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 medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to generate, from a medical image, a plurality of feature target regions for specifying a target of image segmentation. The processing circuitry is configured to specify a target region indicating the region in which the target is present, on a basis of the plurality of feature target regions. The processing circuitry is configured to perform, in the target region, the image segmentation on the target.

Abstract: An ultrasound diagnosis apparatus includes: a transmission and reception circuitry configured to perform, via an ultrasound probe, a first ultrasound scan on a first region in a subject who has a contrast agent injected and a second ultrasound scan on at least a part of a second region in the subject overlapping with the first region, the second ultrasound scan including transmitting and receiving two types of ultrasound waves of which one or both of amplitude levels and phases are different from each other; and processing circuitry configured to generate a blood flow image corresponding to one frame by implementing a Doppler method on the basis of a data sequence including reception data obtained from the first ultrasound scan performed multiple times in mutually the same position within the first region to have the second ultrasound scan performed in-between and to generate a contrast-enhanced image based on a result of the second ultrasound scan performed at least one time.

Abstract: The present disclosure relates to a real-number-based neural network operating in combination with a complex-number-based neural network to perform image processing (e.g., using phase-based medical images). In one embodiment, a method includes, but is not limited to, applying, to inputs of a first trained neural network trained to process real-number-based images, first image data generated from real-number-based measurements obtained by imaging a subject; applying, to inputs of a second trained neural network trained to process complex-number-based images, second image data generated from complex-number-based measurements obtained by imaging the subject; and combining a first output of the first trained neural network and a second output of the second trained neural network to produce a combined image, based on the first image data and the second image data.

Abstract: An image processing apparatus according to the present embodiment comprises processing circuitry configured to acquire a medical image regarding a spine including a plurality of vertebrae, receive, from a user, an input operation of editing a spine number allocated to each of the vertebrae in the medical image, and correct the spine number other than the spine number edited based on the received input operation.

Abstract: An apparatus for incremental motion correction in medical imaging. The apparatus for motion correction in magnetic resonance imaging includes processing circuitry configured to estimate an intermediate image from a first section of k-space, the first section of the k-space corresponding to acquisition time points within a magnetic resonance scan of a subject, the corresponding acquisition time points within the magnetic resonance scan being associated with shots of the k-space determined to have minimal motion, estimate motion parameters of a second section of the k-space using the estimated intermediate image, combine data from the first section of the k-space with data from the second section of the k-space according to the estimated motion parameters, and reconstruct the combined data of the k-space to generate a final image.

Abstract: A photon counting X-ray computed tomography apparatus according to an embodiment includes a photon counting detector and a time digital converter. The photon counting detector is configured to output a pulse corresponding to photons included in an X-ray; and the time digital converter is configured to obtain time information corresponding to timing at which the photons were detected, on the basis of the pulse.

Abstract: According to one embodiment, an ultrasonic diagnostic apparatus includes processing circuitry. The processing circuitry acquires position information relating to an ultrasonic probe and an ultrasonic image. The processing circuitry acquires ultrasonic image data which is obtained by transmission and reception of ultrasonic from the ultrasonic probe at a position where the position information is acquired, the ultrasonic image data being associated with the position information. The processing circuitry executes associating between a first coordinate system relating to the position information and a second coordinate system relating to medical image data. The processing circuitry executes image alignment between an ultrasonic image based on the associated ultrasonic image data and a medical image based on the medical image data.

Abstract: According to one embodiment, an ultrasonic diagnostic apparatus includes an ultrasonic probe and processing circuitry. The processing circuitry causes the ultrasonic probe to perform a first scan and second scan in a same scan sequence, wherein the first scan is for calculating a first index value based on a displacement of tissue by a shear wave that propagates through a living body, the second scan for calculating a second index value indicating an attenuation of a reflected wave signal of an ultrasonic wave applied into the living body. The processing circuitry generates a first image based on the first index value, and generates a second image based on the second index value. The processing circuitry causes a display to simultaneously display the first image and the second image.

