Hadamard Slice Encoding for Reduced-FOV Diffusion-Weighted Imaging

Cataloged from PDF version of article.Methods: A 2D echo-planar RF pulse and matching multiband refocusing RF pulses were designed using the Shinnar-Le Roux algorithm to reduce band interference, and variable-rate selective excitation to shorten the pulse durations. Hadamardencoded images were resolved through a phase-preserving image reconstruction. The performance of the method was evaluated via simulations, phantom experiments, and in vivo high-resolution axial DWI of spinal cord. Purpose: To improve the clinical utility of diffusion-weighted imaging (DWI) by extending the slice coverage of a highresolution reduced field-of-view technique. Theory: Challenges in achieving high spatial resolution restrict the use of DWI in assessment of small structures such as the spinal cord. A reduced field-of-view method with 2D echo-planar radiofrequency (RF) excitation was recently proposed for high-resolution DWI. Here, a Hadamard sliceencoding scheme is proposed to double the slice coverage by exploiting the periodicity of the 2D echo-planar RF excitation profile. Results: The proposed scheme successfully extends the slice coverage, while preserving the sharp excitation profile and the reliable fat suppression of the original method. For in vivo axial DWI of the spinal cord, an in-plane resolution of 0.7 × 0.7 mm2 was achieved with 16 slices. Conclusion: The proposed Hadamard slice-encoding scheme doubles the slice coverage of the 2D echo-planar RF reduced field-of-view method without any scan-time penalty. © 2013 Wiley Periodicals, Inc

Targeted vessel reconstruction in non-contrast-enhanced steady-state free precession angiography

Image quality in non-contrast-enhanced (NCE) angiograms is often limited by scan time constraints. An effective solution is to undersample angiographic acquisitions and to recover vessel images with penalized reconstructions. However, conventional methods leverage penalty terms with uniform spatial weighting, which typically yield insufficient suppression of aliasing interference and suboptimal blood/background contrast. Here we propose a two-stage strategy where a tractographic segmentation is employed to auto-extract vasculature maps from undersampled data. These maps are then used to incur spatially adaptive sparsity penalties on vascular and background regions. In vivo steady-state free precession angiograms were acquired in the hand, lower leg and foot. Compared with regular non-adaptive compressed sensing (CS) reconstructions (CSlow), the proposed strategy improves blood/background contrast by 71.3±28.9% in the hand (mean±s.d. across acceleration factors 1-8), 30.6±11.3% in the lower leg and 28.1±7.0% in the foot (signed-rank test, P< 0.05 at each acceleration). The proposed targeted reconstruction can relax trade-offs between image contrast, resolution and scan efficiency without compromising vessel depiction. © 2016 John Wiley & Sons, Ltd

Análise de timol em cera de abelha por micro-extracção em fase sólida (SPME)

