Symmetric inverse consistent nonlinear registration driven by mutual information.

A nonlinear viscoelastic image registration algorithm based on the demons paradigm and incorporating inverse consistent constraint (ICC) is implemented. An inverse consistent and symmetric cost function using mutual information (MI) as a similarity measure is employed. The cost function also includes regularization of transformation and inverse consistent error (ICE). The uncertainties in balancing various terms in the cost function are avoided by alternatively minimizing the similarity measure, the regularization of the transformation, and the ICE terms. The diffeomorphism of registration for preventing folding and/or tearing in the deformation is achieved by the composition scheme. The quality of image registration is first demonstrated by constructing brain atlas from 20 adult brains (age range 30-60). It is shown that with this registration technique: (1) the Jacobian determinant is positive for all voxels and (2) the average ICE is around 0.004 voxels with a maximum value below 0.1 voxels. Further, the deformation-based segmentation on Internet Brain Segmentation Repository, a publicly available dataset, has yielded high Dice similarity index (DSI) of 94.7% for the cerebellum and 74.7% for the hippocampus, attesting to the quality of our registration method.

Dietary patterns and risk of pancreatic cancer in a hospital-based case-control study

Background. It is important to understand the association between diet and risk of pancreatic cancer in order to better understand the etiology of pancreatic cancer.^ Objectives. Describe the dietary patterns of cases of adenocarcinoma of the pancreas and non-cancer controls and evaluate the odds of having a healthy eating pattern among cases and non-cancer controls.^ Design and Methods. An ongoing hospital-based case-control study was conducted in Houston, Texas from 2000-2008 with 678 pancreatic adenocarcinoma cases and 724 controls. Participants completed a food frequency questionnaire and a risk factor questionnaire. Dietary patterns were derived by principal component analysis and associations between dietary patterns and pancreatic cancer risk were assessed using unconditional logistic regression.^ Results. Two dietary patterns were derived: fruit-vegetable and high fat-meat. There were no statistically significant associations between the fruit-vegetable pattern and pancreatic cancer. An inverse association was seen between the high fat-meat pattern and pancreatic cancer risk when comparing those in the upper intake quintile to those scoring in the lowest quintile after adjusting for demographic and risk factor variables (OR=0.67, p=0.03). In sex-stratified analysis adjusted for demographic and risk factor variables, females scoring in the upper intake quintile of the fruit-vegetable pattern had a 49% lower risk of pancreatic cancer compared to females scoring in the lowest quintile (OR=0.51, p=0.03). An inverse relationship was also seen for the high fat-meat pattern when comparing females in the upper intake quintile to females in the lowest quintile (OR=0.50, p=0.03). In males, neither dietary pattern was significantly associated with pancreatic cancer.^ Conclusions. The current findings for the fruit-vegetable pattern are similar to those of previous studies and support the hypothesis that there is an inverse association between a “healthy” diet (comprised of fruits, vegetables, and whole grains) and risk of having pancreatic cancer (in females only). However, the inverse relationship with the high fat-meat pattern and risk of pancreatic cancer is contrary to other results. Further research on dietary patters and pancreatic cancer risk may lead to better understanding of the etiologic cause of pancreatic cancer.^

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^