly increased in recent years. This review article provides an overview of the most important functional imaging techniques and their potential clinical applications for assessment of native and transplanted kidneys, with special emphasis on the clarification of renal tumors.

Whole-body diffusion kurtosis imaging: initial experience on non-Gaussian diffusion in various organs

INTRODUCTION

Diffusion kurtosis imaging (DKI) is based on a non-Gaussian diffusion model that should inherently better account for restricted water diffusion within the complex microstructure of most tissues than the conventional diffusion-weighted imaging (DWI), which presumes Gaussian distributed water molecule displacement probability. The aim of this investigation was to test the technical feasibility of in vivo whole-body DKI, probe for organ-specific differences, and compare whole-body DKI and DWI results.

MATERIALS AND METHODS

Eight healthy subjects underwent whole-body DWI on a clinical 3.0 T magnetic resonance imaging system. Echo-planar images in the axial orientation were acquired at b-values of 0, 150, 300, 500, and 800 mm²/s. Parametrical whole-body maps of the diffusion coefficient (D), the kurtosis (K), and the traditional apparent diffusion coefficient (ADC) were generated. Goodness of fit was compared between DKI and DWI fits using the sums of squared residuals. Data groups were tested for significant differences of the mean by paired Student t tests.

RESULTS

Good-quality parametrical whole-body maps of D, K, and ADC could be computed. Compared with ADC values, D values were significantly higher in the cerebral gray matter (by 30%) and white matter (27%), renal cortex (23%) and medulla (21%), spleen (101%), as well as erector spinae muscle (34%) (each P value <0.001). No significant differences between D and ADC were found in the cerebrospinal fluid (P = 0.08) and in the liver (P = 0.13). Curves of DKI fitted the measurement points significantly better than DWI curves did in most organs.

CONCLUSIONS

Whole-body DKI is technically feasible and may reflect tissue microstructure more meaningfully than whole-body DWI.

Impact of the calculation algorithm on biexponential fitting of diffusion-weighted MRI in upper abdominal organs

PURPOSE

To compare the variability, precision, and accuracy of six different algorithms (Levenberg-Marquardt, Trust-Region, Fixed-Dp , Segmented-Unconstrained, Segmented-Constrained, and Bayesian-Probability) for computing intravoxel-incoherent-motion-related parameters in upper abdominal organs.

METHODS

Following the acquisition of abdominal diffusion-weighted magnetic resonance images of 10 healthy men, six distinct algorithms were employed to compute intravoxel-incoherent-motion-related parameters in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla. Algorithms were evaluated regarding inter-reader and intersubject variability. Comparability of results was assessed by analyses of variance. The algorithms' precision and accuracy were investigated on simulated data.

RESULTS

A Bayesian-Probability based approach was associated with very low inter-reader variability (average Intraclass Correlation Coefficients: 96.5-99.6%), the lowest inter-subject variability (Coefficients of Variation [CV] for the pure diffusion coefficient Dt : 3.8% in the renal medulla, 6.6% in the renal cortex, 10.4-12.1% in the left and right liver lobe, 15.3% in the spleen, 15.8% in the pancreas; for the perfusion fraction Fp : 15.5% on average; for the pseudodiffusion coefficient Dp : 25.8% on average), and the highest precision and accuracy. Results differed significantly (P < 0.05) across algorithms in all anatomical regions.

CONCLUSION

The Bayesian-Probability algorithm should be preferred when computing intravoxel-incoherent-motion-related parameters in upper abdominal organs.

Evaluation of different mathematical models for diffusion-weighted imaging of normal prostate and prostate cancer using high b-values: a repeatability study

PURPOSE

To evaluate monoexponential, stretched exponential, kurtosis, and biexponential models for diffusion-weighted imaging (DWI) of normal prostate and prostate cancer (PCa), using b-values up to 2000 s/mm(2) , in terms of fitting quality and repeatability.

METHODS

Eight healthy volunteers and 16 PCa patients underwent a total of four repeated 3T DWI examinations using 16 and 12 b-values, respectively. The highest b-value was 2000 s/mm(2) . The normalized mean signal intensities of regions of interest, placed in normal tissue and PCa using anatomical images and prostatectomy sections, were fitted using the four models. The fitting quality was evaluated using Akaike information criteria and F-ratio. Repeatability of the fitted parameters was evaluated using intraclass correlation coefficient ICC(3,1).

RESULTS

The biexponential model provided the best fit to normal prostate and PCa DWI data. The parameters of the monoexponential, kurtosis, and stretched exponential (with the exception of the α parameter) models had higher ICC(3,1) values compared with the biexponential model. The kurtosis model provided a better fit to DWI data of normal prostate and PCa than the monoexponential model, whereas these models had comparable reliability and repeatability based on ICC(3,1) values.

CONCLUSION

Considering the model fit and repeatability, the kurtosis model seems to be the preferred model for characterization of normal prostate and PCa DWI using b-values up to 2000 s/mm(2) .