Abstract: An MR signal processing apparatus according to one embodiment includes processing circuitry. The processing circuitry inputs a plurality of MRS signals acquired by MR spectroscopy on the same target to a trained model, and outputs a parameter for MRS reconstruction.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to set a rotation angle of a non-Cartesian trajectory in a k-space on the basis of information related to cyclic movements of a subject to be imaged, to obtain k-space data by rotating the non-Cartesian trajectory at the set rotation angle, and to generate an image by reconstructing the 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.

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 hypercomplex operation device according to an embodiment includes processing circuitry. The processing circuitry acquires data including a hypercomplex number, inputs a parametric function in which a function form for a first component and a function form for a second component that is different from the first component differ from each other, inputs the data including a hypercomplex number, to apply to the parametric function, and thereby outputs output data.

Abstract: A data reconstruction device according to an embodiment of the present disclosure includes processing circuitry. The processing circuitry is configured to generate a medical image of an image type different from that of reference data on the basis of the reference data. The processing circuitry is configured to obtain acquisition data acquired by using an acquisition method different from that used for data to be generated related to generating the reference data. The processing circuitry is configured to generate a reconstruction image of the image type by correcting inconsistency of the medical image with the acquisition data on the basis of the medical image, the acquisition data, and the reference data.

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: According to one embodiment, a medical data processing apparatus includes a processing circuitry. The processing circuitry is configured to: obtain first medical data; generate, based on the first medical data, a derived model from a trained model; obtain second medical data for a same subject as a subject of the first medical data and with an acquisition parameter different from an acquisition parameter of the first medical data; and apply the second medical data to the derived model to generate third medical data.

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 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: 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: 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: 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 magnetic resonance imaging apparatus according to an embodiment includes a processor. The processor acquires a plurality of pieces of k-space data with undersampling in at least one of axes of k-space and in a certain axis different from the axes of k-space. The processor rearranges the pieces of k-space data into a second order different from a first order in which the pieces of k-space data are acquired. The processor performs a reconstruction process on a rearranged k-space data group to generate an image group.

Abstract: According to one embodiment, a medical image diagnostic apparatus includes processing circuitry. The processing circuitry configured to generate initial restored signal data by applying a first restoration function to input signal data corresponding to medical signal data concerning an object, generate first element-wise product signal data by calculating an element-wise product of the initial restored signal data and reliability data representing a degree of degradation included in the input signal data, and generate restored signal data by applying a second restoration function to at least one of the input signal data and the initial restored signal data and the first element-wise product signal data.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to set a rotation angle of a non-Cartesian trajectory in a k-space on the basis of information related to cyclic movements of a subject to be imaged, to obtain k-space data by rotating the non-Cartesian trajectory at the set rotation angle, and to generate an image by reconstructing the k-space data.

Abstract: A magnetic resonance imaging apparatus according to an embodiment includes sequence controlling circuitry and processing circuitry. The sequence controlling circuitry is configured to acquire k-space data by executing a pulse sequence while performing undersampling. The processing circuitry is configured to generate an output target image by generating a folded image by applying a Fourier transform to the k-space data and further unfolding the folded image by performing a process that uses a regularization term. The processing circuitry applies a weight to the regularization term on the basis of whether or not each of the pixels in the output target image is included in an observation target region.

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: 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.