Cerebral perfusion mapping using a robust and efficient method for deconvolution analysis of dynamic contrast-enhanced images

Contrasted central effects of n-3 versus n-6 diets on brain functions in diet-induced obesity in minipigs

INTRODUCTION

N3 polyunsaturated fatty acids (n-3 PUFAs) exert anti-inflammatory effects for the hypothalamus, but their extra-hypothalamic outcome lack documentation. We evaluated the central consequences of the substitution of saturated fatty acids with n-3 or n-6 PUFA in obesogenic diets.

METHODS

Twenty-one miniature pigs were fed ad libitum obesogenic diets enriched in fat provided either as lard, fish oil (source for n-3 PUFAs), or sunflower oil (source for n-6 PUFAs) for ten weeks. The blood-brain barrier (BBB) permeability was quantified by CT perfusion. Central autonomic network was evaluated using heart rate variability, and PET 18FDG was performed to assess brain metabolism.

RESULTS

BBB permeability was higher in lard group, but heart rate variability changed only in fish oil group. Brain connectivity analysis and voxel-based comparisons show regional differences between groups except for the cingulate cortex in fish oil vs. sunflower oil groups.

DISCUSSION

: The minute changes in brain metabolism in obese pigs feed with fish oil compared with saturated fatty acids were sufficient to induce detrimental changes in heart rate variability. On the contrary, the BBB's decreased permeability in n-3 and n-6 PUFAs groups was protective against an obesity-driven damaged BBB.

Modeling of the contrast-enhanced perfusion test in liver based on the multi-compartment flow in porous media

The paper deals with modeling the liver perfusion intended to improve quantitative analysis of the tissue scans provided by the contrast-enhanced computed tomography (CT). For this purpose, we developed a model of dynamic transport of the contrast fluid through the hierarchies of the perfusion trees. Conceptually, computed time-space distributions of the so-called tissue density can be compared with the measured data obtained from CT; such a modeling feedback can be used for model parameter identification. The blood flow is characterized at several scales for which different models are used. Flows in upper hierarchies represented by larger branching vessels are described using simple 1D models based on the Bernoulli equation extended by correction terms to respect the local pressure losses. To describe flows in smaller vessels and in the tissue parenchyma, we propose a 3D continuum model of porous medium defined in terms of hierarchically matched compartments characterized by hydraulic permeabilities. The 1D models corresponding to the portal and hepatic veins are coupled with the 3D model through point sources, or sinks. The contrast fluid saturation is governed by transport equations adapted for the 1D and 3D flow models. The complex perfusion model has been implemented using the finite element and finite volume methods. We report numerical examples computed for anatomically relevant geometries of the liver organ and of the principal vascular trees. The simulated tissue density corresponding to the CT examination output reflects a pathology modeled as a localized permeability deficiency.

Tissue perfusion rate estimation with compression-based photoacoustic-ultrasound imaging

Tissue perfusion is essential for transporting blood oxygen and nutrients. Measurement of tissue perfusion rate would have a significant impact in clinical and preclinical arenas. However, there are few techniques to image this important parameter and they typically require contrast agents. A label-free methodology based on tissue compression and imaging with a high-frequency photoacoustic-ultrasound system is introduced for estimating and visualizing tissue perfusion rates. Experiments demonstrate statistically significant differences in depth-resolved perfusion rates in a human subject with various temperature exposure conditions.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Patient characteristics influencing the variability of distributed parameter-based models in DCE-CT kinetic analysis

Kinetic parameter variability may be sensitive to kinetic model choice, kinetic model implementation or patient-specific effects. The purpose of this study was to assess their impact on the variability of dynamic contrast-enhanced computed tomography (DCE-CT) kinetic parameters. A total of 11 canine patients with sinonasal tumours received high signal-to-noise ratio, test-double retest DCE-CT scans. The variability for three distributed parameter (DP)-based models was assessed by analysis of variance. Mixed-effects modelling evaluated patient-specific effects. Inter-model variability (CV_inter ) was comparable to or lower than intra-model variability (CV_intra ) for blood flow (CV_inter :[4-28%], CV_intra :[28-31%]), fractional vascular volume (CV_inter :[3-17%], CV_intra :[16-19%]) and permeability-surface area product (CV_inter :[5-12%], CV_intra :[14-15%]). The kinetic models were significantly (P<0.05) impacted by patient characteristics for patient size, area underneath the curve of the artery and of the tumour. In conclusion, DP-based models demonstrated good agreement with similar differences between models and scans. However, high variability in the kinetic parameters and their sensitivity to patient size may limit certain quantitative applications.

Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI

Tracer kinetic methods employed for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial-venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods for T(1) -weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrast-enhanced MRI data.

