Compartmental modelling for magnetic resonance renography

Quantitative renal magnetic resonance imaging: magnetic resonance urography

The goal of functional renal imaging is to identify and quantitate irreversible renal damage and nephron loss, as well as potentially reversible hemodynamic changes. MR urography has evolved into a comprehensive evaluation of the urinary tract that combines anatomical imaging with functional evaluation in a single test without ionizing radiation. Quantitative functional MR imaging is based on dynamic contrast-enhanced MR acquisitions that provide progressive, visible enhancement of the renal parenchyma and urinary tract. The signal changes related to perfusion, concentration and excretion of the contrast agent can be evaluated using both quantitative and qualitative measures. Functional evaluation with MR has continued to improve as a result of significant technical advances allowing for faster image acquisition as well as the development of new tracer kinetic models of renal function. The most common indications for MR urography in children are the evaluation of congenital anomalies of the kidney and urinary tract including hydronephrosis and renal malformations, and the identification of ectopic ureters in children with incontinence. In this paper, we review the underlying acquisition schemes and techniques used to generate quantitative functional parameters including the differential renal function (DRF), asymmetry index, mean transit time (MTT), signal intensity versus time curves as well as the calculation of individual kidney glomerular filtration rate (GFR). Visual inspection and semi-quantitative assessment using the renal transit time (RTT) and calyceal transit times (CTT) are fundamental to accurate diagnosis and are used as a basis for the interpretation of the quantitative data. The importance of visual assessment of the images cannot be overstated when analyzing the quantitative measures of renal function.

An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited

Background

Perfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology.

Results

Five compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft’s model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters.

Conclusion

We developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control.

Functional imaging of acute kidney injury at 3 Tesla: investigating multiple parameters using DCE-MRI and a two-compartment filtration model

OBJECT

To investigate how MR-based parameters reflect functional changes in kidneys with acute kidney injury (AKI) using dynamic contrast enhanced MRI and a two-compartment renal filtration model.

MATERIALS AND METHODS

MRI data of eight male Lewis rats were analyzed retrospectively. Five animals were subjected to AKI, three native rats served as control. All animals underwent perfusion imaging by dynamic contrast-enhanced MRI. Renal blood volume, glomerular filtration rate (GFR) as well as plasma and tubular mean transit times were estimated from regions-of-interest drawn in the renal cortex. Differences between healthy kidneys and kidneys subjected to AKI were analyzed using a paired t-test.

RESULTS

Significant differences between ischemic and healthy kidneys could only be detected for the glomerular filtration rate. For all other calculated parameters, differences were present, however not significant. In rats with AKI, average single kidney GFR was 0.66 ± 0.37 ml/min for contralateral and 0.26 ± 0.12 ml/ min for diseased kidneys (P = 0.0254). For the healthy control group, the average GFR was 0.39 ± 0.06 ml/min and 0.41 ± 0.11 ml/min, respectively. Differences between diseased kidneys of AKI rats and ipsilateral kidneys of the healthy control group were significant (P = 0.0381).

CONCLUSION

Significant differences of functional parameters reflecting damage of the renal tissue of kidneys with AKI compared to the contralateral, healthy kidneys could only be detected by GFR. GFR might be a useful parameter that allows for a spatially resolved detection of abnormal changes of renal tissue by AKI.

Renal perfusion in acute kidney injury with DCE-MRI: deconvolution analysis versus two-compartment filtration model

PURPOSE

To investigate the results of different pharmacokinetic models of a quantitative analysis of renal blood flow (RBF) in acute kidney injury using deconvolution analysis and a two-compartment renal filtration model.

MATERIALS AND METHODS

MRI data of ten male Lewis rats were analyzed retrospectively. Six animals were subjected to unilateral acute kidney injury and underwent perfusion imaging by dynamic contrast-enhanced MRI (DCE-MRI). Renal blood flow was estimated from regions-of-interest depicting the cortex in the DCE-MRI perfusion maps. The perfusion models were compared by a paired t-test and Bland-Altman plots.