Abstract: According to one embodiment, a medical image diagnostic apparatus includes processing circuitry. The processing circuitry configured to acquire first volume data and second volume data relating to a subject, set a first region of interest for the first volume data, set a second region of interest for the second volume data independently from setting of the first region of interest, perform a cropping process on the first volume data and the second volume data based on information representing the first region of interest and the second region of interest, and generate display image data based on the first volume data and the second volume data that have been subjected to the cropping process.

Abstract: An apparatus for medical text processing comprises processing circuitry configured to: obtain a trained model, wherein the trained model is trained to classify medical text documents with a medical classification code; apply the trained model to at least one medical text document to obtain weightings for text terms included in the at least one medical text document, wherein the weightings are associated with the medical classification code; and use the weightings to perform a searching or indexing process.

Abstract: A processing circuit according to an embodiment includes a plurality of logic gates in combination each of which is configured to probabilistically determine, based on signal values of one or two or more input nodes and output nodes at a certain time, signal values at at least one of the input nodes and the output nodes at a subsequent time, in which the processing circuit controls the signal values based on a relationship to be satisfied between at least some nodes of the input nodes and the output nodes.

Abstract: A medical information processing apparatus of an embodiment includes processing circuitry. The processing circuitry acquires biometric data regarding a subject. The processing circuitry identifies reference data regarding a first feature amount on the basis of the biometric data. The processing circuitry acquires a second feature amount by inputting the first feature amount included in the biometric data into a model capable of mutually converting the first feature amount and the second feature amount. The processing circuitry derives pseudo-feature data regarding the first feature amount simulated by inputting the second feature amount into the model. The processing circuitry determines reliability scores with respect to the biometric data on the basis of the reference data and the pseudo-feature data.

Abstract: A magnetic resonance imaging apparatus sound-proofing tube according to an embodiment is a sound-proofing tube for a magnetic resonance imaging apparatus and is formed by using a composite material including a fiber material and a resin material. In the magnetic resonance imaging apparatus sound-proofing tube, the fiber material forms a woven structure by being woven therein. The woven structure is formed so as to continuously extend over at least one round of the lateral face of the sound-proofing tube.

Abstract: An X-ray CT apparatus of an embodiment includes a rotating unit and processing circuitry. The rotating unit is configured to rotatably hold the X-ray tube and the X-ray detector. The processing circuitry is configured to switch energy bin sets related to discrimination of photons detected by the X-ray detector between a first energy bin set and a second energy bin set in which at least a part of an energy bin combination is different from the first energy bin set during one rotation of the rotating unit and collect correction data for each of the first energy bin set and the second energy bin set during one rotation in response to switching between the first energy bin set and the second energy bin set.

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: A medical image processing apparatus comprises processing circuitry configured to: receive a selection of a preset transfer function that sets colors used in volume rendering, wherein the preset transfer function is selected for use in rendering at least one target material; determine a first transfer function relating to transmission or extinction color, wherein the determining of the first transfer function is based on the preset transfer function; generate first rendering data based on transmission or extinction color by using the first transfer function; and determine a second transfer function relating to reflection color, wherein the determining of the second transfer function is based on the first rendering data, wherein the first transfer function and second transfer function are for use in global illumination rendering.

Abstract: A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a medical image subject to a labeling process. The processing circuitry is configured to receive a labeling step in a labeling task performed on the medical image. The processing circuitry is configured, while the labeling step in the labeling task is received, to analyze a local characteristic of a target structure serving as a labeling target in the medical image. The processing circuitry is configured to generate a usable tool set corresponding to the labeling task performed on the medical image, on the basis of the local characteristic.

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: An analyzing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain pieces of Doppler image data in a time series rendering a blood flow in an analysis target formed in a blood vessel. The processing circuitry is configured to analyze the pieces of Doppler image data in the time series and to calculate an index value based on temporal changes in a first blood flow signal intensity level in a first region of interest including the blood flow in the analysis target.