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Background: Recent research on photon detection has led to the introduction of a silicone photomultiplier (SiPM) that operates at a low voltage and is insensitive to magnetic fields. Objective: This work aims to model a scintillation camera with a SiPM sensor and to evaluate the camera reconstructed images from gamma ray projection data.Material and Methods: The type of study in this research is experimental work and analytical. The scintillation camera, modelled from an SiPM sensor array SL4-30035, coupled with a scintillation material Caesium Iodide doped with Thallium (CsI(Tl)), is used in the experimental part. The performance of the camera was evaluated from reconstructed images by a back-projection technique of a radioactive source Caesium-137 (Cs-137). Results: The experiments conducted with a 1 µCi Cs-137 radioactive source have revealed that the bias voltage (Vbias) of the SiPM needs to be set to 27.8 V at an operating temperature between 43 °C to 44 °C. The radioactive source has to be placed within a 1 cm distance from the sensor to obtain the optimum projection data. Finally, the back-projection technique for image reconstruction with linear interpolation pre-processing has revealed that the Ram-Lak filter produces a better image contrast ratio compared to others. Conclusion: This research has successfully modelled a scintillation camera with SiPM that was able to reconstruct images with an 86.4% contrast ratio from gamma ray projection data.

Coded Aperture (CA) imaging has recently been used in nuclear medicine, but still, there is no commercial SPECT imaging camera based on CA for cancer detection. The literature is rich in examples of using the CA for planar and thin 3D imaging. However, thick 3D reconstruction is still challenging for small lesion detection. This paper presents the results of mosaic modified uniformly redundant array (MURA) mask/antimask CA combined with a maximum-likelihood expectation-maximization (MLEM) algorithm. The MLEM is an iterative algorithm applied to a mosaic MURA CA mask/antimask for 3D anthropomorphic breast phantom reconstruction, slice by slice. The difference between the mask and the antimask suppresses the background noise to enhance the quality of reconstructed images. Furthermore, all reconstructed slices are stacked to form a 3D breast phantom image from single-projection data. The results of phantom reconstruction with 8 mm, 6 mm, 4 mm, and 3 mm lesions are presented. Moreover, the proposed SPECT imaging camera can reconstruct a 3D breast phantom from single-projection data of the patient’s scanning. To assess the quality of lesions in the reconstructed images, the contrast-to-background ratio (CBR), the peak signal-to-noise ratio (PSNR) and mean square error (MSE) were measured.

In this study, a novel environment‐friendly PBST/PPC‐based blown film was prepared using maleic anhydride (MA) as a reactive compatibilizer to enhance the compatibility between poly(butylene succinate‐co‐terephthalate) (PBST) and poly(propylene carbonate) (PPC). Results of rheological testing and gel permeation chromatography (GPC) indicated that MA reacted with PBST/PPC during melt‐blending extrusion. Morphological analysis of the cryo‐fractured surfaces of PBST/PPC blend showed significantly improved compatibility between PBST and PPC with the addition of MA. Moreover, the Young's modulus, tensile strength, breaking strain, and tear strength of PBST/PPC/MA blown films increased with an increase in MA content. In comparison to PBST/MA blown film without PPC, the barrier property of PBST/PPC/MA blown films was improved. In addition, in vitro cell experiments showed that the PBST/PPC/MA blown film was suitable for the growth of mouse fibroblast (L929) cells. In vitro ecotoxicity testing on mung bean plant showed that the extracts from the PBST/PPC/MA blown film had no negative effects on the development of mung bean plant. Furthermore, degradability testing in soil also proved that the PBST/PPC/MA blown film had good biodegradability. Thus, the PBST/PPC/MA blown film can be used in fields, such as food packaging and agricultural mulch film. [ABSTRACT FROM AUTHOR]

