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 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: Digital microfluidic device includes a first substrate and a second substrate aligned generally parallel to each other with a gap defined therebetween in side view. At least one of the first substrate and the second substrate include a first electrode array, a second electrode array spaced from and in electrical communication with the first electrode array, and a first interstitial area defined between the first electrode array and the second electrode array. At least one of the first electrode array and the second electrode array is configured to generate electrical actuation forces within an actuation area to urge at least one droplet within the gap along the at least one of the first substrate and the second substrate. At least one spacer is disposed in the first interstitial area to maintain the gap between the first substrate and the second substrate.

Abstract: Digital microfluidic and analyte detection device includes a first substrate and a second substrate aligned generally parallel to each other with a gap defined therebetween in side view. At least one of the first and second substrates has an electrode array configured to generate electrical actuation forces to urge at least one droplet within the gap along the at least one of the first substrate and the second substrate. The electrode array has a plurality of electrodes defining an electrode array area in plan view. At least one of the first substrate and the second substrate has a well array defining a well array area in plan view. The well array area is bounded within the electrode array area and overlapping a portion of each of the plurality of electrodes. The well array area overlaps less than 75% of the electrode array area in plan view.

Abstract: System for storing and dispensing liquid in a digital microfluidic chip includes a plurality of reservoir electrodes defining a reservoir having an outlet and a first end opposite the outlet, the reservoir configured to be in fluidic communication with at least one device electrode proximate the outlet, the at least one device electrode and at least one of the plurality of reservoir electrodes configured to generate electrical actuation forces to dispense at least one droplet from the reservoir through the outlet. The plurality of reservoir electrodes include a first reservoir electrode proximate the first end, a reservoir outlet electrode proximate the outlet, and at least one intermediate reservoir electrode disposed between the first electrode and the reservoir outlet electrode.

Abstract: The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.

Abstract: Systems and methods for determining location-based bid modifier suggestions include determining a content placement cost based in part on a likelihood of a user that has entered a physical establishment completing a transaction, an average transaction amount for the establishment, and an expected return on investment (ROI). A location-based bid modifier may be determined using the computed cost and a base bid amount. In some implementations, the location-based bid modifier may also be based on a probability model that models the probability of the user visiting the establishment.

Abstract: Provided are a human PD-1 knockdown siRNA, a recombinant expression CAR-T vector, a preparation method thereof, and an application of the same. A PD-1 knockdown siRNA expression cassette and an siRNA expression product thereof can be applied to a CAR-T therapy of multiple myeloma (MM) for eliminating or alleviating a tumor immune escape mechanism, and in the suppression of an immune escape mechanism in a CAR-T therapy of a tumor, such as pancreatic cancer, brain glioma, and myeloma.

Abstract: An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: on the basis of first subject data acquired by the imaging performed by the medical image diagnostic apparatus, acquiring noise data in the first subject data; on the basis of second subject data acquired by the imaging performed by a medical image diagnostic modality same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noises based on the noise data are added to the second subject data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and third subject data acquired by the imaging performed by the medical image diagnostic modality.

Abstract: A method and apparatus uses multi-material decomposition of three or more material components to generate material-component images from spectral images reconstructed from spectral computed tomography data. In three-component material decomposition e.g., the Mendonça method is used for multi-material decomposition when the attenuation values satisfy an assumed volume fraction condition (i.e., for a given voxel, the attenuation values are within a triangle having vertices given by unit volume fractions of three respective material components). However, when the volume fraction condition fails (e.g., the attenuation values are outside the triangle), either a shortest-Hausdorff-distance method or a closest-edge method is used for multi-material decomposition. For example, the attenuation values of the voxel are projected onto a lower-dimensional space (e.g., the space of a closest edge) and decomposed into a pair/single material component(s) of the lower-dimensional space.

Abstract: A method and apparatus is provided that uses a deep learning (DL) network to correct projection images acquired using an X-ray source with a large focal spot size. The DL network is trained using a training dataset that includes input data and target data. The input data includes large-focal-spot-size X-ray projection data, and the output data includes small-focal-spot-size X-ray projection data (i.e., smaller than the focal spot of the input data). Thus, the DL network is trained to improve the resolution of projection data acquired using a large focal spot size, and obtain a resolution similar to what is achieved using a small focal spot size. Further, the DL network is can be trained to additional correct other aspects of the projection data (e.g., denoising the projection data).