RESULTS

No significant difference was found between the two compartment model and the deconvolution analysis (P=0.2807). Differences between healthy and diseased kidney in the AKI model were significant for both methods (P<0.05). A Bland-Altman plot showed no systematic errors, and values were equally distributed around the mean difference between the methods lying within the range of 1.96 standard deviations.

CONCLUSION

Both quantification strategies could detect the kidneys that were impaired by AKI. When just aiming at RBF as a marker, a deconvolution analysis can provide similar values as the 2CFM. If functional parameters beyond RBF like glomerular filtration rate are needed, the 2CFM should be employed.

Estimate of FDG excretion by means of compartmental analysis and ant colony optimization of nuclear medicine data

[¹⁸F]fluoro-2-deoxy-D-glucose (FDG) is one of the most utilized tracers for positron emission tomography (PET) applications in oncology. FDG-PET relies on higher glycolytic activity in tumors compared to normal structures as the basis of image contrast. As a glucose analog, FDG is transported into malignant cells which typically exhibit an increased radioactivity. However, different from glucose, FDG is not reabsorbed by the renal system and is excreted to the bladder. The present paper describes a novel computational method for the quantitative assessment of this excretion process. The method is based on a compartmental analysis of FDG-PET data in which the excretion process is explicitly accounted for by the bladder compartment and on the application of an ant colony optimization (ACO) algorithm for the determination of the tracer coefficients describing the FDG transport effectiveness. The validation of this approach is performed by means of both synthetic data and real measurements acquired by a PET device for small animals (micro-PET). Possible oncological applications of the results are discussed in the final section.

UMMPerfusion: an open source software tool towards quantitative MRI perfusion analysis in clinical routine

To develop a generic Open Source MRI perfusion analysis tool for quantitative parameter mapping to be used in a clinical workflow and methods for quality management of perfusion data. We implemented a classic, pixel-by-pixel deconvolution approach to quantify T1-weighted contrast-enhanced dynamic MR imaging (DCE-MRI) perfusion data as an OsiriX plug-in. It features parallel computing capabilities and an automated reporting scheme for quality management. Furthermore, by our implementation design, it could be easily extendable to other perfusion algorithms. Obtained results are saved as DICOM objects and directly added to the patient study. The plug-in was evaluated on ten MR perfusion data sets of the prostate and a calibration data set by comparing obtained parametric maps (plasma flow, volume of distribution, and mean transit time) to a widely used reference implementation in IDL. For all data, parametric maps could be calculated and the plug-in worked correctly and stable. On average, a deviation of 0.032 ± 0.02 ml/100 ml/min for the plasma flow, 0.004 ± 0.0007 ml/100 ml for the volume of distribution, and 0.037 ± 0.03 s for the mean transit time between our implementation and a reference implementation was observed. By using computer hardware with eight CPU cores, calculation time could be reduced by a factor of 2.5. We developed successfully an Open Source OsiriX plug-in for T1-DCE-MRI perfusion analysis in a routine quality managed clinical environment. Using model-free deconvolution, it allows for perfusion analysis in various clinical applications. By our plug-in, information about measured physiological processes can be obtained and transferred into clinical practice.

Prediction and assessment of responses to renal artery revascularization with dynamic contrast-enhanced magnetic resonance imaging: a pilot study

The aim of this study was to assess the potential of dynamic contrast-enhanced (DCE) MRI to predict and evaluate functional outcomes after renal artery revascularization for renal artery stenosis (RAS). The single-kidney glomerular filtration rate (SK-GFR) was measured in 15 patients with atherosclerotic RAS with DCE-MRI and radioisotopes at baseline and 4 mo after revascularization. DCE-MRI also produced measurements of blood flow, blood volume, extraction fraction, tubular transit time, and functional volume. Stented kidneys (n = 22) were divided into three response groups on the basis of the changes in radioisotope SK-GFR: improved (n = 5), stable (n = 13), and deteriorated (n = 4). A good agreement was found between SK-GFR values from DCE-MRI and radioisotopes (correlation coefficient: 0.91). Before intervention, kidneys that improved had lower extraction fraction, higher blood volume, longer tubular transit time, and lower SK-GFR. After intervention, improved kidneys had increased functional volume, and deteriorated kidneys had reduced functional volume and extraction fraction. Revascularization improved blood flow and blood volume in all groups. This pilot study led to the hypothesis that well-vascularized kidneys with reduced extraction fractions are most likely to benefit from revascularization. More generally, DCE-MRI has the potential to replace radioisotope measurement of SK-GFR and may improve patient management by providing additional information on tissue perfusion.