On the scope and interpretation of the Tofts models for DCE-MRI

The Tofts model (TM) and extended Tofts model (ETM) have become a standard for the analysis of dynamic contrast-enhanced MRI. In this study, a mathematical analysis is used to identify exactly in which tissue types the Tofts models may be applied. The results show that the TM is accurate if and only if the tissue is weakly vascularised (small blood volume). The ETM is additionally accurate in highly perfused tissues (high blood flow). In tissues that are highly vascularised, or where tracer exchange is very fast or very slow, TM and ETM accurately fit the data but lead to a misinterpretation of the parameters. In tissue types with intermediate vascularity, perfusion and tracer exchange, neither model offers a good fit to the tissue concentrations. A good fit can be obtained with a measured input function by reducing the temporal resolution, but this does not improve the accuracy of the parameters. In conclusion, the Tofts models only produce reliable parameter values if the tissue is weakly vascularized (TM or ETM) or highly perfused (ETM). Without prior knowledge that at least one of these constraints is fulfilled, the physiological interpretation of the values produced by the Tofts models is unclear.

Validity of perfusion parameters obtained using the modified Tofts model: a simulation study

The two-compartment Tofts model (2CTM) has had widespread use in research and clinical practice. It assumes there is no broadening associated with the bolus transit through the capillary bed of the tissue under study. This assumption is often violated, with consequences that are hard to predict intuitively. The two-compartment exchange model is a generalization of 2CTM obtained by dropping the zero-broadening hypothesis, making it suitable for estimating the impact of violating this assumption. Using data simulated on the basis of the two-compartment exchange model, the correspondence between the hemodynamic parameters serving as input for the two-compartment exchange model and the parameters resulting from fitting the data with the 2CTM was investigated. The influence of tissue type and experimental setup was studied. Generally, a large tissue and setup dependent bias of the 2CTM fitting results with respect to the two-compartment exchange model input was observed. Extreme caution is needed when interpreting 2CTM data.

Dynamic contrast-enhanced CT of head and neck tumors: perfusion measurements using a distributed-parameter tracer kinetic model. Initial results and comparison with deconvolution-based analysis

The objective of this work was to evaluate the feasibility of a two-compartment distributed-parameter (DP) tracer kinetic model to generate functional images of several physiologic parameters from dynamic contrast-enhanced CT data obtained of patients with extracranial head and neck tumors and to compare the DP functional images to those obtained by deconvolution-based DCE-CT data analysis. We performed post-processing of DCE-CT studies, obtained from 15 patients with benign and malignant head and neck cancer. We introduced a DP model of the impulse residue function for a capillary-tissue exchange unit, which accounts for the processes of convective transport and capillary-tissue exchange. The calculated parametric maps represented blood flow (F), intravascular blood volume (v(1)), extravascular extracellular blood volume (v(2)), vascular transit time (t(1)), permeability-surface area product (PS), transfer ratios k(12) and k(21), and the fraction of extracted tracer (E). Based on the same regions of interest (ROI) analysis, we calculated the tumor blood flow (BF), blood volume (BV) and mean transit time (MTT) by using a modified deconvolution-based analysis taking into account the extravasation of the contrast agent for PS imaging. We compared the corresponding values by using Bland-Altman plot analysis. We outlined 73 ROIs including tumor sites, lymph nodes and normal tissue. The Bland-Altman plot analysis revealed that the two methods showed an accepted degree of agreement for blood flow, and, thus, can be used interchangeably for measuring this parameter. Slightly worse agreement was observed between v(1) in the DP model and BV but even here the two tracer kinetic analyses can be used interchangeably. Under consideration of whether both techniques may be used interchangeably was the case of t(1) and MTT, as well as for measurements of the PS values. The application of the proposed DP model is feasible in the clinical routine and it can be used interchangeably for measuring blood flow and vascular volume with the commercially available reference standard of the deconvolution-based approach. The lack of substantial agreement between the measurements of vascular transit time and permeability-surface area product may be attributed to the different tracer kinetic principles employed by both models and the detailed capillary tissue exchange physiological modeling of the DP technique.

Error analysis of tumor blood flow measurement using dynamic contrast-enhanced data and model-independent deconvolution analysis

We performed error analysis of tumor blood flow (TBF) measurement using dynamic contrast-enhanced data and model-independent deconvolution analysis, based on computer simulations. For analysis, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) consisting of gamma-variate functions using an adiabatic approximation to the tissue homogeneity model under various plasma flow (F(p)), mean capillary transit time (T(c)), permeability-surface area product (PS) and signal-to-noise ratio (SNR) values. Deconvolution analyses based on truncated singular value decomposition with a fixed threshold value (TSVD-F), with an adaptive threshold value (TSVD-A) and with the threshold value determined by generalized cross validation (TSVD-G) were used to estimate F(p) values from the simulated concentration-time curves in the VOI and AIF. First, we investigated the relationship between the optimal threshold value and SNR in TSVD-F, and then derived the equation describing the relationship between the threshold value and SNR for TSVD-A. Second, we investigated the dependences of the estimated F(p) values on T(c), PS, the total duration for data acquisition and the shape of AIF. Although TSVD-F with a threshold value of 0.025, TSVD-A with the threshold value determined by the equation derived in this study and TSVD-G could estimate the F(p) values in a similar manner, the standard deviation of the estimates was the smallest and largest for TSVD-A and TSVD-G, respectively. PS did not largely affect the estimates, while T(c) did in all methods. Increasing the total duration significantly improved the variations in the estimates in all methods. TSVD-G was most sensitive to the shape of AIF, especially when the total duration was short. In conclusion, this study will be useful for understanding the reliability and limitation of model-independent deconvolution analysis when applied to TBF measurement using an extravascular contrast agent.