Abstract

The generation of anatomical models is one the most important concern to biomedical researchers as well as to medical doctors, due to needed to understand the human tissues. Is know that the soft tissues like heart, brain, prostate and hard tissues like jaw, bones, skull, etc are structures of complex morphologies, so, the anatomical models generation is not an easy and trivial task. Currently, this task has benefited of advances of imaging diagnostic, which permit obtain cross and longitudinal sections of human body. In this research, we describe a method to obtain 3D discrete models of human body given by a dataset of medical images. Five main modules were implemented in prototype software: (1) Reading and 3D reconstruction of Computerized Axial Tomography and Magnetic Resonance Images. (2) Preprocessing techniques for improve the low medical images quality by using enhancement algorithms to reduce image noise and to increase structures contrast. (3) Combined segmentation techniques for tissue identification, which were applied through a multi-stage approach. (4) Post processing techniques to improve segmented volumes and (5) Exportation task of volumes to readable formats by Computer Aided Design (CAD) tools to be later analyzed by numerical methods. The performance of our method is shown on several medical examples and the techniques were validated using statistical descriptors to compare our models with models from free databases. Results showed that the implemented techniques generate precise and useful models for numerical analysis and medical survey, planning and surgery in a short processing time.

Abstract

The infant brain is unlike the adult brain, with considerable differences in morphological, neurodynamic, and haemodynamic features. As the majority of current MRI analysis tools were designed for use in adults, a primary objective of the Developing Human Connectome Project (dHCP) is to develop optimised methodological pipelines for the analysis of neonatal structural, resting state, and diffusion MRI data. Here, in an independent neonatal dataset we have extended and optimised the dHCP fMRI preprocessing pipeline for the analysis of stimulus-response fMRI data. We describe and validate this extended dHCP fMRI preprocessing pipeline to analyse changes in brain activity evoked following an acute noxious stimulus applied to the infant's foot. We compare the results obtained from this extended dHCP pipeline to results obtained from a typical FSL FEAT-based analysis pipeline, evaluating the pipelines' outputs using a wide range of tests. We demonstrate that a substantial increase in spatial specificity and sensitivity to signal can be attained with a bespoke neonatal preprocessing pipeline through optimised motion and distortion correction, ICA-based denoising, and haemodynamic modelling. The improved sensitivity and specificity, made possible with this extended dHCP pipeline, will be paramount in making further progress in our understanding of the development of sensory processing in the infant brain. Highlights • The dHCP fMRI preprocessing pipeline is generalizable to stimulus-evoked datasets. • Customised FIX denoising in infant fMRI data substantially improves data quality. • The dHCP pipeline greatly improves spatial specificity and sensitivity to signal. • Bespoke fMRI data analysis outperforms typical analytical methods for neonatal data.

Abstract

Na programação de operações de movimentação de petróleo em complexos portuários contendo navios, píeres, tanques e oleodutos, um modelo de otimização das operações é frequentemente utilizado. Entretanto, devido à complexidade do problema, o tempo computacional é um fator limitante. Neste artigo, estuda-se a aplicação de um modelo de programação linear inteira mista (PLIM) existente na literatura e propõe-se uma etapa de pré-processamento, baseada na teoria das restrições (TOC), com o objetivo de reduzir o tempo computacional. A partir da identificação do gargalo do problema, determinadas restrições são retiradas do modelo numa etapa de pré-processamento. Para verificar a escolha das restrições críticas no pré-processamento, uma análise baseada na noção de preço dual do LP é apresentada. Embora a otimalidade da solução não seja garantida, os resultados mostram uma pequena deterioração da solução em relação à ótima. Porém, com o tempo computacional significativamente reduzido."br"In the scheduling of oil transfer operations for harbor plants containing ships, piers, tanks and pipelines, an optimization model is frequently used. However, due to the complexity involved, computational time is a real concern. In this paper, a mixed integer linear programming model (MILP) found in the literature is studied and a preprocessing procedure is proposed. This procedure is based on the Theory of Constraints (TOC), which is used to reduce the model compilation time. Since the problem bottleneck is identified, the corresponding constraints are not considered in the preprocessing step. An analysis based on LP dual price is also carried out to verify actions taken in the preprocessing step. Although optimality cannot be assured, the results obtained show a minor deviation from the optimal solution but with a significant computational time gain. Document type: Article