DCE-MRI of the human kidney using BLADE: a feasibility study in healthy volunteers

PURPOSE

To evaluate the degree of motion compensation in the kidney using two different sampling methods, each in their optimized settings: A BLADE k-space acquisition technique and a routinely used kidney perfusion acquisition scheme (TurboFLASH).

MATERIALS AND METHODS

Dynamic contrast enhanced magnetic resonance examinations were performed in 16 healthy volunteers on a 3 Tesla MR-system with two parameterizations of the BLADE sequence and the standard reference acquisition scheme. Signal intensity enhanced time curves were analyzed with a mathematical model and a widely published separable compartment model on cortex regions to assess robustness versus motion artifacts.

RESULTS

BLADE-measurements with a strip-width of 32 lines constituted the smallest mean values for the sum of squared errors (6065 ± 4996) compared with the measurement with a strip-width of 64 lines (13849 ± 14079) or the standard TurboFLASH (11884 ± 8076). Calculations concerning goodness of the fit of the applied compartment model yielded an overall average of the Akaike Fit Error of 732 ± 141 for BLADE (646 ± 149 for a strip-width of 32 lines, 816 ± 53 for 64 lines) and 1626 ± 303 for the TurboFLASH (TFL) sequence.

CONCLUSION

We demonstrated that renal dynamic contrast enhanced magnetic resonance imaging using BLADE k-space sampling with a strip-width of 32 is significantly less sensitive to motion than a widely published Turbo-Flash sequence with nearly similar parameters.

Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney: initial results in patients and healthy volunteers

Renal diseases can lead to kidney failure that requires life-long dialysis or renal transplantation. Early detection and treatment can prevent progression towards end stage renal disease. MRI has evolved into a standard examination for the assessment of the renal morphology and function. We propose a wavelet-based clustering to group the voxel time courses and thereby, to segment the renal compartments. This approach comprises (1) a nonparametric, discrete wavelet transform of the voxel time course, (2) thresholding of the wavelet coefficients using Stein's Unbiased Risk estimator, and (3) k-means clustering of the wavelet coefficients to segment the kidneys. Our method was applied to 3D dynamic contrast enhanced (DCE-) MRI data sets of human kidney in four healthy volunteers and three patients. On average, the renal cortex in the healthy volunteers could be segmented at 88%, the medulla at 91%, and the pelvis at 98% accuracy. In the patient data, with aberrant voxel time courses, the segmentation was also feasible with good results for the kidney compartments. In conclusion wavelet based clustering of DCE-MRI of kidney is feasible and a valuable tool towards automated perfusion and glomerular filtration rate quantification.

[Examination of self-navigating MR-sequences for perfusion imaging of the kidneys]

Due to the worldwide increasing number of cases of chronic kidney diseases renal imaging - as a non-invasive technique in magnetic resonance imaging - has become a very important tool for an early diagnosis of probable insufficiencies and malfunction. Especially, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides a technique to derive physiological parameters like renal blood flow or glomerular filtration rate. Similar to the entire field of abdominal imaging, the major problems are motion artifacts that primarily arise from the patient's respiration. The self-navigating BLADE-sequence with a post processing motion correction is an approach that does not require breath holding and is therefore also easily applicable to patients who are not able to undergo multiple breath hold examinations. In this work, a T(1)-weighted BLADE-sequence was optimized to demonstrate the feasibility of this technique to perfusion imaging. The number of phase-encoding lines of one BLADE has a direct impact on the reduction of motion artifacts. In comparison to standard DCE-MRI sequences, the developed BLADE-sequence with optimized number of phase encoding lines could significantly reduce motion artifacts. A quantitative analysis revealed that up to a 50% displacement of the kidneys could be corrected. Therefore, it was demonstrated that dynamic motion corrected measurements without the need of a breath hold-technique are feasible.