Objectives: Recently, there have been important advances in the clinical application of targeted hybrid near-infrared (NIR) fluorescent-radioactive tracers. ICG-99mTc-nanocolloid, for example, is already being used by some centres for sentinel lymph node biopsy in head and neck cancer. The radioactive component allows imaging at depths which would not be possible with NIR alone and, once exposed, the NIR fluorescence reporter can be imaged at very high resolution. Gamma detection is currently carried out with a separate hand-held gamma camera or with a non-imaging probe. Visualisation of NIR fluorescence during surgery requires a dedicated NIR camera, several of which are available commercially. We describe a novel hand-held hybrid NIR-gamma small field of view camera, capable of displaying co-aligned images from both modalities, which can be fused into one image or viewed separately. This study is a preliminary investigation of the performance of the fluorescence component of this camera, including phantom studies and first images from a preclinical pilot study. Methods: The hybrid camera consists of a 1500 µm thick thallium doped caesium iodide columnar (CsI:Tl) scintillator coupled to an electron multiplying charged coupled device (EMCCD). A 1.0mm diameter tungsten pinhole collimator gives a 40mm x 40mm nominal field of view for an 8mm x 8mm CCD detection area. A fluorescence camera was aligned to provide the same field of view as the gamma camera with an LED ring as the excitation source. The performance of the fluorescence imaging was quantified in this study for the fluorophores ICG and IRDye800CW (CW800) using a range of bespoke phantom experiments. In vivo images were also obtained from a preclinical study of a targeted hybrid tracer (cRGD-CW800-TCO + TCO-DOTA-111In) in mice with HT29 colorectal cancer xenografts. RESULTS AND CONCLUSION: The portable hybrid camera prototype has been shown to successfully image dual NIR-gamma tracers using both in vitro and in vivo experimental models. With further development, this camera could be used intraoperatively, offering the benefits of gamma imaging at depth in tissues and high resolution surface NIR fluorescence imaging in a single imaging system.
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Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of concrete. For this reason, the researchers conducted an experimental program to determine the stress-strain relationship of 30 concrete samples reinforced with two distinct fibers (a hybrid of polyvinyl alcohol and steel fibers), with compressive strengths ranging from 40 to 120 MPa. A total of 80% of the experimental results were used to develop a new empirical stress-strain model, which was accomplished through the application of the parti
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A frequently used approach for denoising is the shrinkage of coefficients of the noisy signal representation in a transform domain. This paper proposes an algorithm based on hybrid transform (stationary wavelet transform proceeding by slantlet transform); The slantlet transform is applied to the approximation subband of the stationary wavelet transform. BlockShrink thresholding technique is applied to the hybrid transform coefficients. This technique can decide the optimal block size and thresholding for every wavelet subband by risk estimate (SURE). The proposed algorithm was executed by using MATLAB R2010aminimizing Stein’s unbiased with natural images contaminated by white Gaussian noise. Numerical results show that our algorithm co
... Show MoreEmotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In
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