Jeff L. Zhang

Optimization of b-value sampling for diffusion-weighted imaging of the kidney

Diffusion‐weighted imaging (DWI) involves data acquisitions at multiple b values. In this paper, we presented a method of selecting the b values that maximize estimation precision of the biexponential analysis of renal DWI data. We developed an error propagation factor for the biexponential model, and proposed to optimize the b‐value samplings by minimizing the error propagation factor. A prospective study of four healthy human subjects (eight kidneys) was done to verify the feasibility of the proposed protocol and to assess the validity of predicted precision for DWI measures, followed by Monte Carlo simulations of DWI signals based on acquired data from renal lesions of 16 subjects. In healthy subjects, the proposed methods improved precision (P = 0.003) and accuracy (P < 0.001) significantly in region‐of‐interest based biexponential analysis. In Monte Carlo simulation of renal lesions, the b‐sampling optimization lowered estimation error by at least 20–30% compared with uniformly distributed b values, and improved the differentiation between malignant and benign lesions significantly. In conclusion, the proposed method has the potential of maximizing the precision and accuracy of the biexponential analysis of renal DWI. Magn Reson Med, 2011.

Diffusion-weighted intravoxel incoherent motion imaging of renal tumors with histopathologic correlation

PurposeThe aim of this study was to use intravoxel incoherent motion diffusion-weighted imaging to discriminate subtypes of renal neoplasms and to assess agreement between intravoxel incoherent motion (perfusion fraction, fp) and dynamic contrast-enhanced magnetic resonance imaging (MRI) metrics of tumor vascularity. Subjects and MethodsIn this Health Insurance Portability and Accountability Act–compliant, institutional review board–approved prospective study, 26 patients were imaged at 1.5-T MRI using dynamic contrast-enhanced MRI with high temporal resolution and diffusion-weighted imaging using 8 b values (range, 0-800 s/mm2). Perfusion fraction (fp), tissue diffusivity (Dt), and pseudodiffusivity (Dp) were calculated using biexponential fitting of the diffusion data. Apparent diffusion coefficient (ADC) was calculated with monoexponential fit using 3 b values of 0, 400, and 800 s/mm2. Dynamic contrast-enhanced data were processed with a semiquantitative method to generate model-free parameter cumulative initial area under the curve of gadolinium concentration at 60 seconds (CIAUC60). Perfusion fraction, Dt, Dp, ADC, and CIAUC60 were compared between different subtypes of renal lesions. Perfusion fraction was correlated with CIAUC60. ResultsWe examined 14 clear cell, 4 papillary, 5 chromophobe, and 3 cystic renal cell carcinomas (RCCs). Although fp had higher accuracy (area under the curve, 0.74) for a diagnosis of clear cell RCC compared with Dt or ADC, the combination of fp and Dt had the highest accuracy (area under the curve, 0.78). The combination of fp and Dt diagnosed papillary RCC and cystic RCC with 100% accuracy, and clear cell RCC and chromophobe RCC, with 86.5% accuracy. There was significant strong correlation between fp and CIAUC60 (r = 0.82; P < 0.001). ConclusionIntravoxel incoherent motion parameters fp and Dt can discriminate renal tumor subtypes. Perfusion fraction demonstrates good correlation with CIAUC60 and can assess degree of tumor vascularity without the use of exogenous contrast agent.

Estimates of glomerular filtration rate from MR renography and tracer kinetic models.

To compare six methods for calculating the single‐kidney glomerular filtration rate (GFR) from T1‐weighted magnetic resonance (MR) renography (MRR) against reference radionuclide measurements.

Functional assessment of the kidney from magnetic resonance and computed tomography renography: Impulse retention approach to a multicompartment model

A three‐compartment model is proposed for analyzing magnetic resonance renography (MRR) and computed tomography renography (CTR) data to derive clinically useful parameters such as glomerular filtration rate (GFR) and renal plasma flow (RPF). The model fits the convolution of the measured input and the predefined impulse retention functions to the measured tissue curves. A MRR study of 10 patients showed that relative root mean square errors by the model were significantly lower than errors for a previously reported three‐compartmental model (11.6% ± 4.9 vs 15.5% ± 4.1; P < 0.001). GFR estimates correlated well with reference values by 99mTc‐DTPA scintigraphy (correlation coefficient r = 0.82), and for RPF, r = 0.80. Parameter‐sensitivity analysis and Monte Carlo simulation indicated that model parameters could be reliably identified. When the model was applied to CTR in five pigs, expected increases in RPF and GFR due to acetylcholine were detected with greater consistency than with the previous model. These results support the reliability and validity of the new model in computing GFR, RPF, and renal mean transit times from MR and CT data. Magn Reson Med 59:278–288, 2008.

