The Impact of Body Image on Women in Later Life: Effects on Quality of Life and Body Perception

Physical, mental, and emotional changes can occur throughout the aging process, making it important to treat different age groups as separate populations when researching body image. Yet, very few instruments have been validated for middle-aged and older adults. An online survey was used to perform a validation of the Body Image Quality of Life Inventory (BIQLI) and examine demographic relationships, with a sample of 947 women, ages 40-79, from across the United States. A principal component analysis (PCA) and convergent validity supported the use of the BIQLI for women ages 40 to 79. A one-factor model was validated for comparing BIQLI scores across decades. A two-factor model provided useful information about potential subscales within the BIQLI for women of certain decades. Two- and three-factor models had different patterns for each decade, making the one-factor model the only solution for comparing across age groups. The strongest demographic relationship existed between total BIQLI score and body mass index, though the relationship weakened as age increased. Income level and relationship status had small associations with BIQLI score only for the women in their 60s. Results indicate that the 60s may be a time of transition for body image and warrants continued research. Race also played a distinct role with Caucasians having lower scores than the sample with all races combined. Combining races may skew findings and lead to incorrect assumptions, especially when Caucasians are included in a sample

Neuroconductor: an R platform for medical imaging analysis

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience

A novel recruiting and surveying method: Participatory research during a Pacific Islander community’s traditional cultural event

Little is known about the health status of Marshallese, a Pacific Islander subpopulation living in the United States. The Marshallese have established a growing community in Northwest Arkansas, providing a unique opportunity for increasing knowledge regarding the health of this minority group. This article describes how a community-based participatory research process was used by a community and university coalition to identify and refine questionnaires and recruit study participants. Questionnaires were self-administered on computers during a one-week traditional cultural event. A total of 874 Marshallese from Arkansas completed the questionnaire, exceeding the goal of 600 respondents. Lessons learned, including the level and timing of involvement of both the leadership and the community at large, are discussed in detail. This approach enhanced communication and collaboration between the Marshallese community, service providers and researchers, resulting in higher participation and interest among the Marshallese community.Keywords: participatory research, minority populations, community health assessment, community coalition, Marshalles

Multi-start Method with Prior Learning for Image Registration

We propose an efficient image registration strategy that is based on learned prior distributions of transformation parameters. These priors are used to constrain a finite- time multi-start optimization method. Motivation for this approach comes from the fact that standard affine brain image registration methods, especially those based on gradient descent optimization alone, are affected by the initial search position. While global optimization methods can resolve this problem, they are are often very time consuming. Our goal is to build an explicit prior model of the gap between a typical registration solution and the solution gained by a global optimization method. We use this learned prior model to restrict randomized search in the relevant parameter space surrounding the initial solution. Global optimization in this restricted parameter space provides, in finite time, results that are superior to both gradient descent and the general multi-start strategy. The performance of our method is illustrated on a data set of 67 elderly and neurodegenerative brains. Our novel learning strategy and the associated registration method are shown to outperform other approaches. Theoretical, synthetic and real-world examples illustrate this improvement

Fiber Orientation Estimation Guided by a Deep Network

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for fiber tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs with a relatively small number of diffusion gradients. However, accurate FO estimation in regions with complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent the diffusion signals. To estimate the mixture fractions of the dictionary atoms (and thus coarse FOs), a deep network is designed specifically for solving the sparse reconstruction problem. Here, the smaller dictionary is used to reduce the computational cost of training. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding dense basis FOs is used and a weighted l1-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and real dMRI data, and the results demonstrate the benefit of using a deep network for FO estimation.Comment: A shorter version is accepted by MICCAI 201

Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm

Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of treatment due to its minimally invasive nature. However, due to the limited field of view of the endoscope, surgeons rely on navigation systems to guide them within the nasal cavity. State of the art navigation systems report registration accuracy of over 1mm, which is large compared to the size of the nasal airways. We present an anatomically constrained video-CT registration algorithm that incorporates multiple video features. Our algorithm is robust in the presence of outliers. We also test our algorithm on simulated and in-vivo data, and test its accuracy against degrading initializations.Comment: 8 pages, 4 figures, MICCA