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: acquiring noise data by imaging a phantom using a medical imaging apparatus; based on first subject projection data acquired by the imaging performed by a medical image diagnostic modality of a same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noise based on the noise data is added to the first subject projection data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and second subject projection data acquired by the imaging performed by the medical image diagnostic modality.

Abstract: An ultrasound diagnosis apparatus of an embodiment includes storage circuitry and processing circuitry. The storage circuitry stores therein a trained model trained using a first ultrasound signal containing a saturated signal as input data and a second ultrasound signal in which effect of saturation is reduced from the first ultrasound signal, as target data. The processing circuitry inputs a third ultrasound signal containing a saturated signal to the trained model and acquires a fourth ultrasound signal that is output from the trained model and in which effect of saturation is reduced from the third ultrasound signal, to generate the fourth ultrasound signal.

Abstract: A method for harmonic imaging is provided and includes inputting first training ultrasound data, including a fundamental component and a harmonic component, to each of a plurality of teacher models, and training each teacher model with the first training ultrasound as teacher input data and second training ultrasound data, including the harmonic component, as teacher target data; acquiring, for each teacher, corresponding first estimated data output from the teacher model, in response to input of first ultrasound data to the teacher model; selecting a first particular teacher model by evaluating the corresponding first estimated data output from each of the trained teacher models; and training a student model with the first ultrasound data as student input data and the corresponding first estimated data of the selected first particular teacher model as student target data.

Abstract: An apparatus, method, and computer-readable medium having processing circuitry to receive first ultrasound data including at least one harmonic component, and apply the first ultrasound data to inputs of a trained deep neural network model that outputs enhanced ultrasound image data, the deep neural network model having been trained with training data including input ultrasound data and corresponding target ultrasound data having predetermined target features, and output the enhanced ultrasound image data.

Abstract: Provided is a novel active ingredient derived from locust bean and the use thereof. The present invention is a compound represented by the following formula (I) or a salt thereof, or a solvate thereof.

Abstract: An ultrasound diagnosis apparatus according to an embodiment includes processing circuitry. This processing circuitry collects a first ultrasound signal including one or more harmonic components. By executing weighed addition processing where a coefficient distribution is applied to the first ultrasound signal for different directions of two or more dimensions, the processing circuitry generates a second ultrasound signal including components of each order at a ratio different, at a specific frequency, from a ratio among the frequency components included in the first ultrasound signal.

Abstract: Provided is a novel active ingredient derived from locust bean and the use thereof. The present invention is a compound represented by the following formula (I) or a salt thereof, or a solvate thereof.

Abstract: Provided is a novel active ingredient derived from locust bean and the use thereof. The present invention is a compound represented by the following formula (I) or a salt thereof, or a solvate thereof.

Abstract: A method, apparatus, and computer-readable storage medium for controlling exposure/irradiation during a main three-dimensional X-ray imaging scan using at least one spatially-distributed characteristic of a pre-scan/scout scan preceding the main scan. The at least one spatially-distributed characteristic includes (1) a spatially-distributed noise characteristic of the pre-scan and/or (2) a spatially-distributed identification of exposure-sensitive tissue types. The at least one spatially-distributed characteristic can be calculated from images reconstructed from sinogram/projection data and/or from sinogram/projection directly using a neural network.

Abstract: The present disclosure relates to a method for patient-specific optimization of imaging protocols. According to an embodiment, the present disclosure relates to a method for generating a patient-specific imaging protocol, comprising acquiring scout scan data, the scout scan data including scout scan information and scout scan parameters, generating a simulated image based on the acquired scout scan data, deriving a simulated dose map from the generated simulated image, determining image quality of the generated simulated image by applying machine learning to the generated simulated image, the neural network being trained to generate at least one probabilistic quality representation corresponding to at least one region of the generated simulated image, evaluating the determined image quality relative to a image quality threshold and the derived simulated dose map relative to a dosage threshold, optimizing.

