Abstract: According to one embodiment, a medical data processing apparatus includes processing circuitry. The processing circuitry acquires medical data, and generates an imaging parameter by inputting the medical data to a trained model, the imaging parameter being a parameter of a medical image diagnostic apparatus with respect to the medical data, the trained model being trained to generate an imaging parameter of the medical image diagnostic apparatus based on medical data.

Abstract: One embodiment, a medical data processing apparatus includes processing circuitry. The processing circuitry obtains medical data relating to a subject, and outputs medical diagnostic data obtained by performing predetermined processing on the medical data, along with standardized medical data based on the medical data, the standardized medical date being standardized for machine learning without performing part or all of the predetermined processing.

Abstract: According to one embodiment, an image data restoration apparatus includes processing circuitry. The processing circuitry acquires a plurality of items of observation data respectively corresponding to a plurality of channels used in a computation; and iteratively executes a computation to optimize a fitting score between first observation data corresponding to a first channel subset and first intermediate image data based on the first observation data, and executes, after the iterative computation satisfies a predetermined condition, a computation to optimize a fitting score between second observation data corresponding to a second channel subset and second intermediate image data based on the second observation data to generate restored image data and outputs the restored image data.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry configured to generate a plurality of reference partial k-space data items based on the filling positions and reference k-space data, generate a plurality of difference k-space data items by taking differences between the partial k-space data items and the reference k-space data items to each of the frames, generate a plurality of difference images by applying the reconstruction processing respectively to the difference k-space data items, and generate a plurality of composite images by combining the reference image with each of the difference images.

Abstract: According to one embodiment, an apparatus includes processing circuitry. The processing circuitry acquires output data from a trained model by entering examination data acquired at an examination, the examination data corresponding to first data, the output data corresponding to second data, into the trained model configured to, based on the first data acquired through transmission of an ultrasound wave for a first number of times, output the second data acquired through transmission of an ultrasound wave for a second number of times that is greater than the first number of times.

Abstract: In one embodiment, an MRI apparatus includes a scanner and processing circuitry. The scanner includes at least two gradient coils. The processing circuitry is configured to cause the scanner to acquire k-space data for correction in a band-shaped two-dimensional k-space along a readout direction, or in a columnar three-dimensional k-space along a readout direction, while changing rotation angles, wherein each of the rotation angles corresponds to the readout direction, generate correction data for correcting an error due to a gradient magnetic field generated by the gradient coils, by using the acquired k-space data for correction, cause the scanner to acquire k-space data for reconstruction, based on a radial acquisition method, while correcting the gradient magnetic field by using the correction data, and generate an image by reconstructing the acquired k-space data for reconstruction.

Abstract: According to one embodiment, a data processing apparatus includes processing circuitry. The processing circuitry acquires input data relating to a processing target including a plurality of data segments corresponding respectively to a plurality of imaging contrasts determined by a first pulse sequence. The processing circuitry generates output data relating to the processing target by applying a trained model to input data relating to the processing target. The processing circuitry outputs output data relating to the processing target.

Abstract: According to one embodiment, a data processing apparatus includes processing circuitry. The processing circuitry is configured to group a plurality of channels of input data based on a physical relationship between the input data to classify the plurality of channels into a plurality of subsets. The processing circuitry is configured to perform convolutional processing of the input data in units of subsets for the plurality of subsets.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes imaging control circuitry and processing circuitry. The imaging control circuitry acquires MR signals in accordance with a first pulse sequence set in an imaging protocol. The processing circuitry determines, during MR signal acquisition according to the first pulse sequence, whether or not additional acquisition of MR signals is necessary based on a determination of image quality based on the acquired MR signals, and if necessity of additional acquisition is determined, adds a second pulse sequence for the additional acquisition to the imaging protocol. The imaging control circuitry acquires MR signals in accordance with the added second pulse sequence. The processing circuitry reconstructs an MR image based on the MR signals acquired through the first pulse sequence and the second pulse sequence.

Abstract: An image processing apparatus according to an embodiment includes conversion circuitry, magnitude image generating circuitry and phase image generating circuitry. The conversion circuitry is configured to convert time-series k-space data into first time-series x-space data, the x-space representing a spatial position. The magnitude image generating circuitry is configured to generate a magnitude image from second time-series x-space data, the second time-series x-space data being acquired by applying a first filter to the first time-series x-space data. The phase image generating circuitry is configured to generate a phase image from third time-series x-space data, the third time-series x-space data being acquired by applying, to the first time-series x-space data, a second filter that is different from the first filter.

