Comparison of MR imaging sequences for liver and head and neck interventions: is there a single optimal sequence for all purposes?

To compare the appropriate pulse sequences for interventional device guidance during magnetic resonance (MR) imaging at 0.2 T and to evaluate the dependence of sequence selection on the anatomic region of the procedure. Using a C-arm 0.2 T system, four interventional MR sequences were applied in 23 liver cases and during MR-guided neck interventions in 13 patients. The imaging protocol consisted of: multislice turbo spin echo (TSE) T2w, sequential-slice fast imaging with steady precession (FISP), a time-reversed version of FISP (PSIF), and FISP with balanced gradients in all spatial directions (True-FISP) sequences. Vessel conspicuity was rated and contrast-to-noise ratio (CNR) was calculated for each sequence and a differential receiver operating characteristic was performed. Liver findings were detected in 96% using the TSE sequence. PSIF, FISP, and True-FISP imaging showed lesions in 91%, 61%, and 65%, respectively. The TSE sequence offered the best CNR, followed by PSIF imaging. Differential receiver operating characteristic analysis also rated TSE and PSIF to be the superior sequences. Lesions in the head and neck were detected in all cases by TSE and FISP, in 92% using True-FISP, and in 84% using PSIF. True-FISP offered the best CNR, followed by TSE imaging. Vessels appeared bright on FISP and True-FISP imaging and dark on the other sequences. In interventional MR imaging, no single sequence fits all purposes. Image guidance for interventional MR during liver procedures is best achieved by PSIF or TSE, whereas biopsies in the head and neck are best performed using FISP or True-FISP sequences

DETECÇÃO DE ÁRVORES EM NUVENS DE PONTOS DE VARREDURA LASER TERRESTRE

A utilização do laser terrestre para levantamentos em povoamentos florestais tem como objetivo prover dados à modelagem tridimensional das árvores, no entanto, para que seja possível aplicar tal modelo, é necessário realizar a detecção dos pontos que fazem parte de árvores na varredura. O presente estudo propõe um método para a detecção de árvores a partir da nuvem de pontos 3D de plantios florestais. Inicialmente, procura-se reconstituir a distribuição espacial das árvores a partir da aplicação de um algoritmo de segmentação em uma seção transversal (1 metro) da nuvem de pontos. Em seguida, é apresentado um algoritmo para detectar a posição das árvores com base no padrão de alinhamento do povoamento. Por fim, os resultados obtidos são apresentados para validação pelo usuário da nuvem de pontos. O método apresentado foi testado em parcelas circulares instaladas em povoamentos de Eucalyptus spp. levantados por varreduras simples e múltiplas. Os resultados apontaram a necessidade de utilização de múltiplas estações de TLS para redução do efeito de sombreamento no levantamento das parcelas circulares. A aplicação do método de detecção de árvores em conjunto com a análise visual resultou na identificação de 100% das árvores a partir das nuvens de pontos das parcela

Two Statistical Tools for Assessing Functionality and Protein Characteristics of Different Fava Bean (Vicia faba L.) Ingredients

Fava bean (Vicia faba L.) is a promising source of proteins that can be potentially used as nutritional and/or functional agents for industrial food applications. Fava ingredients are industrially produced, modified, and utilized for food applications. Their processing conditions influence physico-chemical protein properties that further impact ingredient functionality. To design a functionally suitable ingredient, an understanding of the interrelationships between different properties is essential. Hence, this work aimed to assess two statistical analytical tools, Pearson’s correlation and Principal Component Analysis (PCA), for investigating the role of the process conditions of fava ingredients on their functional and protein properties. Fava concentrates were processed by pH (2, 4, 6.4 and 11), temperature (55, 75 and 95 ∘C) and treatment duration (30 and 360 min) into different modified ingredients. These were utilized under two application conditions (pH 4 and 7), and their foam and emulsion properties as well as their ingredient characteristics (charge, solubility, and intrinsic fluorescence) were measured. The results show that foam and emulsion properties are not correlated to each other. They are associated with different protein and non-protein attributes as fava concentrate is a multi-component matrix. Importantly, it is found that the results from the two statistical tools are not fully comparable but do complement each other. This highlights that both statistical analytical tools are equally important for a comprehensive understanding of the impact of process conditions on different properties and the interrelationships between them. Therefore, it is recommended to use Pearson’s correlation and principal component analysis in future investigations of new plant-based proteins