Abstract: An apparatus and method are described using a forward model to correct pulse pileup in spectrally resolved X-ray projection data from photon-counting detectors (PCDs). To calibrate the forward model, which represents each order of pileup using a respective pileup response matrix (PRM), an optimization search determines the elements of the PRMs that optimize an objective function measuring agreement between the spectra of recorded counts affected by pulse pileup and the estimated counts generated using forward model of pulse pileup. The spectrum of the recorded counts in the projection data is corrected using the calibrated forward model, by determining an argument value that optimizes the objective function, the argument being either a corrected X-ray spectrum or the projection lengths of a material decomposition. Images for material components of the material decomposition are then reconstructed using the corrected projection data.

Abstract: A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.

Abstract: A trencher includes a trencher body; a cable detection mechanism disposed at a front portion of the trencher body; a chain mechanism and a jet mechanism disposed in the center of a bottom portion of the trencher body; a first track mechanism and a second track mechanism, the first track mechanism being disposed on a first side of the bottom portion of the trencher body, and the second track mechanism being disposed on a second side of the bottom portion of the trencher body; and a soil discharging component disposed at a rear portion of the trencher body.

Abstract: A method and apparatus is provided to perform medical imaging in which feature-aware reconstruction is performed using a neural network. The neural network is trained to perform feature-aware reconstruction by using a training dataset in which the target data has a spatially-dependent degree of denoising and artifact reduction based on the features represented in the image. For example, a target image can be generated by reconstructing multiple images, each using a respective regularization parameter that is optimized for a different anatomy/organ (e.g., abdomen, lung, bone, etc.). And a target image can be generated using artifact reduction method (e.g. metal artifact reduction, aliasing artifact reduction, etc.). Then respective regions/features (e.g., abdomen, lung, and bone, artifact free, regions/features) can be extracted from the corresponding images and combined into a single combined image, which is used as the target data to train the neural network.

Abstract: A method and apparatus is provided to reduce the noise in medical imaging by training a deep learning (DL) network to select the optimal parameters for a convolution kernel of an adaptive filter that is applied in the data domain. For example, in X-ray computed tomography (CT) the adaptive filter applies smoothing to a sinogram, and the optimal amount of the smoothing and orientation of the kernel (e.g., a bivariate Gaussian) can be determined on a pixel-by-pixel basis by applying a noisy sinogram to the DL network, which outputs the parameters of the filter (e.g., the orientation and variances of the Gaussian kernel). The DL network is trained using a training data set including target data (e.g., the gold standard) and input data. The input data can be sinograms generated by a low-dose CT scan, and the target data generated by a high-dose CT scan.

Abstract: An electronic device includes a first input module located at a first region, a second input module located at a second region, a detection module, and a processing module. The detection module is configured to transmit a first signal to the first region and the second region and receive a second signal returned based on the first signal. The processing module is connected to the detection module and configured to analyze the second signal to obtain an analysis result, and shield input information of the first input module or the second input module according to the analysis result.

Abstract: A method and apparatuses are provided that use a neural network to correct artifacts in computed tomography (CT) images, especially cone-beam CT (CBCT) artifacts. The neural network is trained using a training dataset of artifact-minimized images paired with respective artifact-exhibiting images. In some embodiments, the artifact-minimized images are acquired using a small cone angle for the X-ray beam, and the artifact-exhibiting images are acquired either by forwarding projecting the artifact-minimized images using a large-cone-angle CBCT configuration or by performing a CBCT scan. In some embodiments, the network is a 2D convolutional neural network, and an artifact-exhibiting image is applied to the neural network as 2D slices taken for the coronal and/or sagittal views. Then the 2D image results from the neural network are reassembled as a 3D imaging having reduced imaging artifacts.

Abstract: The present application relates to a method and device for extracting information from a histogram for display on an electronic device. The method comprises the following steps: inputting, into the electronic device, a document, which includes a histogram to be processed; detecting each element in the histogram to be processed by using a target detection method based on a Faster R-CNN model pre-stored in the electronic device; performing text recognition on each detected text element box by the electronic device; to extract corresponding text information; and converting all the detected elements and text information into structured data for display on the electronic device. The method and the device can detect all the elements in the histogram through deep learning and the use of the Faster R-CNN model for target detection, thus providing a simple and effective solution for information extraction in the histogram.

Abstract: A multi-instance 2-Level-Memory (2LM) architecture manages access by processing instances having different memory usage priorities to memory having different performance and cost levels. The 2LM architecture includes a virtual memory management module that manages access by respective processing instances by creating memory instances based on specified memory usage priority levels and specified virtual memory sizes and defining policies for each usage priority level of the created memory instances. In response to a virtual memory request by a processing instance, the virtual memory management module determines whether a virtual memory size at a designated usage priority level requested by a processing instance can be satisfied by a policy of a created first memory instance and, if not, selects another memory instance that can satisfy the requested virtual memory size at the designated usage priority level and swaps out the first memory instance in favor of the other memory instance.

Abstract: The present application relates to a method and a device for extracting information from a pie chart. The method comprises the following steps: detecting each element in a pie chart to be processed and position information thereof, wherein the elements comprise text elements and legend elements; performing text recognition on the detected text elements and legend elements to obtain text information corresponding to the text elements and legend texts included in the legend elements respectively; and obtaining sector information and legend information according to each detected element and position information thereof and the legend texts, and enabling the sector information to correspond to the legend information one by one, wherein the sector information comprises a sector color and a proportion of the sector in the pie chart, and the legend information comprises a legend color and a corresponding legend text thereof.