Abstract: According to one embodiment, a signal data processing apparatus includes processing circuitry. The processing circuitry obtains first signal data of a plurality of frames that are in time series, selects a processing target frame among the frames and a reference frame that is similar to the processing target frame, inputs the first signal data of the processing target frame and the first signal data of the reference frame to the machine learning model, and outputs second signal data in which a deficient part of the first signal data corresponding to the processing target frame is reduced.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry collects magnetic resonance data in accordance with a pulse sequence. The processing circuitry determines image quality based on the magnetic resonance data. The processing circuitry selects a re-collection pulse sequence when it is determined that the image quality does not satisfy criteria, the re-collection pulse sequence having at least one of a type of sequence or an imaging condition differing from that of the pulse sequence.

Abstract: According to one embodiment, a medical image processing apparatus includes an acquirer, a first processor and a second processor. The acquirer is configured to acquire nonequispaced sampled data from a test object. The first processor is configured to derive product-sums of the nonequispaced sampled data acquired by the acquirer and a plurality of coefficient sets and generate equispaced sampled data including a plurality of elements with which the product-sums derived for the coefficient sets are associated as element values. The second processor is configured to generate a medical image in which at least part of the test object has been imaged through reconstruction basis on the equispaced sampled data generated by the first processor.

Abstract: A photon counting X-ray CT apparatus according to an embodiment includes: data acquiring circuitry, and processing circuitry. The data acquiring circuitry is configured to allocate energy measured by signals output from a photon counting detector in response to incidence of X-ray photons to any of a plurality of first energy bins so as to acquire a first data group as count data of each of the first energy bins. The processing circuitry is configured to determine a plurality of second energy bins obtained by grouping the first energy bins in accordance with a decomposition target material that is a material to be decomposed in a imaging region, allocate the first data group to any of the second energy bins so as to generate a second data group, and use the second data group to generate an image representing a distribution of the decomposition target material.

Abstract: According to one embodiment, a medical information processing apparatus has processing circuitry. The processing circuitry acquires medical data on a subject, acquires numerical data obtained by digitizing an acquisition condition of the medical data, and applies a machine learning model to input data including the numerical data and the medical data, thereby generating output data based on the medical data.

Abstract: According to one embodiment, a medical information processing apparatus includes at least one processor. The processor is configured to execute a program to acquire first data collected from a test object, input the acquired first data into a first model, determine whether second data, which is output from the first model receiving the first data, is to be input into a second model, and in a case where the processor determines that the second data is to be input into the second model, input the second data into the second model, and output third data which is output from the second model receiving the second data.

Abstract: According to one embodiment, medical signal processing apparatus includes a processing circuit. The processing circuit adjust a level of activation of a unit included in a learned model in accordance with classification of an imaging condition for a process target medical signal. The processing circuit generates an output signal by applying the learned model in which the level of activation has been adjusted, to the medical signal.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry configured to generate a plurality of reference partial k-space data items based on the filling positions and reference k-space data, generate a plurality of difference k-space data items by taking differences between the partial k-space data items and the reference k-space data items to each of the frames, generate a plurality of difference images by applying the reconstruction processing respectively to the difference k-space data items, and generate a plurality of composite images by combining the reference image with each of the difference images.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes first processing circuitry and second processing circuitry. The first processing circuitry executes a pulse sequence in a acquisition pattern set such that sampling densities of a plurality of pieces of k space data are made different in accordance with a predetermined imaging parameter when the pieces of k space data having different values of the imaging parameter are acquired while changing the values of the imaging parameter. The second processing circuitry generates an image based on the pieces of k space data.

Abstract: A medical data processing apparatus includes a memory and processing circuitry. The memory stores a learned model including an input layer to which first MR data and second MR data having the same imaging target as the first MR data and an imaging parameter different from the first MR data are inputted, an output layer from which third MR data is output with a missing portion of the first MR data restored, and at least one intermediate layer arranged between the input layer and the output layer. The processing circuitry generates third MR data relating to the subject, from the first MR data serving as a process target and relating to the subject and the second MR data relating to the subject and acquired by an imaging parameter different from the first MR data serving as the process target, in accordance with the learned model.