Abstract: Provided are an image processing apparatus, a medical imaging apparatus, and an image processing program that remove noise from an image having different noise levels depending on regions in the image at a low calculation cost, and enable high quality according to a preference of a reader. A plurality of image generators receive measurement data or a captured image obtained by an image data acquisition apparatus and generate different images for a same imaging range. An image selection and combination unit selects different image regions from a plurality of images generated by the plurality of image generators according to a predetermined region selection pattern, and generates one image by combining the images of the selected image regions.

Abstract: The present invention is to perform appropriate noise reduction processing on an image having different signal levels or noise levels depending on an imaging condition or a reconstruction condition. A magnetic resonance imaging apparatus according to the invention includes: a measurement unit that receives a nuclear magnetic resonance signal generated in a subject by a receiving coil; an image reconstruction unit that processes the nuclear magnetic resonance signal received by the receiving coil and reconstructs an image of the subject; an SNR spatial distribution calculation unit that calculates spatial distribution of a signal-to-noise ratio of the image using spatial distribution of a noise level and spatial distribution of the signal of the image; and a noise reduction unit that reduces noise from the image based on the spatial distribution of the signal-to-noise ratio.

Abstract: An image diagnosis supporting device includes a model reader that reads an image diagnostic model that outputs a diagnostic result for a diagnostic image that is an input medical image, a storage unit that stores facility data that is a plurality of medical images associated with diagnostic results held in a facility, and an adjuster that adjusts, based on the facility data, the image diagnostic model or the diagnostic image input to the image diagnostic model.

Abstract: To obtain a predictive model that shows a diagnostic prediction result with higher accuracy and high medical validity. A medical imaging apparatus includes an imaging unit that collects an image signal of an inspection target, and an image processing unit that generates first image data from the image signal and performs image processing of the first image data. The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that abstracts the first feature quantity to extract a second feature quantity, a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data, and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value.

Abstract: A small-sized normal mode helical antenna is provided that is not affected by nearby conductors. The antenna is an antenna including a coil constituting a normal mode helical antenna, an area of a cross section perpendicular to an axis of the coil for a single turn at each end of the coil being less than an area of a cross section perpendicular to the axis of the coil near the coil center.

Abstract: The receiving coil device includes one or a plurality of receiving coils configured to cover a head of a subject, a base portion on which the head of the subject is to be placed, a holder portion supported by the base portion, one of the receiving coils being fixed to the holder portion, a mechanism portion configured to bring the receiving coil fixed to the holder portion into close contact with a part of the head, and further, a detection unit configured to detect a physical quantity related to a displacement of the holder portion on the holder portion or the base portion. The physical quantity detected by the detection unit is sent to an MRI apparatus including the receiving coil device.

Abstract: In an image noise reduction process using a CNN, noise can be reduced effectively irrespective of signal levels and noise levels of the image. Noise information and signal level information are calculated from an image inputted in the CNN. Using the calculated information, a normalization factor suitable for the CNN is determined, and normalization of the input image is performed. The noise information is estimated from magnitude of background noise of the input image in the case where the input image is an MRI image. The signal information can be calculated, for example, as a mean value of pixel values of a subject region with respect to an image obtained by dividing the input image by the magnitude of the background noise.

Abstract: Provided is a method for performing reconstruction and noise removal with high accuracy on various undersampling patterns including equidistant undersampling. An image processing unit that processes measurement data acquired by an MRI apparatus performs image reconstruction by using measurement data on respective channels measured in a predetermined undersampling pattern and sensitivity distributions of respective reception coils. At this time, denoising of a reconstructed image and a calculation for maintaining consistency between original measurement data and the measurement data on the respective channels created from denoised images are sequentially processed. Accordingly, image restoration and denoising with high accuracy are possible without depending on the undersampling pattern.

Abstract: The invention provides a technique capable of effectively and appropriately removing noise from various kinds of images including noise and artifacts and images in which a noise pattern changes due to a difference in imaging conditions. Based on a noise removal technique using AI, noise characteristics including artifacts are analyzed for each image, the image is classified based on an analysis result, an optimal neural network for a noise processing is applied for each classification, and the noise and the artifacts are reduced.

Abstract: The present invention is to perform appropriate noise reduction processing on an image having different signal levels or noise levels depending on an imaging condition or a reconstruction condition. A magnetic resonance imaging apparatus according to the invention includes: a measurement unit that receives a nuclear magnetic resonance signal generated in a subject by a receiving coil; an image reconstruction unit that processes the nuclear magnetic resonance signal received by the receiving coil and reconstructs an image of the subject; an SNR spatial distribution calculation unit that calculates spatial distribution of a signal-to-noise ratio of the image using spatial distribution of a noise level and spatial distribution of the signal of the image; and a noise reduction unit that reduces noise from the image based on the spatial distribution of the signal-to-noise ratio.

Abstract: The receiving coil device includes one or a plurality of receiving coils configured to cover a head of a subject, a base portion on which the head of the subject is to be placed, a holder portion supported by the base portion, one of the receiving coils being fixed to the holder portion, a mechanism portion configured to bring the receiving coil fixed to the holder portion into close contact with a part of the head, and further, a detection unit configured to detect a physical quantity related to a displacement of the holder portion on the holder portion or the base portion. The physical quantity detected by the detection unit is sent to an MRI apparatus including the receiving coil device.