Abstract: An apparatus and method of denoising a dynamic image is provided. The dynamic image can represent a time-series of snapshot images. The dynamic image is transformed, using a sparsifying transformation, into an aggregate image and a series of transform-domain images. The transform-domain images represent kinetic information of the dynamic images (i.e., differences between the snapshots), and the aggregate image represents static information (i.e., features and structure common among the snapshots). The transform-domain images, which can be approximated using a sparse approximation method, are denoised. The denoised transform-domain images are recombined with the aggregate image using an inverse sparsifying transformation to generate a denoised dynamic image. The transform-domain images can be denoised using at least one of a principal component analysis method and a K-SVD method.

Abstract: A PDL1 block CAR-T transgenic vector for suppressing immune escape includes: AmpR sequence containing ampicillin resistance gene (SEQ ID NO: 1); prokaryotic replicon pUC Ori sequence (SEQ ID NO: 2); virus replicon SV40 Ori sequence (SEQ ID NO: 3); eWPRE enhanced posttranscriptional regulatory element of hepatitis B virus (SEQ ID NO: 11); human EF1a promoter (SEQ ID NO: 12); lentiviral packaging cis-elements for lentiviral packaging; humanized single-chain antibody fragment PDL1scFv1 (SEQ ID NO: 21), PDL1scFv2 (SEQ ID NO: 22), or PDL1scFv3 (SEQ ID NO: 23) of human PDL1; IRES ribosome binding sequence (SEQ ID NO: 25); IL6 signal peptide (SEQ ID NO: 26); human antibody Fc segment (SEQ ID NO: 27); and chimeric antigen receptors of the second or third generation CAR for integrating recognition, transmission and initiation. A preparation method of the PDL1 block CAR-T transgenic vector and an application thereof in a preparation of anti-immune escape drugs.

Abstract: The present application relates to an image segmentation method and device operating on an electronic device. The image segmentation method comprises the following steps: performing deep learning to obtain an FCN (Fully Convolutional Network) model, and calculating the loss using L(pji)=?(1?pji)r log(pji) in the deep learning process; inputting an image to be segmented to the last updated FCN model to obtain the probability that each pixel in the image to be segmented is in each of the categories, and selecting the category corresponding to the maximum probability as the category determined by the image segmentation for the pixel. By improving the loss function of the FCN model, the accuracy of image classification is improved, and chart information in an electronic document is accurately extracted by means of pixel classification.

Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.

Abstract: Provided are a siRNA of the human interleukin 6, a recombinant expression CAR-T vector, and a construction method and a use thereof. The siRNA can be used in the treatment of acute B-cell lymphocytic leukemia with CAR19-T for eliminating or alleviating the symptoms of cytokine release syndrome (CRS), and can also be used for alleviating the CRS symptoms caused by treating tumours with CAR-T and even can also be used for alleviating CRS caused by other types of treatment.

Abstract: A method and apparatus is provided to iteratively reconstruct a computed tomography (CT) image using a spatially-varying content-oriented regularization parameter, thereby achieving uniform statistical properties within respective organs/regions and different statistical properties (e.g., degree of smoothing and noise level) among the respective organs/regions. For example, less smoothing and sharper features/resolution can be applied within a lung region than within a soft-tissue region by using a smaller regularization parameter value in the lung region than in the soft-tissue region. This can be achieved, e.g., using a minimum intensity projection to suppress/eliminate sub-solid nodules in the lung region. The content-oriented regularization parameter can be generated by reconstructing an initial CT image, which is then segmented/classified according to organs and/or tissue type.

Abstract: A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.

Abstract: A deep learning (DL) network reduces artifacts in computed tomography (CT) images based on complementary sparse-view projection data generated from a sparse kilo-voltage peak (kVp)-switching CT scan. The DL network is trained using input images exhibiting artifacts and target images exhibiting little to no artifacts. Another DL network can be trained to perform image-domain material decomposition of the artifact-mitigated images by being trained using target images in which beam hardening is corrected and spatial variations in the X-ray beam are accounted for. Further, material decomposition and artifact mitigation can be integrated in a single DL network that is trained using as inputs reconstructed images having artifacts and as targets material images without artifacts with beam-hardening corrections, etc. Further, the target material images can be transformed using a whitening transform to decorrelate noise.

Abstract: A method and apparatus is provided to reconstruct a computed tomography image using iterative reconstruction combined with variance-reduced acceleration techniques. The acceleration techniques including: ordered subsets, separable quadratic surrogates, and Nesterov's acceleration. Ordered subset iteration is used, but instead of calculating a gradient of the objective function for only one subset per iteration, a full gradient of the total objective function is used. This decreases the variance and mitigates limit cycles. A correction term is calculated as the difference between the subset gradient and the full gradient, and this correction term is used when performing the update of the reconstructed image. The ordered subset can be combined with Nesterov's acceleration. To improve computational efficiency, the full gradient can be calculated once every T iterations, with negligible degradation to the convergence rate.

Abstract: A method and apparatus is provided to iteratively reconstruct a computed tomography (CT) image using a spatially-varying content-oriented regularization parameter, thereby achieving uniform statistical properties within respective organs/regions and different statistical properties (e.g., degree of smoothing and noise level) among the respective organs/regions. For example, less smoothing and sharper features/resolution can be applied within a lung region than within a soft-tissue region by using a smaller regularization parameter value in the lung region than in the soft-tissue region. This can be achieved, e.g., using a minimum intensity projection to suppress/eliminate sub-solid nodules in the lung region. The content-oriented regularization parameter can be generated by reconstructing an initial CT image, which is then segmented/classified according to organs and/or tissue type.