The effects of cryoablation on renal cell carcinoma perfusion and glomerular filtration rate measured using dynamic contrast-enhanced MRI: a feasibility study.

AIM: To assess the effect of cryoablation on renal cell carcinoma (RCC) perfusion and single kidney (SK) glomerular filtration rate (GFR) using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). MATERIALS AND METHODS: Eighteen patients undergoing percutaneous cryoablation of a solitary RCC between August 2010 and November 2011 were evaluated with DCE-MRI immediately before and 1 month post-cryoablation. DCE-MRI data were acquired with 2 s temporal resolution in a coronal plane during the first pass of a 0.1 mmol/kg bolus dose of Gd-DOTA. Perfusion of the RCC (in ml/min/100 ml tissue) was estimated using a maximum slope technique. An index of SK GFR (SK-GFRi) was assessed using data acquired every 30 s for the following 3 min in the axial plane and analysed using Rutland-Patlak plots. This was compared to the GFR estimated by creatinine clearance (eGFR). RESULTS: Perfusion in the zone of ablation decreased significantly (p<0.001) from a mean of 98.0 ± 37.5 ml/min/100 ml pre-cryoablation to 11.6 ± 4.1 ml/min/100 ml post-cryoablation; a mean decrease of 88.2%. Functional analysis was performed in seventeen patients. eGFR was underestimated by SK-GFRi which decreased significantly in tumour-bearing (-31.7%, p = 0.011), but not in contralateral kidneys (-4.4%, p = 0.14). CONCLUSION: It is feasible to measure RCC perfusion pre- and post-cryoablation using DCE-MRI. The significant decrease within the zone of ablation suggests that this technique may be useful for assessment of treatment response. Further work is required to address the underestimation of eGFR by SK-GFRi and to validate the perfusion findings.

Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability.

The tracer-kinetic models developed in the early 1990s for dynamic contrast-enhanced MRI (DCE-MRI) have since become a standard in numerous applications. At the same time, the development of MRI hardware has led to increases in image quality and temporal resolution that reveal the limitations of the early models. This in turn has stimulated an interest in the development and application of a second generation of modelling approaches. They are designed to overcome these limitations and produce additional and more accurate information on tissue status. In particular, models of the second generation enable separate estimates of perfusion and capillary permeability rather than a single parameter K(trans) that represents a combination of the two. A variety of such models has been proposed in the literature, and development in the field has been constrained by a lack of transparency regarding terminology, notations and physiological assumptions. In this review, we provide an overview of these models in a manner that is both physically intuitive and mathematically rigourous. All are derived from common first principles, using concepts and notations from general tracer-kinetic theory. Explicit links to their historical origins are included to allow for a transfer of experience obtained in other fields (PET, SPECT, CT). A classification is presented that reveals the links between all models, and with the models of the first generation. Detailed formulae for all solutions are provided to facilitate implementation. Our aim is to encourage the application of these tools to DCE-MRI by offering researchers a clearer understanding of their assumptions and requirements.

MR first pass perfusion of benign and malignant cardiac tumours-significant differences and diagnostic accuracy.

OBJECTIVES: To determine the diagnostic value of magnetic resonance (MR) first pass perfusion in the differentiation of benign and malignant cardiac tumours. METHODS: 24 patients with cardiac tumours (11 malignant, histopathological correlation present in all cases) were examined using MRI. In addition to morphological sequences a saturation-recovery T1w-GRE technique was implemented for tumour perfusion. The maximum relative signal enhancement (RSE[%]) and the slope of the RSE(t)-curve (slopeRSE[%/s]) of tumour tissue were assessed. A t-test was used to identify significant differences between benign and malignant tumours. Sensitivities and specificities were calculated for detection of malignant lesions and were compared with the sensitivity and specificity based on solely morphological image assessment. RESULTS: The RSE and slopeRSE of malignant cardiac tumours were significantly higher compared with benign lesions (p < 0.001 and p < 0.001). The calculated sensitivities and specificities of RSE and slopeRSE for identification of malignant lesions were 100% and 84.6% and 100% and 92.3%, respectively with cut-off values of 80% and 6%/s. The sensitivity and specificity for identification of malignant lesions on the basis of morphological imaging alone were 90.9% and 69.2%. CONCLUSIONS: With first pass perfusion, malignant cardiac masses can be identified with higher sensitivity and specificity compared with morphological image assessment alone.

Error estimation for perfusion parameters obtained using the two-compartment exchange model in dynamic contrast-enhanced MRI: a simulation study.

In theory, the application of the two-compartment exchange model (2CXM) to data from a dynamic contrast-enhanced (DCE) MRI exam allows the estimation of the plasma flow, plasma volume, extraction flow and extravascular-extracellular volume. The aim of this paper was to explore whether simulations based on the 2CXM could provide useful information on the trustworthiness of the results. The deviations from the input values of the haemodynamic quantities were estimated for a 'reference tissue' with a clear bi-phasic response and four 'limit tissues' with more challenging 2CXM fitting properties. The impact of the instrumental factors sampling step (T(s)), acquisition window (T(acq)) and contrast-to-noise ratio (CNR) was investigated. Each factor was varied separately, while keeping the other ones at a value above concern. Measurement guidelines to ensure that all deviations fell within a predefined range (±20%) could not be derived, but simulations for fixed T(s) and T(acq) were found to provide a practical tool for studying the error behaviour to be expected from a given experimental set-up and for comparing measurement protocols. At the level of an individual DCE exam, a bootstrap version of the simulation approach was shown to lead to a useful estimate of the errors on the fitted parameters.

Bolus-tracking MRI with a simultaneous T1- and T2*-measurement.

The aim of this study was to propose and evaluate a methodology to analyze simultaneously acquired T2*-weighted dynamic susceptibility contrast (DSC) MRI and T(1)-weighted dynamic contrast enhanced (DCE) MRI data. Two generalized models of T2*-relaxation are proposed to account for tracer leakage, and a two-compartment exchange model is used to separate tracer in intra- and extravascular spaces. The methods are evaluated using data extracted from ROIs in three mice with subcutaneously implanted human colorectal tumors. Comparing plasma flow values obtained from DCE-MRI and DSC-MRI data defines a practical experimental paradigm to measure T2*-relaxivities, and reveals a factor of 15 between values in tissue and blood. Comparing mean transit time values obtained from DCE-MRI and DSC-MRI without leakage correction, indicates a significant reduction of susceptibility weighting in DSC-MRI during tracer leakage. A one-parameter gradient correction model provides a good approximation for this susceptibility loss, but redundancy of the parameter limits the practical potential of this model for DSC-MRI. Susceptibility loss is modeled more accurately with a variable T2*-relaxivity, which allows to extract new parameters that cannot be derived from DSC-MRI or DCE-MRI alone. They reflect the cellular and vessel geometry, and thus may lead to a more complete characterization of tissue structure.

MR-based semi-automated quantification of renal functional parameters with a two-compartment model--an interobserver analysis.

PURPOSE: To assess the interobserver agreement in a semi-automated quantification approach of MR-renal perfusion and filtration parameters with a two-compartment model analysis. MATERIALS AND METHODS: Twelve consecutive patients underwent renal perfusion measurements after intravenous injection of 7 ml Gd-BOPTA at 4 ml/s at 3.0 T. Two independent observers placed two regions of interest (ROI) manually on the axial slice, one in the abdominal aorta to determine the arterial input function (AIF), and one at the tissue-air interface for retrospective triggering. The data were fitted on a pixel-by-pixel basis to the two-compartment model, producing maps of the perfusion parameters FP (plasma flow), TP (plasma mean transit time) and of the tubular filtration parameters FT (tubular flow) and TT (tubular mean transit time). A cortical ROI was segmented by selecting those pixels with plasma volume VP>10 ml/100 ml, and the model fit was repeated on a ROI basis to produce the cortical averages. RESULTS: The average values (observer 1/observer 2) were FP (226.2/187.3 ml/100 ml/min), TP (9.0/9.1s), FT (23.5/20.8 ml/100 ml/min), TT (142.1/140.0 s). The correlation coefficients between both observers were 0.90 (FP), 0.80 (TP), 0.80 (FT), 0.78 (TT). Correlations of all values were significant (p<0.05). A paired t-test yielded significant differences for FP (p=0.004). DISCUSSION/CONCLUSION: The data demonstrate a significant systematic difference for the parameter FP, while TP seems to be most stable. Further decrease of the residual variability of all parameters seems desirable to improve the robustness of the method for clinical routine.

Deconvolution of bolus-tracking data: a comparison of discretization methods.

Model-free measurement of perfusion from bolus-tracking data requires a discretization of the tracer kinetic model. In this study a classification is provided of existing approaches to discretization, and the accuracy of these methods is compared. Two methods are included which are delay invariant (circulant and time shift) and three methods which are not (volterra, singular and hybrid). Simulations of magnetic resonance imaging (MRI) in the brain are performed for two tissue types (plug flow and compartment) with variable delay and dispersion times, temporal resolution and signal to noise. Simulations were compared to measurements in a patient data set. Both delay-invariant methods are equally accurate, but the circulant method is sensitive to data truncation. Overall volterra produces highest estimates of perfusion, followed by hybrid, singular and delay-invariant methods. Volterra is most accurate except in plug-flow without delay or dispersion, which represents an unrealistic tissue type. Differences between methods vanish when delay or dispersion times increase above the temporal resolution. It is concluded that when negative delays cannot be avoided or when an accurate estimate of left-right perfusion ratios is required, the time shift is the method of choice. When delays are certain to be positive and absolute accuracy is the objective, the volterra method is to be preferred.

Quantification of perfusion and permeability in breast tumors with a deconvolution-based analysis of second-bolus T1-DCE data.

PURPOSE: To test the feasibility of using a second-bolus injection, added to a routine breast MRI examination, to measure regional perfusion and permeability in human breast tumors. MATERIALS AND METHODS: In 30 patients with breast tumors, first a routine whole-breast T1-DCE sequence was applied, and the slice where the lesion enhanced maximally was located. At that slice position, T1-weighted MR images were acquired at 0.3-second resolution using a second-bolus dynamic inversion recovery (IR)-prepared turbo field echo (TFE) sequence. A pixel-by-pixel model-independent deconvolution of the relative signal enhancement was performed to estimate the tumor blood flow (TBF), tumor volume of distribution (TVD), mean transit time (MTT), extraction flow product (EF), and extraction fraction (E). In addition to this pilot study, a first appraisal of its sensitivity to tissue type was made on the basis of a small patient cohort. RESULTS: In the malignant tumors, the parametric maps clearly delineated tumors from the breast tissue and enabled visualization of the heterogeneity. The deconvolution analysis provided objective parametric maps of tumor perfusion with a mean TBF (84 +/- 48 mL/100 mL/minute) in malignant tumors in the high range of literature values. CONCLUSION: In terms of these perfusion values, our method appears promising to quantitatively characterize tumor pathophysiology. However, the number of patients was limited, and the separation between malignant and benign groups was not clear-cut. Additional parameters generated through compartment modeling may improve the tumor differentiation.

Pixel-by-pixel deconvolution of bolus-tracking data: optimization and implementation.

