Abstract: A data generation apparatus according to one embodiment includes processing circuitry. The processing circuitry receives a relative value of molecules in a first region as input data. The processing circuitry applies, to the input data, a function having predetermined coefficients to be learned. The processing circuitry computes a relative value of the molecules in a second region to be output data from the function. The processing circuitry outputs the output data.

Abstract: An image reconstruction apparatus according to an embodiment includes a processing circuitry configured to reconstruct at least one image from a plurality of pieces of k-space data acquired in a time direction. The processing circuitry performs image estimation calculation that reconstructs estimated images sharing the k-space data in the time direction, and adaptation calculation that adapts the estimated images to prior knowledge in the time direction.

Abstract: An image generating apparatus according to the embodiment includes processing circuitry. The processing circuitry acquires MR data acquired in read-out directions including a first read-out direction and a second read-out direction intersecting the first read-out direction, filter sensitivity distributions corresponding to the read-out directions and indicating distributions of sensitivity of a low-pass filter, and coil sensitivity distributions corresponding to coil elements used to acquire the MR data. The processing circuitry generates synthesis sensitivity distributions for the respective read-out directions by synthesizing the filter sensitivity distributions and the coil sensitivity distributions for the respective read-out directions. The processing circuitry generates an MR image based on the synthesis sensitivity distributions and MR data.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry is configured to design a pulse sequence including a plurality of refocus pulses and a plurality of crusher gradient fields, in such a manner that a first crusher gradient field to be applied after a first refocus pulse group and a second crusher gradient field to be applied before a second refocus pulse group after the first refocus pulse group overlap one another at least partially. The processing circuitry is configured to acquire magnetic resonance spectroscopic signals by executing the designed pulse sequence.

Abstract: A physical simulation apparatus includes element reception circuitry, a storage device, storage control circuitry, and simulation circuitry. The element reception circuitry successively receives sequence elements of a hardware control sequence that is divided according to a division rule on a transmission side. The storage device stores the received sequence elements. The storage control circuitry reads out the sequence elements stored in the storage device, in a case where a separately determined condition is satisfied. The simulation circuitry executes a physical simulation, based on the read-out sequence elements, and computes a predicted computation value of a signal value that a control target apparatus of the sequence elements collects.

Abstract: According to one embodiment, a magnetic resonance imaging apparatus includes sequence control circuitry and processing circuitry. The sequence control circuitry performs under-sampled data acquisition whose sample points are located at an equal interval in k-space and acquires k-space frames. The processing circuitry generates a plurality of k-space frames related to a plurality of time resolutions based on the k-space frames. In each of the plurality of k-space frames, the sample points are located at an equal interval, and the interval differs for each of the plurality of k-space frames. The processing circuitry generates a time-series image based on the plurality of k-space frames.

Abstract: According to one embodiment, a data compression apparatus includes processing circuitry. The processing circuitry generates reconstructed data by performing reconstruction processing on data. The processing circuitry generates decompressed reconstructed data by performing the reconstruction processing on decompressed data obtained by decompressing compressed data that is generated by performing compression processing on the data. The processing circuitry determines a parameter relating to a compression ratio of the data based on comparison between the reconstructed data and the decompressed reconstructed data.

Abstract: A data processing apparatus includes processing circuitry configured to: acquire data that are based on a magnetic resonance signal acquired from a specific region of an object, the data being acquired for detecting a chemical shift of substance; and perform relation calculation between a designated chemical shift arbitrarily designated in a predetermined range of the chemical shift and displacement amount of a position of the specific region displaced due to the designated chemical shift, as position correction data.

Abstract: According to one embodiment, a data processing apparatus includes processing circuitry. The processing circuitry generates two or more magnetic resonance spectroscopy (MRS) pulse sequences in which different frequency bands are selected as suppression targets. The processing circuitry obtains multiple MRS signals acquired by the two or more MRS pulse sequences. The processing circuitry estimates a relative amount of each molecule included in a measurement target region from the MRS signals, based on a co-occurrence of a frequency profile that depends on a molecule type.

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, 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: According to one embodiment, a pseudo-data generation apparatus includes processing circuitry. The processing circuitry acquires one or more pieces of partial observation data that form part of whole observation data. The processing circuitry generates pseudo-whole observation data by inputting the one or more pieces of partial observation data to a function, the pseudo-whole observation data being pseudo-data of the whole observation data. The function is optimized by training so that partial observation data for training and pseudo-partial observation data for training resemble each other, the pseudo-partial observation data for training being obtained by converting the pseudo-whole observation data for training.

Abstract: According to one embodiment, an inference apparatus includes a processing circuit configured to: obtain processing target data; and calculate inference data by applying a trained neural network to the processing target data, wherein the trained neural network includes an ensemble activation function for each of a plurality of unit network structures configured to convert an input vector element to an output vector element, the ensemble activation function being configured to execute a calculation based on a plurality of activation functions and a plurality of mixing coefficients respectively corresponding to the activation functions.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes sequence control circuitry and processing circuitry. The sequence control circuitry performs multi-frame acquisition where FOVs (Field Of Views) of at least two acquired frames are overlapped in a first direction. Then, based on the multi-frame acquisition performed by the sequence control unit, the processing unit generates data regarding the components in the first direction of flow of a fluid.

Abstract: A magnetic resonance imaging apparatus according to one embodiment includes sequence control circuitry and processing circuitry. The sequence control circuitry performs a first data acquisition for chemical shift measurement and a second data acquisition for either chemical shift measurement or MR imaging, which differs from chemical shift measurement, on the same subject under certain conditions that differ between those data acquisitions. The processing circuitry performs medical data classification on the subject based on first MR data obtained through the first data acquisition and second MR data obtained through the second data acquisition.

Abstract: A medical data processing apparatus according to one embodiment includes processing circuitry. The processing circuitry obtains a compressed channel of data generated by compressing a plurality of first medical channels of data defined by first domain representation and respectively corresponding to a plurality of components, via an intermediate channel of data defined by second domain representation. The processing circuitry decodes the compressed channel of data to a second medical channel of data defined by the first domain representation based on a conversion process from the plurality of first medical channels of data to the compressed dataset.

Abstract: A medical image processing apparatus includes processing circuitry. The processing circuitry generates a plurality of first medical images by applying a plurality of first machine learning models having different elements to a set of raw data, or applying a first machine learning model to a set of raw data a plurality of times while changing elements. The processing circuitry outputs a second medical image and a first reliability relating to the second medical image based on the first medical images.

Abstract: A data processing device according to an embodiment generates output data by inputting, to a learned model, as input data, first input data of a complex value and second input data that is data related to a complex phase, the learned model having been trained using, as input data, first input data for learning of a complex value obtained based on collected data collected by a medical image diagnostic device and second input data for learning that is data related to a complex phase of the collected data, and using, as output data, output data for learning.

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

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.