Abstract: An information processing method and an electronic device are provided. The method includes: acquiring first biometric information and second biometric information of a predetermined object located in a target area by an image acquisition using a synchronous acquisition process; and performing a biometric authentication on the predetermined object by combining the first biometric information and the second biometric information.

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: The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.

Abstract: Systems and methods for determining location-based bid modifier suggestions include determining a content placement cost based in part on a likelihood of a user that has entered a physical establishment completing a transaction, an average transaction amount for the establishment, and an expected return on investment (ROI). A location-based bid modifier may be determined using the computed cost and a base bid amount. In some implementations, the location-based bid modifier may also be based on a probability model that models the probability of the user visiting the establishment.

Abstract: A method and apparatus uses multi-material decomposition of three or more material components to generate material-component images from spectral images reconstructed from spectral computed tomography data. In three-component material decomposition e.g., the Mendonça method is used for multi-material decomposition when the attenuation values satisfy an assumed volume fraction condition (i.e., for a given voxel, the attenuation values are within a triangle having vertices given by unit volume fractions of three respective material components). However, when the volume fraction condition fails (e.g., the attenuation values are outside the triangle), either a shortest-Hausdorff-distance method or a closest-edge method is used for multi-material decomposition. For example, the attenuation values of the voxel are projected onto a lower-dimensional space (e.g., the space of a closest edge) and decomposed into a pair/single material component(s) of the lower-dimensional space.

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: An OCTS-based CAR-T vector for treating pancreatic cancer and malignant mesothelioma includes lentiviral skeleton plasmid, human EF1? promoter (SEQ ID NO: 14), OCTS chimeric receptor structural domain, and PDL1 single-chain antibody; the OCTS chimeric receptor structural domain consists of CD8 leader chimeric receptor signal peptide (SEQ ID NO: 15), PDL1 single-chain antibody light chain VL (SEQ ID NO: 16), PDL1 single-chain antibody heavy chain VH (SEQ ID NO: 17), mesothelin single-chain antibody light chain VL (SEQ ID NO: 18), mesothelin single-chain antibody heavy chain VH (SEQ ID NO: 19), antibody Inner-Linker (SEQ ID NO: 20), single-chain antibody Inter-Linker (SEQ ID NO: 21), CD8 Hinge chimeric receptor linker (SEQ ID NO: 22), CD8 Transmembrane chimeric receptor transmembrane domain (SEQ ID NO: 23), TCR chimeric receptor T cell activation domain (SEQ ID NO: 26) and chimeric receptor co-stimulator domain.

Abstract: A method and apparatus are provided that use deep learning (DL) networks to reduce noise and artifacts in reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) images. DL networks are used in both the sinogram and image domains. In each domain, a detection network is used to (i) determine if particular types of artifacts are exhibited (e.g., beam-hardening artifact, ring, motion, metal, photon-starvation, windmill, zebra, partial-volume, cupping, truncation, streak artifact, and/or shadowing artifacts), (ii) determine whether the detected artifact can be corrected through a changed scan protocol or image-processing techniques, and (iii) determine whether the detected artifacts are fatal, in which case the scan is stopped short of completion. When the artifacts can be corrected, corrective measures are taken through a changed scan protocol or through image processing to reduce the artifacts (e.g.

Abstract: A method and apparatus is provided to reconstruct a computed tomography image using iterative reconstruction (IR) that is accelerated using various combinations of ordered subsets, conjugate gradient, preconditioning, resetting/restarting, and/or gradient approximation techniques. For example, when restarting criteria are satisfied the IR algorithm can be reset by setting conjugate-gradient parameters to initial values and/or by changing the number of ordered subsets. The IR algorithm can be accelerated by approximately calculating the gradients, by using a diagonal or Fourier preconditioner, and by selectively updating the preconditioner based on the regularization function. The update direction and step size can be calculated using the preconditioner and a surrogate function, which is not necessarily separable.