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Objectives: To describe fracture rates, back pain, and health-related quality of life (HRQoL) in postmenopausal women with osteoporosis and prior bisphosphonate therapy, treated with teriparatide for up to 18 months and followed up for a further 18 months. Design: Prospective, multinational, and observational study. Methods: Data on prior bisphosphonate use, clinical fractures, back pain visual analog scale (VAS), and HRQoL (EQ-5D) were collected over 36 months. Fracture data were summarized in 6-month intervals and analyzed using logistic regression with repeated measures. Changes from baseline in back pain VAS and EQ-VAS were analyzed using a repeated measures model. Results: Of the 1581 enrolled patients with follow-up data, 1161 (73.4%) had a history of prior bisphosphonate use (median duration: 36 months). Of them, 169 (14.6%) sustained >= 1 fracture during 36-month follow-up. Adjusted odds of fracture were significantly decreased at each 6-month interval compared with the first 6 months of teriparatide treatment: 37% decrease in the 12 to ! 18 months period during teriparatide treatment (P=0.03) and a 76% decrease in the 12- to 18-month period after teriparatide was discontinued (P<0.001). Significant reductions in back pain and improvement in HRQoL were observed. Conclusions: Postmenopausal women with severe osteoporosis previously treated with bisphosphonates had a significant reduction in the incidence of fractures compared with the first 6 months of therapy, a reduction in back pain and an improvement in HRQoL during up to 18 months of teriparatide treatment. These outcomes were still evident for at least 18 months after teriparatide was discontinued. The results should be interpreted in the context of an uncontrolled, observational study in a routine clinical setting.

The goal of this paper is to present the development of a new reconstruction methodology for restoring Magnetic Resonance Images (MRI) from reduced scans in k-space. The proposed approach considers the combined use of Neural Network models and Bayesian restoration, in the problem of MRI image extraction from sparsely sampled k-space, following several different sampling schemes, including spiral and radial, Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required, The goal in such a case is to make measurement time smaller by reducing scanning trajectories as much as possible, In this way, however, underdetermined equations are introduced and poor image reconstruction follows, It is suggested here that significant improvements could be achieved, concerning quality of the extracted image, by judiciously applying Neural Network and Bayesian estimation methods to the k-space data, More specifically, it is demonstrated that Neural Network techniques could construct efficient priors and introduce them in the procedure of Bayesian reconstruction, These ANN Priors are independent of specific image properties and probability distributions, They are based on training supervised Multilayer Perceptron (MLP) neural filters to estimate the missing samples of complex k-space and thus, to improve k-space information capacity, Such a neural filter based prior is integrated to the maximum likelihood procedure involved in the Bayesian reconstruction, It is found that the proposed methodology leads to enhanced image extraction results favorably compared to the ones obtained by the traditional Bayesian MRI reconstruction approach as well as by the pure MLP based reconstruction approach., Papers from 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Oct 25-28, 2001, held in Istanbul, Turkey. See also ADM001351 for entire conference on cd-rom. Original document contains color images.