Comparison of fitting methods and b‐value sampling strategies for intravoxel incoherent motion in breast cancer

To compare fitting methods and sampling strategies, including the implementation of an optimized b‐value selection for improved estimation of intravoxel incoherent motion (IVIM) parameters in breast cancer.

Assessment of Renal Function with Dynamic Contrast Enhanced MR Imaging

MR imaging is a promising noninvasive modality that can provide a comprehensive picture of renal anatomy and function in a single examination. The advantages of MR imaging are its high contrast and temporal resolution and lack of exposure to ionizing radiation. In the past few years, considerable progress has been made in development of methods of renal functional MR imaging and their applications in various diseases. This article reviews the key factors for acquisition and analysis of dynamic contrast-enhanced renal MR imaging (MR renography) and the most significant developments in this field over the past few years.

New magnetic resonance imaging methods in nephrology

Established as a method to study anatomic changes, such as renal tumors or atherosclerotic vascular disease, magnetic resonance imaging (MRI) to interrogate renal function has only recently begun to come of age. In this review, we briefly introduce some of the most important MRI techniques for renal functional imaging, and then review current findings on their use for diagnosis and monitoring of major kidney diseases. Specific applications include renovascular disease, diabetic nephropathy, renal transplants, renal masses, acute kidney injury and pediatric anomalies. With this review, we hope to encourage more collaboration between nephrologists and radiologists to accelerate the development and application of modern MRI tools in nephrology clinics.

Kidney Function: Glomerular Filtration Rate Measurement with MR Renography in Patients with Cirrhosis

PURPOSE To assess the accuracy of glomerular filtration rate (GFR) measurements obtained with low-contrast agent dose dynamic contrast material-enhanced magnetic resonance (MR) renography in patients with liver cirrhosis who underwent routine liver MR imaging, with urinary clearance of technetium 99m ((99m)Tc) pentetic acid (DTPA) as the reference standard. MATERIALS AND METHODS This HIPAA-compliant study was institutional review board approved. Written informed patient consent was obtained. Twenty patients with cirrhosis (14 men, six women; age range, 41-70 years; mean age, 54.6 years) who were scheduled for routine 1.5-T liver MR examinations to screen for hepatocellular carcinoma during a 6-month period were prospectively included. Five-minute MR renography with a 3-mL dose of gadoteridol was performed instead of a routine test-dose timing examination. The GFR was estimated at MR imaging with use of two kinetic models. In one model, only the signal intensities in the aorta and kidney parenchyma were considered, and in the other, renal cortical and medullary signal intensities were treated separately. The GFR was also calculated by using serum creatinine levels according to the Cockcroft-Gault and modification of diet in renal disease (MDRD) formulas. All patients underwent a (99m)Tc-DTPA urinary clearance examination on the same day to obtain a reference GFR measurement. The accuracies of all MR- and creatinine-based GFR estimations were compared by using Wilcoxon signed rank tests. RESULTS The mean reference GFR, based on (99m)Tc-DTPA clearance, was 74.9 mL/min/1.73 m(2) ± 27.7 (standard deviation) (range, 10.3-120.7 mL/min/1.73 m(2)). With both kinetic models, 95% of MR-based GFRs were within 30% of the reference values, whereas only 40% and 60% of Cockcroft-Gault- and MDRD-based GFRs, respectively, were within this range. MR-based GFR estimates were significantly more accurate than creatinine level-based estimates (P < .001). CONCLUSION GFR assessment with MR imaging, which outperformed the Cockcroft-Gault and MDRD formulas, adds less than 10 minutes of table time to a clinically indicated liver MR examination without ionizing radiation. SUPPLEMENTAL MATERIAL http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101338/-/DC1.

Functional MRI of the kidneys

Renal function is characterized by different physiologic aspects, including perfusion, glomerular filtration, interstitial diffusion, and tissue oxygenation. Magnetic resonance imaging (MRI) shows great promise in assessing these renal tissue characteristics noninvasively. The last decade has witnessed a dramatic progress in MRI techniques for renal function assessment. This article briefly describes relevant renal anatomy and physiology, reviews the applications of functional MRI techniques for the diagnosis of renal diseases, and lists unresolved issues that will require future work. J. Magn. Reson. Imaging 2013;37:282–293.

Use of cardiac output to improve measurement of input function in quantitative dynamic contrast-enhanced MRI.

To validate a new method for converting MR arterial signal intensity versus time curves to arterial input functions (AIFs).