Abstract: According to one embodiment, a solid-state imaging device includes a plurality of pixels, a plurality of sampling switches, a plurality of sample-and-hold circuits, and a plurality of output switches. The plurality of pixels are arranged at least in a column direction. The plurality of sampling switches are configured to sample signals outputted from the pixels belonging to columns in parallel. The plurality of sample-and-hold circuits are configured to sample and hold signals outputted from the plurality of sampling switches. The plurality of output switches are configured to output signals stored in the plurality of sample-and-hold circuits at predetermined timing.

Abstract: A medical data processing method according to an embodiment includes: outputting second spectral data by inputting first spectral data related to an examined subject imaged by a spectral medical imaging apparatus to a trained model configured to generate, on the basis of the first spectral data, the second spectral data having less noise than the first spectral data and a higher resolution than the first spectral data. The first spectral data in the medical data processing method according to the embodiment corresponds to medical data obtained by performing a spectral scan on the examined subject. The trained model in the medical data processing method according to the embodiment is configured to perform a noise reducing process and a super-resolution process on the first spectral data.

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 medical image processing apparatus according to an embodiment includes processing circuitry configured to acquire a radiographic image of a subject and acquire a post-processing image with reduced steak artifacts by applying a model that reduces streak artifacts to the radiographic image, wherein the model is generated by machine learning that uses, as a training data pair, a first radiographic image and a second radiographic image based on artifact generation processing for generating streak artifacts.

Abstract: According to one embodiment, a solid-state imaging device includes a plurality of pixels, a plurality of sampling switches, a plurality of sample-and-hold circuits, and a plurality of output switches. The plurality of pixels are arranged at least in a column direction. The plurality of sampling switches are configured to sample signals outputted from the pixels belonging to columns in parallel. The plurality of sample-and-hold circuits are configured to sample and hold signals outputted from the plurality of sampling switches. The plurality of output switches are configured to output signals stored in the plurality of sample-and-hold circuits at predetermined timing.

Abstract: A medical image processing method includes obtaining a first set of projection data by performing, with a first CT apparatus including a first detector with a first pixel size, a first CT scan of an object in a first imaging region of the first detector; obtaining a first CT image with a first resolution by reconstructing the first set of projection data; obtaining a processed CT image with a resolution higher than the first resolution by applying a machine-learning model for resolution enhancement to the first CT image; and displaying the processed CT image or outputting the processed CT image for analysis. The machine-learning model is obtained by training using a second CT image based on a second set of projection data acquired by a second CT scan of the object in a second imaging region with a second CT apparatus including a second detector with a second pixel size.

Abstract: A medical data processing method according to an embodiment includes inputting first medical data relating to a subject imaged with a medical image capture apparatus to a learned model to configured to generate second medical data having lower noise than that of the first medical data and having a super resolution compared with the first medical data based on the first medical data to output the second medical data.

Abstract: A solid-state imaging device according to an embodiment includes a first charge detector, a first output circuit and a pulse generator. The first charge detector includes a plurality of first pixels and is configured to detect charges accumulated in the plurality of first pixels. The first output circuit is configured to amplify the charges detected by the first charge detector and output the charges as an output signal. The pulse generator is configured to generate a sampling pulse to extract a charge signal from the output signal during a period different from a signal output period in which the output signal is outputted.

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: 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 solid-state imaging device according to an embodiment includes a first charge detector, a first output circuit and a pulse generator. The first charge detector includes a plurality of first pixels and is configured to detect charges accumulated in the plurality of first pixels. The first output circuit is configured to amplify the charges detected by the first charge detector and output the charges as an output signal. The pulse generator is configured to generate a sampling pulse to extract a charge signal from the output signal during a period different from a signal output period in which the output signal is outputted.

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 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: According to one embodiment, a solid-state imaging device includes a plurality of pixels, a plurality of charge detection parts, a signal line, an output circuit, and a plurality of individual current sources. The pixels are arranged in a first direction. Each of the pixels includes a photoelectric conversion part. The charge detection parts convert signal charges of photoelectric conversion parts of the pixels to voltages. The signal line is commonly connected to the charge detection parts. The output circuit amplifies a signal output of the signal line. The individual current sources are provided for each of the pixels, and are connected to the signal line.

Abstract: Techniques are provided that enable displaying of medical images that depict cyclic motions in the subject. An X-ray CT system scans, with X-rays, the subject whose targeted region is experiencing a cyclic motion and acquires detection data. This X-ray CT system comprises a reconstruction processor, a moving image creator, and a display controller. The reconstruction processor generates a plurality of sets of volumetric data based on a plurality of sets of detection data that have been acquired during one cycle of the cyclic motion. The moving-image creator creates a moving image that shows the cyclic motion, on the basis of at least a part of the plural sets of volumetric data. The display controller superposes the moving image over an image based on the volumetric data and displays these images on the display unit.

Abstract: According to one embodiment, a solid-state imaging device includes a plurality of pixels, a plurality of charge detection parts, a signal line, an output circuit, and a plurality of individual current sources. The pixels are arranged in a first direction. Each of the pixels includes a photoelectric conversion part. The charge detection parts convert signal charges of photoelectric conversion parts of the pixels to voltages. The signal line is commonly connected to the charge detection parts. The output circuit amplifies a signal output of the signal line. The individual current sources are provided for each of the pixels, and are connected to the signal line.

Abstract: According to one embodiment, an X-ray computed tomography apparatus includes an X-ray tube, an X-ray detector, a projection data generation circuitry, a setting circuitry, and a scan start timing determination circuitry. The setting circuitry configured to set a region of interest on a slice image generated by a first scan for the object. The scan start timing determination circuitry configured to determine, based on a plurality of projection data values of interest corresponding to the region of interest out of the projection data values generated by a second scan at a dose lower than that in the first scan, a timing of terminating the second scan and starting a third scan at a dose higher than that in the second scan.

Abstract: According to one embodiment, a medical imaging diagnosis apparatus includes a top-plate, a bed, a first gantry, a second gantry and a moving assembly. The top-plate places a subject thereon. The bed supports the top-plate. The first gantry includes an X-ray generator and an X-ray detector which revolve around the top-plate. The second gantry includes a gamma ray detector which detects gamma rays emitted from the subject. The moving assembly moves, based on a first position indicative of a center position of an effective view field in the first gantry and a second position indicative of a center position of an effective view field in the second gantry, the top-plate relative to the second position.

Abstract: A medical imaging apparatus includes a reconstruction unit, a ROI setting unit, and a controller. The reconstruction unit receives projection data that is based on radiation detected by a detector, divides the projection data into a plurality of subsets, and apply a reconstruction process to the subsets by successive approximation to successively generate images. The ROI setting unit sets a ROI in the images. The controller obtains a variation value indicating a change in image quality from the images, and, when the variation value reaches a predetermined value, instructs the reconstruction unit on a new subset count less than the number of the subsets to sequentially reconstruct subsets as many as the new subset count into an image. The reconstruction unit assigns a weight to the projection data based on the ROI, and divides the projection data into the subsets based on the weight.

Abstract: According to one embodiment, an X-ray computed tomography apparatus includes an X-ray tube, an X-ray detector, a projection data generation circuitry, a setting circuitry, and a scan start timing determination circuitry. The setting circuitry configured to set a region of interest on a slice image generated by a first scan for the object. The scan start timing determination circuitry configured to determine, based on a plurality of projection data values of interest corresponding to the region of interest out of the projection data values generated by a second scan at a dose lower than that in the first scan, a timing of terminating the second scan and starting a third scan at a dose higher than that in the second scan.