A aplicação contínua de acaricídas lipofílicos sintéticos no tratamento das abelhas conduz a uma acumulação que depende da frequência, lipofilicidade e quantidade de princípio activo utilizada. Este efeito é mais acentuado na cera de abelha que no mel, no entanto, e porque a persistência destes resíduos é elevada, provoca o aparecimento de resistências e a perda do seu efeito acaricida.[1] Esta razão levou à pesquisa de outros compostos alternativos não tóxicos e não persistentes, com efeito sobre o ácaro das abelhas, Varroa Jacobsoni. Entre estes compostos encontra-se o timol, um composto fenólico, volátil, presente no tomilho. Dos diversos componentes dos óleos essenciais este é sem dúvida o que demonstrou maior efeito acaricida, utilizando-se no tratamento das abelhas directamente ou como componente de diversas formulações.[2] Em Portugal, foi introduzido muito recentemente sob a forma comercial de APIGUARD: um gel, à base de timol, que controla termicamente a libertação do princípio activo. O controlo dos resíduos de timol na cera de abelha e no mel é assim um desafio actual quer do ponto de vista sanitário quer de qualidade alimentar. A micro-extracção em fase sólida (SPME) é uma técnica de preparação de amostras que se baseia na sorção de analítos no revestimento de uma fibra de sílica fundida e posterior desorção térmica no injector de um cromatógrafo em fase gasosa (GC). Para além de combinar num único processo etapas de extracção, purificação e concentração dos analitos, a técnica de SPME apresenta uma série de vantagens relativamente às técnicas de extracção convencionais, como a extracção líquido-líquido e extracção em fase sólida, nomeadamente a sua relativa simplicidade e rapidez, reduzido custo e não utilização de solventes para a extracção de analitos, para além de permitir a extracção por imersão directa na amostra gasosa ou líquida e extracção por amostragem do espaço-de-cabeça da amostra líquida ou sólida.[3] Ao contrário das técnicas tradicionais, que permitem uma extracção quantitativa dos analitos, a técnica de SPME baseia-se num equilíbrio de partição do analito. Esta particularidade torna a técnica de SPME bastante sensível a parâmetros experimentais que possam afectar os coeficientes de partição dos analitos e, consequentemente, a sensibilidade e reprodutibilidade dos resultados.[4] O objectivo deste trabalho é o desenvolvimento de uma metodologia para a análise de timol em ceras contaminadas, utilizando como padrão interno a benzofenona. Em primeiro lugar, procedeu-se à optimização da técnica através da determinação da quantidade de cera, temperatura de análise e período de contacto da fibra com o espaço-de-cabeça da amostra mais adequados para o caso em estudo. Numa segunda fase, procedeu-se à análise de diversas lâminas de cera contaminadas propositadamente com timol e sujeitas a diferentes condições de armazenamento: em frio, ao ar e em estufa. Finalmente, procedeu-se à construção da curva de calibração e quantificação do timol presente nas diversas amostras de cera analisadas. Considerando-se os resultados, para os níveis de contaminação avaliados, as condições analíticas mais adequadas ocorrem com a utilização de 1 g de cera, mantendo-se a fibra em contacto com o espaço-de-cabeça durante 40 minutos a uma temperatura de 60 ºC. Nestas condições experimentais foi possível obter uma boa correlação linear (r2=0,990) no intervalo de concentrações [3,5-14 mg/g]. A quantidade de timol encontrada nas amostras é significativamente inferior à colocada durante o processo de fabrico das lâminas, pelo que o processo de conservação não é o mais adequado, sendo evidente uma menor quantidade de timol quando a lâmina de cera é colocada na estufa

Seeing SPIOs Directly In Vivo with Magnetic Particle Imaging

Magnetic particle imaging (MPI) is a new molecular imaging technique that directly images superparamagnetic tracers with high image contrast and sensitivity approaching nuclear medicine techniques—but without ionizing radiation. Since its inception, the MPI research field has quickly progressed in imaging theory, hardware, tracer design, and biomedical applications. Here, we describe the history and field of MPI, outline pressing challenges to MPI technology and clinical translation, highlight unique applications in MPI, and describe the role of the WMIS MPI Interest Group in collaboratively advancing MPI as a molecular imaging technique. We invite interested investigators to join the MPI Interest Group and contribute new insights and innovations to the MPI field. © 2017, World Molecular Imaging Society

Gas and seismicity within the Istanbul seismic gap

Understanding micro-seismicity is a critical question for earthquake hazard assessment. Since the devastating earthquakes of Izmit and Duzce in 1999, the seismicity along the submerged section of North Anatolian Fault within the Sea of Marmara (comprising the “Istanbul seismic gap”) has been extensively studied in order to infer its mechanical behaviour (creeping vs locked). So far, the seismicity has been interpreted only in terms of being tectonic-driven, although the Main Marmara Fault (MMF) is known to strike across multiple hydrocarbon gas sources. Here, we show that a large number of the aftershocks that followed the M 5.1 earthquake of July, 25th 2011 in the western Sea of Marmara, occurred within a zone of gas overpressuring in the 1.5–5 km depth range, from where pressurized gas is expected to migrate along the MMF, up to the surface sediment layers. Hence, gas-related processes should also be considered for a complete interpretation of the micro-seismicity (~M < 3) within the Istanbul offshore domain

Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboar

Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582)

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard