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

Providence Independent, V. 16, Thursday, May 7, 1891, [Whole Number: 829]

[4] p. “Persistent in the Right – Fearless in Opposing Wrong.” Newspaper published in Collegeville, Pa. Weekly. Contains local, national and international news, fiction and advertisements.https://digitalcommons.ursinus.edu/providence/1607/thumbnail.jp

Strength and conditioning practices in rowing

There is limited published research on the practices of strength and conditioning (S &C) coaches in Great Britain. Information about training program design would be useful in developing models of good practice and ecologically valid intervention studies. The aim of this research was to quantify the training practices of coaches responsible for the S&C of rowing athletes. A questionnaire was developed that consisted of 6 sections: (a) personal details, (b) physical testing, (c) strength and power development, (d) flexibility development, (e) unique aspects of the program, and (f) any further relevant comments regarding the athletes prescribed training program. Twenty-two rowing and 10 S&C coaches with an average of 10.5 ± 7.2 years' experience agreed to complete the questionnaire. Approximately, 34% coached rowers of Olympic standard, 34% coached national standard, 3% coached regional standard, 19% coached club standard, and 10% coached university standard rowers. All coaches agreed that strength training enhanced rowing performance and the majority (74%) indicated that athletes' strength trained 2-3 times a week. Almost all coaches (94%) reported their rowers performed strength training, with 81% using Olympic lifting, and 91% employing a periodized training model. The clean (63%) and squat (27%) were rated the most important prescribed exercises. Approximately 50% of coaches used plyometrics such as depth jumps, box drills, and standing jumps. Ninety-four percent indicated they conducted physical testing on their rowers, typically assessing cardiovascular endurance (80%), muscular power (70%), muscular strength (70%), and anaerobic capacity (57%). This research represents the only published survey to date on the S&C practices in rowing within Great Britain

A Digital Atlas of the Dog Brain

There is a long history and a growing interest in the canine as a subject of study in neuroscience research and in translational neurology. In the last few years, anatomical and functional magnetic resonance imaging (MRI) studies of awake and anesthetized dogs have been reported. Such efforts can be enhanced by a population atlas of canine brain anatomy to implement group analyses. Here we present a canine brain atlas derived as the diffeomorphic average of a population of fifteen mesaticephalic dogs. The atlas includes: 1) A brain template derived from in-vivo, T1-weighted imaging at 1 mm isotropic resolution at 3 Tesla (with and without the soft tissues of the head); 2) A co-registered, high-resolution (0.33 mm isotropic) template created from imaging of ex-vivo brains at 7 Tesla; 3) A surface representation of the gray matter/white matter boundary of the high-resolution atlas (including labeling of gyral and sulcal features). The properties of the atlas are considered in relation to historical nomenclature and the evolutionary taxonomy of the Canini tribe. The atlas is available for download (https://cfn.upenn.edu/aguirre/wiki/public:data_plosone_2012_datta)