Measurement of the vascular input function in mice for DCE-MRI

DCE-MRI is an important technique in the study of small animal cancer models because its sensitivity to vascular changes opens the possibility of quantitative assessment of early therapeutic response. However, extraction of physiologically descriptive parameters from DCE-MRI data relies upon measurement of the vascular input function (VIF), which represents the contrast agent concentration time course in the blood plasma. This is difficult in small animal models due to artifacts associated with partial volume, inflow enhancement, and the limited temporal resolution achievable with MR imaging. In this work, the development of a suite of techniques for high temporal resolution, artifact resistant measurement of the VIF in mice is described. One obstacle in VIF measurement is inflow enhancement, which decreases the sensitivity of the MR signal to the presence of contrast agent. Because the traditional techniques used to suppress inflow enhancement degrade the achievable spatiotemporal resolution of the pulse sequence, improvements can be achieved by reducing the time required for the suppression. Thus, a novel RF pulse which provides spatial presaturation contemporaneously with the RF excitation was implemented and evaluated. This maximizes the achievable temporal resolution by removing the additional RF and gradient pulses typically required for suppression of inflow enhancement. A second challenge is achieving the temporal resolution required for accurate characterization of the VIF, which exceeds what can be achieved with conventional imaging techniques while maintaining adequate spatial resolution and tumor coverage. Thus, an anatomically constrained reconstruction strategy was developed that allows for sampling of the VIF at extremely high acceleration factors, permitting capture of the initial pass of the contrast agent in mice. Simulation, phantom, and in vivo validation of all components were performed. Finally, the two components were used to perform VIF measurement in the murine heart. An in vivo study of the VIF reproducibility was performed, and an improvement in the measured injection-to-injection variation was observed. This will lead to improvements in the reliability of quantitative DCE-MRI measurements and increase their sensitivity.

Nonviolent factors: Variables associated with ethnic violence, and factors which are associated with nonviolent response to grievances toward members of other ethnic groups

Ethnic violence appears to be the major source of violence in the world. Ethnic hostilities are potentially all-pervasive because most countries in the world are multi-ethnic. Public health's focus on violence documents its increasing role in this issue.^ The present study is based on a secondary analysis of a dataset of responses by 272 individuals from four ethnic groups (Anglo, African, Mexican, and Vietnamese Americans) who answered questions regarding variables related to ethnic violence from a general questionnaire which was distributed to ethnically diverse purposive, nonprobability, self-selected groups of individuals in Houston, Texas, in 1993.^ One goal was psychometric: learning about issues in analysis of datasets with modest numbers, comparison of two approaches to dealing with missing observations not missing at random (conducting analysis on two datasets), transformation analysis of continuous variables for logistic regression, and logistic regression diagnostics.^ Regarding the psychometric goal, it was concluded that measurement model analysis was not possible with a relatively small dataset with nonnormal variables, such as Likert-scaled variables; therefore, exploratory factor analysis was used. The two approaches to dealing with missing values resulted in comparable findings. Transformation analysis suggested that the continuous variables were in the correct scale, and diagnostics that the model fit was adequate.^ The substantive portion of the analysis included the testing of four hypotheses. Hypothesis One proposed that attitudes/efficacy regarding alternative approaches to resolving grievances from the general questionnaire represented underlying factors: nonpunitive social norms and strategies for addressing grievances--using the political system, organizing protests, using the system to punish offenders, and personal mediation. Evidence was found to support all but one factor, nonpunitive social norms.^ Hypothesis Two proposed that the factor variables and the other independent variables--jail, grievance, male, young, and membership in a particular ethnic group--were associated with (non)violence. Jail, grievance, and not using the political system to address grievances were associated with a greater likelihood of intergroup violence.^ No evidence was found to support Hypotheses Three and Four, which proposed that grievance and ethnic group membership would interact with other variables (i.e., age, gender, etc.) to produce variant levels of subgroup (non)violence.^ The generalizability of the results of this study are constrained by the purposive self-selected nature of the sample and small sample size (n = 272).^ Suggestions for future research include incorporating other possible variables or factors predictive of intergroup violence in models of the kind tested here, and the development and evaluation of interventions that promote electoral and nonelectoral political participation as means of reducing interethnic conflict. ^