Abstract: The present invention provides a technique capable of accurately and automatically setting a region of interest for acquiring biological information based on a biological information signal obtained from a subject placed in an examination space in a non-contact manner. A signal analyzing unit of a medical imaging apparatus uses the biological information signal for each of a plurality of regions included in a predetermined range among signals measured by a biological information measuring apparatus to select the region of interest in which movement of the subject is to be acquired among the plurality of regions. The movement of the subject is calculated using the biological information signal measured by the biological information measuring apparatus from the selected region of interest.

Abstract: An image diagnostic device that obtains a prediction model indicating a higher accuracy diagnosis prediction result includes: an observation unit that collects an image of an examination object; and an image processing unit that generates first image data from the image, and performs image processing of the first image data. The image processing unit is provided with: a feature extraction unit that extracts a first feature from the first image data; a feature transformation unit that converts the first feature to a second feature to be extracted from second image data; and an identification unit that calculates a prescribed parameter value using the converted second feature. The feature extraction unit includes a prediction model learned using a plurality of combinations of the first image data and feature, and the feature transformation unit includes a feature transformation model learned using a plurality of combinations of the first and second features.

Abstract: A learning model learned to provide high image quality of a first image is generated. A first image and a second image are received from the same target, high image quality of the first image is provided by using the learned model, and a first high image quality image is obtained. By using the first high image quality image and the second image as inputs, a high image quality image of the second image having the image quality of the first high image quality image is generated while maintaining contrast of the second image.

Abstract: An image diagnosis supporting device includes a model reader that reads an image diagnostic model that outputs a diagnostic result for a diagnostic image that is an input medical image, a storage unit that stores facility data that is a plurality of medical images associated with diagnostic results held in a facility, and an adjuster that adjusts, based on the facility data, the image diagnostic model or the diagnostic image input to the image diagnostic model.

Abstract: Image processing using a machine learning model is enabled, thereby accurately reducing noise to improve image quality. A medical image is acquired; and it is evaluated whether noise in the medical image exceeds a predetermined reference value. A noise reducer reduces the noise of the medical image that has been determined to include noise that exceeds the reference value. The noise of the medical image is reduced using a machine learning model constructed by collecting a plurality of learning data sets that include an image with noise as input data and an image without noise as output data. The machine learning model includes a plurality of layers that perform convolution on an image that is input, one layer of which includes a filter layer in which a plurality of linear or nonlinear filters are incorporated, and convolution coefficients of the plurality of linear or nonlinear filters are predetermined.

Abstract: Provided are an image processing apparatus, a medical imaging apparatus, and an image processing program that remove noise from an image having different noise levels depending on regions in the image at a low calculation cost, and enable high quality according to a preference of a reader. A plurality of image generators receive measurement data or a captured image obtained by an image data acquisition apparatus and generate different images for a same imaging range. An image selection and combination unit selects different image regions from a plurality of images generated by the plurality of image generators according to a predetermined region selection pattern, and generates one image by combining the images of the selected image regions.

Abstract: To obtain a predictive model that shows a diagnostic prediction result with higher accuracy and high medical validity. A medical imaging apparatus includes an imaging unit that collects an image signal of an inspection target, and an image processing unit that generates first image data from the image signal and performs image processing of the first image data. The image processing unit includes a feature quantity extraction unit that extracts a first feature quantity from the first image data, a feature quantity abstraction unit that abstracts the first feature quantity to extract a second feature quantity, a feature quantity conversion unit that converts the second feature quantity into a third feature quantity extracted by second image data, and an identification unit that uses the converted third feature quantity to calculate a predetermined parameter value.

Abstract: Image processing using a machine learning model is enabled, thereby accurately reducing noise to improve image quality. A medical image is acquired; and it is evaluated whether noise in the medical image exceeds a predetermined reference value. A noise reducer reduces the noise of the medical image that has been determined to include noise that exceeds the reference value. The noise of the medical image is reduced using a machine learning model constructed by collecting a plurality of learning data sets that include an image with noise as input data and an image without noise as output data. The machine learning model includes a plurality of layers that perform convolution on an image that is input, one layer of which includes a filter layer in which a plurality of linear or nonlinear filters are incorporated, and convolution coefficients of the plurality of linear or nonlinear filters are predetermined.

Abstract: An image diagnostic device that obtains a prediction model indicating a higher accuracy diagnosis prediction result includes: an observation unit that collects an image of an examination object; and an image processing unit that generates first image data from the image, and performs image processing of the first image data. The image processing unit is provided with: a feature extraction unit that extracts a first feature from the first image data; a feature transformation unit that converts the first feature to a second feature to be extracted from second image data; and an identification unit that calculates a prescribed parameter value using the converted second feature. The feature extraction unit includes a prediction model learned using a plurality of combinations of the first image data and feature, and the feature transformation unit includes a feature transformation model learned using a plurality of combinations of the first and second features.