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This study compared three methods for analyzing DCE-MRI data with a reference region (RR) model: a linear least-square fitting with numerical analysis (LLSQ-N), a nonlinear least-square fitting with numerical analysis (NLSQ-N), and an analytical analysis (NLSQ-A). The accuracy and precision of estimating the pharmacokinetic parameter ratios KR and VR, where KR is defined as a ratio between the two volume transfer constants,K trans,TOI and K trans,RR, and VR is the ratio between the two extracellular extravascular volumes,ve,TOI and ve,RR, were assessed using simulations under various signal-to-noise ratios (SNRs) and temporal resolutions (4, 6, 30, and 60s). When no noise was added, the simulations showed that the mean percent error (MPE) for the estimated KR and VR using the LLSQ-N and NLSQ-N methods ranged from 1.2% to 31.6% with various temporal resolutions while the NLSQ-A method maintained a very high accuracy (<1.0×104%) regardless of the temporal resolution. The simulation also indicated that the LLSQ-N and NLSQ-N methods appear to underestimate the parameter ratios more than the NLSQ-A method. In addition, seven in vivo DCE-MRI datasets from spontaneously occurring canine brain tumors were analyzed with each method. Results for the in vivo study showed that KR(ranging from 0.63 to 3.11) and VR(ranging from 2.82 to 19.16) for the NLSQ-A method were both higher than results for the other two methods (KR ranging from 0.01 to 1.29 and VR ranging from 1.48 to 19.59). A temporal downsampling experiment showed that the averaged percent error for the NLSQ-A method (8.45%) was lower than the other two methods (22.97% for LLSQ-N and 65.02% for NLSQ-N) for KR, and the averaged percent error for the NLSQ-A method (6.33%) was lower than the other two methods (6.57% for LLSQ-N and 13.66% for NLSQ-N) for VR. Using simulations, we showed that the NLSQ-A method can estimate the ratios of pharmacokinetic parameters more accurately and precisely than the NLSQ-N and LLSQ-N methods over various SNRs and temporal resolutions. All simulations were validated with in vivo DCE MRI data. [ABSTRACT FROM AUTHOR]

Abstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is performed by obtaining sequential MRI images, before, during and after the injection of a contrast agent. It is usually used to observe the exchange of contrast agent between the vascular space and extravascular extracellular space (EES), and provide information about blood volume and microvascular permeability. To estimate the kinetic parameters derived from the pharmacokinetic model, accurate knowledge of the arterial input function (AIF) is very important. However, the AIF is usually unknown, and it remains very difficult to obtain such information noninvasively. In this article, without knowledge of the AIF, we applied a reference region (RR) model to analyze the kinetic parameters. The RR model usually depends on kinetic parameters found in previous studies of a reference region. However, both the assignment of reference region parameters (intersubject variation) and the selection of the reference region itself (intrasubject variation) may confound the results obtained by RR methods. Instead of using literature values for those pharmacokinetic parameters of the reference region, we proposed to use two pharmacokinetic parameter ratios between the tissue of interest (TOI) and the reference region. Specifically, one parameter K R is calculated as the ratio between the volume transfer constant K trans of the TOI and RR. Similarly, another parameter V R is calculated as the ratio between the extravascular extracellular volume fraction v e of the TOI and RR. To investigate the consistency of the two ratios, the K trans of the RR was varied ranging from 0.1 to 1.0 min−1, covering the cited literature values. A simulated dataset with different levels of Gaussian noises and an in vivo dataset acquired from five canine brains with spontaneous occurring brain tumors were used to study the proposed ratios. It is shown from both datasets that these ratios are independent of K trans of the RR, implying that there is potentially no need to obtain information about literature values from the reference region for future pharmacokinetic modeling and analysis. [Copyright &y& Elsevier]

Recently, the development of durable hydrophobic surfaces has received much attention, with anti-icing applications in harsh environments such as aircrafts, wind turbines, power lines, and marine vessels. In this paper we describe a design methodology employing a lattice Boltzmann method to determine the optimal geometry of microstructures to achieve superhydrophobicity. We describe a top-down fabrication method to form superhydrophobic micro-hierarchical metal surface using photolithography, nanoimprinting, and continuous metal-to-metal replication using pulse-reverse-current electrochemical deposition. The surfaces were formed of nickel, which has a large hardness and is resistant to corrosion, making it suitable for use in harsh external conditions. We compared the measured wettability of fabricated micro-hierarchical metal surface with that from numerical simulations. The contact angle and contact angle hysteresis of four metal surfaces were measured (i.e., a bare surface, a random nanostructured surface, an engineered nanostructured surface, and an engineered hierarchical structured surface), and the anti-icing properties of these four metal surfaces were investigated. [ABSTRACT FROM AUTHOR]