PREDICTIVE VARIABLES FOR WEIGHT CHANGE 2 YEARS FOLLOWING PARTICIPATION IN A WEIGHT LOSS INTERVENTION PROGRAM

A historical prospective study was designed to assess the man weight status of subjects who participated in a behavioral weight reduction program in 1983 and to determine whether there was an association between the dependent variable weight change and any of 31 independent variables after a 2 year follow-up period. Data was obtained by abstracting the subjects records and from a follow-up questionnaire administered 2 years following program participation. Five hundred nine subjects (386 females and 123 males) of 1460 subjects who participated in the program, completed and returned the questionnaire. Results showed that mean weight was significantly different (p < 0.001) between the measurement at baseline and after a 2 year follow-up period. The mean weight loss of the group was 5.8 pounds, 10.7 pounds for males and 4.2 pounds for females after a 2 year follow-up period. A total of 63.9% of the group, 69.9% of males and 61.9% of females were still below their initial weight after the 2 year follow-up period. Sixteen of the 31 variables assessed utilizing bivariate analyses were found to be significantly (p (LESSTHEQ) 0.05) associated with weight change after a 2 year follow-up period. These variables were then entered into a multivariate linear regression model. A total of 37.9% of the variance of the dependent variable, weight change, was accounted for by all 16 variables. Eight of these variables were found to be significantly (p (LESSTHEQ) 0.05) predictive of weight change in the stepwise multivariate process accounting for 37.1% of the variance. These variables included: Two baseline variables (percent over ideal body weight at enrollment and occupation) and six follow-up variables (feeling in control of eating habits, percent of body weight lost during treatment, frequency of weight measurement, physical activity, eating in response to emotions, and number of pounds of weight gain needed to resume a diet). It was concluded that a greater amount of emphasis should be placed on the six follow-up variables by clinicians involved in the treatment of obesity, and by the subjects themselves to enhance their chances of success at long-term weight loss. ^

Fast magnetic resonance temperature imaging for focused ultrasound thermal therapy

The current standard for temperature sensitive imaging using magnetic resonance (MR) is 2-D, spoiled, fast gradient-echo (fGRE) phase-difference imaging exploiting temperature dependent changes in the proton resonance frequency (PRF). The echo-time (TE) for optimal sensitivity is larger than the typical repetition time (TR) of an fGRE sequence. Since TE must be less than TR in the fGRE sequence, this limits the technique's achievable sensitivity, spatial, and temporal resolution. This adversely affects both accuracy and volume coverage of the measurements. Accurate measurement of the rapid temperature changes associated with pulsed thermal therapies, such as high-intensity focused ultrasound (FUS), at optimal temperature sensitivity requires faster acquisition times than those currently available. ^ Use of fast MR acquisition strategies, such as interleaved echo-planar and spiral imaging, can provide the necessary increase in temporal performance and sensitivity while maintaining adequate signal-to-noise and in-plane spatial resolution. This research explored the adaptation and optimization of several fast MR acquisition methods for thermal monitoring of pulsed FUS thermal therapy. Temperature sensitivity, phase-difference noise and phase-difference to phase-difference-to noise ratio for the different pulse sequences were evaluated under varying imaging parameters in an agar gel phantom to establish optimal sequence parameters for temperature monitoring. The temperature sensitivity coefficient of the gel phantom was measured, allowing quantitative temperature extrapolations. ^ Optimized fast sequences were compared based on the ability to accurately monitor temperature changes at the focus of a high-intensity focused ultrasound unit, volume coverage, and contrast-to-noise ratio in the temperature maps. Operating parameters, which minimize complex phase-difference measurement errors introduced by use of the fast-imaging methods, were established. ^

Investigation of arterial spin labeling MRI for quantitative cerebral blood flow measurement

Arterial spin labeling (ASL) is a technique for noninvasively measuring cerebral perfusion using magnetic resonance imaging. Clinical applications of ASL include functional activation studies, evaluation of the effect of pharmaceuticals on perfusion, and assessment of cerebrovascular disease, stroke, and brain tumor. The use of ASL in the clinic has been limited by poor image quality when large anatomic coverage is required and the time required for data acquisition and processing. This research sought to address these difficulties by optimizing the ASL acquisition and processing schemes. To improve data acquisition, optimal acquisition parameters were determined through simulations, phantom studies and in vivo measurements. The scan time for ASL data acquisition was limited to fifteen minutes to reduce potential subject motion. A processing scheme was implemented that rapidly produced regional cerebral blood flow (rCBF) maps with minimal user input. To provide a measure of the precision of the rCBF values produced by ASL, bootstrap analysis was performed on a representative data set. The bootstrap analysis of single gray and white matter voxels yielded a coefficient of variation of 6.7% and 29% respectively, implying that the calculated rCBF value is far more precise for gray matter than white matter. Additionally, bootstrap analysis was performed to investigate the sensitivity of the rCBF data to the input parameters and provide a quantitative comparison of several existing perfusion models. This study guided the selection of the optimum perfusion quantification model for further experiments. The optimized ASL acquisition and processing schemes were evaluated with two ASL acquisitions on each of five normal subjects. The gray-to-white matter rCBF ratios for nine of the ten acquisitions were within ±10% of 2.6 and none were statistically different from 2.6, the typical ratio produced by a variety of quantitative perfusion techniques. Overall, this work produced an ASL data acquisition and processing technique for quantitative perfusion and functional activation studies, while revealing the limitations of the technique through bootstrap analysis. ^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^