Abstract: Particular embodiments may detect, by a core network, a change in network traffic types from a first network traffic type to a second network traffic type. The core network includes one or more network functionality components. Each of the one or more network functionality components is decomposed into multiple service types. The core network may determine several service instances for deployment in response to the change in the network traffic types. Each of the service instances may belong to one of the multiple decomposed service types. The core network may deploy several service instances to one or more server machines of the core network according to a decomposed service type of a respective service instance.

Abstract: A method of processing information acquired by imaging performed by a medical image diagnostic apparatus, the method including but not limited to at least one of (A) acquiring a training image volume including at least one three-dimensional object having an embedded three-dimensional feature having a first cross-sectional area in a first three-dimensional plane; selecting a second cross-sectional area in a second three-dimensional plane containing the embedded three-dimensional feature, wherein the second cross-sectional area is larger than the first cross-sectional area; and training an untrained neural network with an image of the second cross-sectional area generated from the training image volume; and (B) acquiring a first set of training data; determining a first distribution of tissue density information from the first set of training data; generating from the first set of training data a second set of training data by performing at least one of a tissue-density shifting process and a tissue-density sca

Abstract: A method, apparatus, and computer-readable storage medium for controlling exposure/irradiation during a main three-dimensional X-ray imaging scan using at least one spatially-distributed characteristic of a pre-scan/scout scan preceding the main scan. The at least one spatially-distributed characteristic includes (1) a spatially-distributed noise characteristic of the pre-scan and/or (2) a spatially-distributed identification of exposure-sensitive tissue types. The at least one spatially-distributed characteristic can be calculated from images reconstructed from sinogram/projection data and/or from sinogram/projection directly using a neural network.

Abstract: Particular embodiments may detect, by a core network, a change in network traffic types from a first network traffic type to a second network traffic type. The core network includes one or more network functionality components. Each of the one or more network functionality components is decomposed into multiple service types. The core network may determine several service instances for deployment in response to the change in the network traffic types. Each of the service instances may belong to one of the multiple decomposed service types. The core network may deploy several service instances to one or more server machines of the core network according to a decomposed service type of a respective service instance.

Abstract: Particular embodiments may receive a request to perform a task to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may identify service instances for deployment based on the task. Each of the service instances may belong to one of the multiple decomposed service types. The service instances may be deployed to one or more server machines to accomplish the task by the core network based on resource requirements of the service instances and current resource availability of the one or more server machines.

Abstract: A method and system enable to-be-processed medical image data and its corresponding noise characteristic information to be normalized to resemble noise characteristic information of training data used to train at least one neural network for at least one ultrasound data acquisition mode. After normalizing, this processed medical image data is input into the trained neural network for producing output data used for generating cleaner images. Noise characteristic information can be used directly in training a neural network, generating a trained neural network that can handle medical image data with various noise characteristics.

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: acquiring noise data by imaging a phantom using a medical imaging apparatus; based on first subject projection data acquired by the imaging performed by a medical image diagnostic modality of a same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noise based on the noise data is added to the first subject projection data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and second subject projection data acquired by the imaging performed by the medical image diagnostic modality.

Abstract: The present disclosure relates to a method for patient-specific optimization of imaging protocols. According to an embodiment, the present disclosure relates to a method for generating a patient-specific imaging protocol, comprising acquiring scout scan data, the scout scan data including scout scan information and scout scan parameters, generating a simulated image based on the acquired scout scan data, deriving a simulated dose map from the generated simulated image, determining image quality of the generated simulated image by applying machine learning to the generated simulated image, the neural network being trained to generate at least one probabilistic quality representation corresponding to at least one region of the generated simulated image, evaluating the determined image quality relative to a image quality threshold and the derived simulated dose map relative to a dosage threshold, optimizing.

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: Particular embodiments may receive a request to perform a task to a core network by a user device via an access point. The user device may be authenticated by the core network which comprises one or more network functionality components, and each of the one or more network functionality components may be decomposed into multiple service types. The core network may identify service instances for deployment based on the task. Each of the service instances may belong to one of the multiple decomposed service types. The service instances may be deployed to one or more server machines to accomplish the task by the core network based on resource requirements of the service instances and current resource availability of the one or more server machines.