Impact of the arterial input function on microvascularization parameter measurements using dynamic contrast-enhanced ultrasonography

A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an in silico model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the in silico spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models.

Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI

This paper aims to solve the arterial input function (AIF) determination in dynamic contrast-enhanced MRI (DCE-MRI), an important linear ill-posed inverse problem, using the maximum entropy technique (MET) and regularization functionals. In addition, estimating the pharmacokinetic parameters from a DCE-MR image investigations is an urgent need to obtain the precise information about the AIF-the concentration of the contrast agent on the left ventricular blood pool measured over time. For this reason, the main idea is to show how to find a unique solution of linear system of equations generally in the form of [Formula: see text] named an ill-conditioned linear system of equations after discretization of the integral equations, which appear in different tomographic image restoration and reconstruction issues. Here, a new algorithm is described to estimate an appropriate probability distribution function for AIF according to the MET and regularization functionals for the contrast agent concentration when applying Bayesian estimation approach to estimate two different pharmacokinetic parameters. Moreover, by using the proposed approach when analyzing simulated and real datasets of the breast tumors according to pharmacokinetic factors, it indicates that using Bayesian inference-that infer the uncertainties of the computed solutions, and specific knowledge of the noise and errors-combined with the regularization functional of the maximum entropy problem, improved the convergence behavior and led to more consistent morphological and functional statistics and results. Finally, in comparison to the proposed exponential distribution based on MET and Newton's method, or Weibull distribution via the MET and teaching-learning-based optimization (MET/TLBO) in the previous studies, the family of Gamma and Erlang distributions estimated by the new algorithm are more appropriate and robust AIFs.

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis

Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.

Study of the reliability of quantification methods of dynamic contrast-enhanced ultrasonography: numerical modeling of blood flow in tumor microvascularization

Dynamic contrast-enhanced ultrasonography is a recent functional dynamic imaging technique that allows evaluation of the efficacy of anti-angiogenic treatments by quantifying changes in specific parameters of the tumor vasculature. Preclinical and clinical experimental studies now reveal the existence of sources of variability in the quantitative methods. In order to study the reliability of quantification methods (both semi-quantitative and quantitative), we have developed the first numerical model of blood flow and contrast agents in vascular networks with computational fluid dynamics Fluent software version 15.0 (ANSYS, France). We studied four vascular networks (1.84  ×  10^-3, 2.28  ×  10^-3, 2.4  ×  10^-3 and 2.54  ×  10^-3 ml) and four blood velocities (0.01, 0.02, 0.03 and 0.05 m s^-1). For variations in tumor vascular volume the quantitative method is more sensitive, with variations of parameter perfusion of 25.7%, in contrast to variations of the semi-quantitative parameters between 14.9 and 19.5%. For changes in blood velocity the semi-quantitative method is more sensitive, with variation of the area under the enhancement curve (64%), the maximum of the enhancement curve (60%), and the slope of the enhancement curve (73%). The transit time parameters from the two quantitative methods were weakly sensitive to both blood volume and blood flow variations. This study is hopeful and may be extended to the treatment of more complex vascular networks, to approach clinical conditions, and to the evaluation of quantification methods in contrast imaging.

Toward a Standardization of Ultrasound Scanners for Dynamic Contrast-Enhanced Ultrasonography: Methodology and Phantoms

The standardization of ultrasound scanners for dynamic contrast-enhanced ultrasonography (DCE-US) is mandatory for evaluation of clinical multicenter studies. We propose a robust method using a phantom for measuring the variation of the harmonic signal intensity obtained from the area under the time-intensity curve versus various contrast-agent concentrations. The slope of this measured curve is the calibration parameter. We tested our method on two devices from the same manufacturer (AplioXV and Aplio500, Toshiba, Tokyo, Japan) using the same settings as defined for a French multicenter study. The Aplio500's settings were adjusted to match the slopes of the AplioXV, resulting in the following settings on the Aplio500: at 3.5 MHz: MI = 0.15; CG = 35 dB and at 8 MHz: MI = 0.10; CG = 32 dB. This calibration method is very important for future DCE-US multicenter studies.

Reproducibility of Contrast-Enhanced Ultrasound in Mice with Controlled Injection

PURPOSE

Sensitivity of contrast-enhanced ultrasound (CEUS) to microvascular flow modifications can be limited by intra-injection variability (injected dose, rate, volume).

PROCEDURES

To evaluate the effect of injection variability on microvascular flow evaluation, CEUS was compared between controlled and manual injections where enhancement was assessed in vitro within a flow phantom, in normal murine kidney (N = 12) and in murine ectopic tumors (N = 10).

RESULTS

For both in vitro and in vivo measurements in the renal cortex, controlled injections significantly improved reproducibility of functional parameter estimation. Their coefficient of variation (CV) in the renal cortex ranged from 4 to 19 % for controlled injection vs. 5 to 43 % for manual injections. For measurements in tumors, controlled injection only decreased the CV significantly for the mean transit time. In tumors, multiple injections of contrast agent with a 15-min delay between each were shown to strongly modify contrast uptake by facilitating penetration of microbubbles.

CONCLUSION

Improved reproducibility of CEUS assessments in murine models should provide more robust quantification of flow parameters and more sensitive evaluation of tumor modifications in therapeutic models.

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

BACKGROUND

In the estimation of physiological kinetic parameters from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) data, the determination of the arterial input function (AIF) plays a key role.

OBJECTIVES

This paper proposes a Bayesian method to estimate the physiological parameters of DCE-MRI along with the AIF in situations, where no measurement of the AIF is available.

METHODS

In the proposed algorithm, the maximum entropy method (MEM) is combined with the maximum a posterior approach (MAP). To this end, MEM is used to specify a prior probability distribution of the unknown AIF. The ability of this method to estimate the AIF is validated using the Kullback-Leibler divergence. Subsequently, the kinetic parameters can be estimated with MAP. The proposed algorithm is evaluated with a data set from a breast cancer MRI study.

RESULTS

The application shows that the AIF can reliably be determined from the DCE-MRI data using MEM. Kinetic parameters can be estimated subsequently.

CONCLUSIONS

The maximum entropy method is a powerful tool to reconstructing images from many types of data. This method is useful for generating the probability distribution based on given information. The proposed method gives an alternative way to assess the input function from the existing data. The proposed method allows a good fit of the data and therefore a better estimation of the kinetic parameters. In the end, this allows for a more reliable use of DCE-MRI.

A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors

In the last few years, several imaging methods, such as magnetic resonance imaging (MRI) and computed tomography, have been used to investigate the degree of blood-brain barrier (BBB) permeability in patients with neurological diseases including multiple sclerosis, ischemic stroke, and brain tumors. One promising MRI method for assessing the BBB permeability of patients with neurological diseases in vivo is T1-weighted dynamic contrast-enhanced (DCE)-MRI. Here we review the technical issues involved in DCE-MRI in the study of human brain tumors. In the first part of this paper, theoretical models for the DCE-MRI analysis will be described, including the Toft-Kety models, the adiabatic approximation to the tissue homogeneity model and the two-compartment exchange model. These models can be used to estimate important kinetic parameters related to BBB permeability. In the second part of this paper, details of the data acquisition, issues related to the arterial input function, and procedures for DCE-MRI image analysis are illustrated.