Blind deconvolution in dynamic contrast-enhanced MRI and ultrasound

Spatially regularized estimation of the tissue homogeneity model parameters in DCE-MRI using proximal minimization

PURPOSE

The Tofts and the extended Tofts models are the pharmacokinetic models commonly used in dynamic contrast-enhanced MRI (DCE-MRI) perfusion analysis, although they do not provide two important biological markers, namely, the plasma flow and the permeability-surface area product. Estimates of such markers are possible using advanced pharmacokinetic models describing the vascular distribution phase, such as the tissue homogeneity model. However, the disadvantage of the advanced models lies in biased and uncertain estimates, especially when the estimates are computed voxelwise. The goal of this work is to improve the reliability of the estimates by including information from neighboring voxels.

THEORY AND METHODS

Information from the neighboring voxels is incorporated in the estimation process through spatial regularization in the form of total variation. The spatial regularization is applied on five maps of perfusion parameters estimated using the tissue homogeneity model. Since the total variation is not differentiable, two proximal techniques of convex optimization are used to solve the problem numerically.

RESULTS

The proposed algorithm helps to reduce noise in the estimated perfusion-parameter maps together with improving accuracy of the estimates. These conclusions are proved using a numerical phantom. In addition, experiments on real data show improved spatial consistency and readability of perfusion maps without considerable lowering of the quality of fit.

CONCLUSION

The reliability of the DCE-MRI perfusion analysis using the tissue homogeneity model can be improved by employing spatial regularization. The proposed utilization of modern optimization techniques implies only slightly higher computational costs compared to the standard approach without spatial regularization.

Interobserver Variation of the Bolus-and-Burst Method for Pancreatic Perfusion with Dynamic - Contrast-Enhanced Ultrasound

PURPOSE

Dynamic contrast-enhanced ultrasound (DCE-US) can be used for calculating organ perfusion. By combining bolus injection with burst replenishment, the actual mean transit time (MTT) can be estimated. Blood volume (BV) can be obtained by scaling the data to a vessel on the imaging plane. The study aim was to test interobserver agreement for repeated recordings using the same ultrasound scanner and agreement between results on two different scanner systems.

MATERIALS AND METHODS

Ten patients under evaluation for exocrine pancreatic failure were included. Each patient was scanned two times on a GE Logiq E9 scanner, by two different observers, and once on a Philips IU22 scanner, after a bolus of 1.5 ml Sonovue. A 60-second recording of contrast enhancement was performed before the burst and the scan continued for another 30 s for reperfusion. We performed data analysis using MATLAB-based DCE-US software. An artery in the same depth as the region of interest (ROI) was used for scaling. The measurements were compared using the intraclass correlation coefficient (ICC) and Bland Altman plots.

RESULTS

The interobserver agreement on the Logiq E9 for MTT (ICC=0.83, confidence interval (CI) 0.46-0.96) was excellent. There was poor agreement for MTT between the Logiq E9 and the IU22 (ICC=-0.084, CI -0.68-0.58). The interobserver agreement for blood volume measurements was excellent on the Logiq E9 (ICC=0.9286, CI 0.7250-0.98) and between scanners (ICC=0.86, CI=0.50-0.97).

CONCLUSION

Interobserver agreement was excellent using the same scanner for both parameters and between scanners for BV, but the comparison between two scanners did not yield acceptable agreement for MTT. This was probably due to incomplete bursting of bubbles in some of the recordings on the IU22.

Quantification of tumor perfusion using dynamic contrast-enhanced ultrasound: impact of mathematical modeling

Dynamic contrast-enhanced ultrasound has been proposed to monitor tumor therapy, as a complement to volume measurements. To assess the variability of perfusion parameters in ideal conditions, four consecutive test-retest studies were acquired in a mouse tumor model, using controlled injections. The impact of mathematical modeling on parameter variability was then investigated. Coefficients of variation (CV) of tissue blood volume (BV) and tissue blood flow (BF) based-parameters were estimated inside 32 sub-regions of the tumors, comparing the log-normal (LN) model with a one-compartment model fed by an arterial input function (AIF) and improved by the introduction of a time delay parameter. Relative perfusion parameters were also estimated by normalization of the LN parameters and normalization of the one-compartment parameters estimated with the AIF, using a reference tissue (RT) region. A direct estimation (rRTd) of relative parameters, based on the one-compartment model without using the AIF, was also obtained by using the kinetics inside the RT region. Results of test-retest studies show that absolute regional parameters have high CV, whatever the approach, with median values of about 30% for BV, and 40% for BF. The positive impact of normalization was established, showing a coherent estimation of relative parameters, with reduced CV (about 20% for BV and 30% for BF using the rRTd approach). These values were significantly lower (p  <  0.05) than the CV of absolute parameters. The rRTd approach provided the smallest CV and should be preferred for estimating relative perfusion parameters.

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI

PURPOSE

One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used.

METHODS

Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested.

RESULTS

The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates.

CONCLUSION

Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup.