Radovan Jiřík

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

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.

Semi-automatic motion compensation of contrast-enhanced ultrasound images from abdominal organs for perfusion analysis

This paper presents a system for correcting motion influences in time-dependent 2D contrast-enhanced ultrasound (CEUS) images to assess tissue perfusion characteristics. The system consists of a semi-automatic frame selection method to find images with out-of-plane motion as well as a method for automatic motion compensation. Translational and non-rigid motion compensation is applied by introducing a temporal continuity assumption. A study consisting of 40 clinical datasets was conducted to compare the perfusion with simulated perfusion using pharmacokinetic modeling. Overall, the proposed approach decreased the mean average difference between the measured perfusion and the pharmacokinetic model estimation. It was non-inferior for three out of four patient cohorts to a manual approach and reduced the analysis time by 41% compared to manual processing.

Blind deconvolution in dynamic contrast-enhanced MRI and ultrasound

This paper is focused on quantitative perfusion analysis using MRI and ultrasound. In both MRI and ultrasound, most approaches allow estimation of rate constants (Ktrans, kep for MRI) and indices (AUC, TTP) that are only related to the physiological perfusion parameters of a tissue (e.g. blood flow, vessel permeability) but do not allow their absolute quantification. Recent methods for quantification of these physiological perfusion parameters are shortly reviewed. The main problem of these methods is estimation of the arterial input function (AIF). This paper summarizes and extends the current blind-deconvolution approaches to AIF estimation. The feasibility of these methods is shown on a small preclinical study using both MRI and ultrasound.

The precision of DCE-MRI using the tissue homogeneity model with continuous formulation of the perfusion parameters

The present trend in dynamic contrast-enhanced MRI is to increase the number of estimated perfusion parameters using complex pharmacokinetic models. However, less attention is given to the precision analysis of the parameter estimates. In this paper, the distributed capillary adiabatic tissue homogeneity pharmacokinetic model is extended by the bolus arrival time formulated as a free continuous parameter. With the continuous formulation of all perfusion parameters, it is possible to use standard gradient-based optimization algorithms in the approximation of the tissue concentration time sequences. This new six-parameter model is investigated by comparing Monte-Carlo simulations with theoretically derived covariance matrices. The covariance-matrix approach is extended from the usual analysis of the primary perfusion parameters of the pharmacokinetic model to the analysis of the perfusion parameters derived from the primary ones. The results indicate that the precision of the estimated perfusion parameters can be described by the covariance matrix for signal-to-noise ratio higher than~20dB. The application of the new analysis model on a real DCE-MRI data set is also presented.

Contrast-enhanced ultrasonography of the pancreas shows impaired perfusion in pancreas insufficient cystic fibrosis patients

BackgroundPerfusion assessment of the pancreas is challenging and poorly evaluated. Pancreatic affection is a prevalent feature of cystic fibrosis (CF). Little is known about pancreatic perfusion in CF. We aimed to assess pancreatic perfusion by contrast-enhanced ultrasound (CEUS) analysed in the bolus-and-burst model and software.MethodsWe performed contrast enhanced ultrasound of the pancreas in 25 CF patients and 20 healthy controls. Perfusion data was analysed using a dedicated perfusion model providing the mean capillary transit-time (MTT), blood flow (BF) and blood-volume (BV). CF patients were divided according to exocrine function.ResultsThe pancreas insufficient CF patients had longer MTT (p ≤ 0.002), lower BF (p < 0.001) and lower BV (p < 0.05) compared to the healthy controls and sufficient CF patients. Interrater analysis showed substantial agreement for the analysis of mean transit time.ConclusionThe bolus-and-burst method used on pancreatic CEUS-examinations demonstrates reduced perfusion in CF patients with pancreas affection. The perfusion model and software requires further optimization and standardization to be clinical applicable for the assessment of pancreatic perfusion.

Iterative Methods for Fast Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI Data

This paper introduces new variational formulation for reconstruction from subsampled dynamic contrast-enhanced DCE-MRI data, that combines a data-driven approach using estimated temporal basis and total variation regularization (PCA TV). We also experimentally compares the performance of such model with two other state-of-the-art formulations. One models the shape of perfusion curves in time as a sum of a curve belonging to a low-dimensional space and a function sparse in a suitable domain (L + S model). The other possibility is to regularize both spatial and time domains (ICTGV). We are dealing with the specific situation of the DCE-MRI acquisition with a 9.4T small animal scanner, working with noisier signals than human scanners and with a smaller number of coil elements that can be used for parallel acquisition and small voxels. Evaluation of the selected methods is done through subsampled reconstruction of radially-sampled DCE-MRI data. Our analysis shows that compressed sensed MRI in the form of regularization can be used to increase the temporal resolution of acquisition while keeping a sufficient signal-to-noise ratio.

Time-Efficient Fourier Domain Evaluation of Pharmacokinetic Model in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Dynamic contrast-enhanced magnetic resonance imaging obtains information about tissue perfusion and permeability. Following the administration of a contrast agent, concentration-time curves measured in each voxel are fitted by a pharmacokinetic model formulated as a time-domain convolution of an arterial input function (AIF) and an impulse residue function (IRF). Since the measurement window contains hundreds of time samples, the discrete convolution is demanding, even when it is performed via discrete Fourier transform (DFT). Additionally, its discretization causes convergence complications in the curve fitting and it is not applicable to functions without a closed-form expression in the time domain, e.g. tissue homogeneity model IRF. Both issues can be solved by formulating the functions in a closed form in the Fourier domain. In the Fourier domain, the model transforms to multiplication of IRF and AIF, followed by the inverse DFT. To avoid time-domain aliasing, the number of samples in the Fourier domain must be higher than the sum of supports of the functions in the time domain. If the functions are slowly decaying exponentials, the support is theoretically infinite, which dramatically reduces the computational performance. In this contribution, we propose a modification of IRF in the Fourier domain to consider the measurement window. Our solution reduces the required number of samples to three times the measurement window compared to dozens needed without the modification and reduces the number of DFTs. This provides faster evaluation of the pharmacokinetic model and its derivatives for each voxel in each iteration of the curve fitting.

Semi-parametric arterial input functions for quantitative dynamic contrast enhanced magnetic resonance imaging in mice

OBJECTIVE An extension of single- and multi-channel blind deconvolution is presented to improve the estimation of the arterial input function (AIF) in quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). METHODS The Lucy-Richardson expectation-maximization algorithm is used to obtain estimates of the AIF and the tissue residue function (TRF). In the first part of the algorithm, nonparametric estimates of the AIF and TRF are obtained. In the second part, the decaying part of the AIF is approximated by three decaying exponential functions with the same delay, giving an almost noise free semi-parametric AIF. Simultaneously, the TRF is approximated using the adiabatic approximation of the Johnson-Wilson (aaJW) pharmacokinetic model. RESULTS In simulations and tests on real data, use of this AIF gave perfusion values close to those obtained with the corresponding previously published nonparametric AIF, and are more noise robust. CONCLUSION When used subsequently in voxelwise perfusion analysis, these semi-parametric AIFs should give more correct perfusion analysis maps less affected by recording noise than the corresponding nonparametric AIFs, and AIFs obtained from arteries. SIGNIFICANCE This paper presents a method to increase the noise robustness in the estimation of the perfusion parameter values in DCE-MRI.