Quantification of haemodynamic parameters with a deconvolution analysis of bolus-tracking data is an ill-posed problem which requires regularization. In a previous study, simulated data without structural errors were used to validate two methods for a pixel-by-pixel analysis: standard-form Tikhonov regularization with either the L-curve criterion (LCC) or generalized cross validation (GCV) for selecting the regularization parameter. However, problems of image artefacts were reported when the methods were applied to patient data. The aim of this study was to investigate the nature of these problems in more detail and evaluate strategies of optimization for routine application in the clinic. In addition we investigated to which extent the calculation time of the algorithm can be minimized. In order to ensure that the conclusions are relevant for a larger range of clinical applications, we relied on patient data for evaluation of the algorithms. Simulated data were used to validate the conclusions in a more quantitative manner. We conclude that the reported problems with image quality can be removed by appropriate optimization of either LCC or GCV. In all examples this could be achieved with LCC without significant perturbation of the values in pixels where the regularization parameter was originally selected accurately. GCV could not be optimized for the renal data, and in the CT data only at the cost of image resolution. Using the implementations given, calculation times were sufficiently short for routine application in the clinic.

Quantification of renal perfusion and function on a voxel-by-voxel basis: a feasibility study.

The feasibility of a voxel-by-voxel deconvolution analysis of T(1)-weighted DCE data in the human kidney and its potential for obtaining quantification of perfusion and filtration was investigated. Measurements were performed on 14 normal humans and 1 transplant at 1.5 T using a Turboflash sequence. Signal time-courses were converted to tracer concentrations and deconvolved with an aorta AIF. Parametric maps of relative renal blood flow (rRBF), relative renal volume of distribution (rRVD), relative mean transit time (rMTT), and whole cortex extraction fraction (E) were obtained from the impulse response functions. For the normals average cortical rRBF, rRVD, rMTT, and E were 1.6 mL/min/mL (SD 0.8), 0.4 mL/mL (SD 0.1), 17s (SD 7), and 22.6% (SD 6.1), respectively. A gradual voxelwise rRBF increase is found from the center of two infarction zones toward the edges. Voxel IRFs showed more detail on the nefron substructure than ROI IRFs. In conclusion, quantitative voxelwise perfusion mapping based on deconvolved T(1)-DCE renal data is feasible, but absolute quantification requires inflow correction. rRBF maps and quantitative values are sufficiently sensitive to detect perfusion abnormality in pathologic areas, but further research is necessary to separate perfusion from extraction and to characterize the different compartments of the nephron on the (sub)voxel level.

Choice of the regularization parameter for perfusion quantification with MRI.

Truncated singular value decomposition (TSVD) is an effective method for the deconvolution of dynamic contrast enhanced (DCE) MRI. Two robust methods for the selection of the truncation threshold on a pixel-by-pixel basis--generalized cross validation (GCV) and the L-curve criterion (LCC)--were optimized and compared to paradigms in the literature. GCV and LCC were found to perform optimally when applied with a smooth version of TSVD, known as standard form Tikhonov regularization (SFTR). The methods lead to improvements in the estimate of the residue function and of its maximum, and converge properly with SNR. The oscillations typically observed in the solution vanish entirely, and perfusion is more accurately estimated at small mean transit times. This results in improved image contrast and increased sensitivity to perfusion abnormalities, at the cost of 1-2 min in calculation time and hyperintense clusters in the image. Preliminary experience with clinical data suggests that the latter problem can be resolved using spatial continuity and/or hybrid thresholding methods. In the simulations GCV and LCC are equivalent in terms of performance, but GCV thresholding is faster.

Diffusion and perfusion MRI: basic physics.

Diffusion and perfusion MR imaging are now being used increasingly in neuro-vascular clinical applications. While diffusion weighted magnetic resonance imaging exploits the translational mobility of water molecules to obtain information on the microscopic behaviour of the tissues (presence of macromolecules, presence and permeability of membranes, equilibrium intracellular-extracellular water, ellipsis), perfusion weighted imaging makes use of endogenous and exogenous tracers for monitoring their hemodynamic status. The combination of both techniques is extremely promising for the early detection and assessment of stroke, for tumor characterisation and for the evaluation of neurodegenerative diseases. This article provides a brief review of the basic physics principles underlying the methodologies followed.