Bone is the most common site of metastasis in prostate cancer (PCa) patients and is correlated with poor prognosis and increasing economic burden. Few studies have analyzed the prognostic prediction for metastatic PCa patients with the assistance of neural networks. Four convolutional neural network (CNN) models are developed and evaluated to predict the overall survival (OS) of PCa patients with bone metastasis. All the CNN models are first trained with 64 samples and evaluated with 10 samples; two models use only bone scan images and two models use both bone scan images and clinical parameters (CPs). The predictions of the best models are compared with those by two urology surgeons on 20 test samples. Our best models can predict OS of PCa patients with bone metastasis with AUC=0.8022 by using only bone scan images and AUC=0.8132 by using both bone scan images and CPs on 20 test samples. The best Youden indexes of the two models are 0.6263 and 0.7142, respectively, which are 0.3077 and 0.3131 higher than that of the urologists' average Youden index, which indicate that CNN models exhibit significant advantages. CNN models are suitable to predict OS in PCa patients with bone metastasis using bone scan images and CPs. Our models show better performance in terms of accuracy and stability than urology surgeons. [ABSTRACT FROM AUTHOR]

Compared with their younger counterparts, older adults are inclined to allocate more attentional resources to positive over negative materials. This age-related positivity effect has been reported in various experimental paradigms; however, studies have not investigated the attention stage at which it appears or its potential neural mechanism. Thus, we investigated the time and frequency domain dynamics of younger and older adults during emotional attention processes. We obtained electroencephalography oscillation and event-related potential data for 20 older and 20 younger participants while they performed an emotional dot-probe task. We focused our time and frequency domain dynamics analyses on the posterior regions as a key structure for facial emotion perception and the frontal regions as a crucial structure for cognitive control. In the time domain, older adults showed an initial attentional shift to happy-related stimuli, whereas their younger counterparts did not demonstrate emotional modulation, as reflected by the N2pc component. The time–frequency decomposition was analyzed for the N2pc time window. The results showed that compared with younger adults, older adults showed an increased alpha power for happy faces in the right-posterior regions. Moreover, a parallel pattern was seen in frontal theta activity. The current findings highlight how electrocortical activity of the brain might moderate the tendency to prioritize positive information among healthy older adults. The emergence of an age-related positivity effect may be related to frontal cognitive control processing. These findings provide insight into the prevention and treatment of unsuccessful aging, such as late-life depression and anxiety. [ABSTRACT FROM AUTHOR]

Background: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information. Methods: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics‐DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1‐score metrics. Results: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82–0.88), sensitivity of 0.85 (0.80–0.88), and specificity of 0.84 (0.83–0.87), performing better than the other models. Conclusion: The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC. [ABSTRACT FROM AUTHOR]

Objectives: By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods: A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results: The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion: Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC. [ABSTRACT FROM AUTHOR]

Background: Recent research on photon detection has led to the introduction of a silicone photomultiplier (SiPM) that operates at a low voltage and is insensitive to magnetic fields. Objective: This work aims to model a scintillation camera with a SiPM sensor and to evaluate the camera reconstructed images from gamma ray projection data. Material and Methods: The type of study in this research is experimental work and analytical. The scintillation camera, modelled from an SiPM sensor array SL4-30035, coupled with a scintillation material Caesium Iodide doped with Thallium (CsI(Tl)), is used in the experimental part. The performance of the camera was evaluated from reconstructed images by a back-projection technique of a radioactive source Caesium-137 (Cs-137). Results: The experiments conducted with a 1 µCi Cs-137 radioactive source have revealed that the bias voltage (Vbias) of the SiPM needs to be set to 27.8 V at an operating temperature between 43 °C to 44 °C. The radioactive source has to be placed within a 1 cm distance from the sensor to obtain the optimum projection data. Finally, the back-projection technique for image reconstruction with linear interpolation preprocessing has revealed that the Ram-Lak filter produces a better image contrast ratio compared to others. Conclusion: This research has successfully modelled a scintillation camera with SiPM that was able to reconstruct images with an 86.4% contrast ratio from gamma ray projection data. [ABSTRACT FROM AUTHOR]