Hitachi, Ltd.

Fujifilm Healthcare Corporation

Yoshimi Noguchi

Masahiro Ogino

Takuma Shibahara

Tomofumi Nishiura

Hisaaki Ochi

Tetsuhiko Takahashi

Yoshitaka Bito

Suguru Yokosawa

Yukio Kaneko

Masaru Murashita

Hisako Nagao

Takenori Murase

Kenta Sakuragi

Nao Ito

Kuniaki Harada

Toshinori Maeda

Maki Kuwayama

Takashi Toyomura

Kenta Takanohashi

Yuuko Nagase

Nobuyasu Inoue

An imaging unit of an MRI apparatus performs imaging of a positioning image of a subject including a spine; a first imaging that images a cross section including the spine and extending along a longitudinal direction of the spine; and a second imaging that images a cross section in a direction of traversing the spine. An automatic cross-section position setting unit detects a specific tissue of the spine using a scout image or an image including the spine acquired in the first imaging step, performs a matching process between the detected specific tissue of the spine and a spine model, and calculates an imaging cross-section position of the second imaging based upon a specific tissue position of the spine specified by matching, thereby performing automatic setting.

Magnetic resonance imaging apparatus with auto-positioning function, method for controlling magnetic resonance imaging apparatus, and program for auto-setting of imaging plane

To provide a medical image processing apparatus and a medical image processing method enabling a reduction in burden of re-imaging and re-interpretation of a medical image. The medical image processing apparatus for processing a medical image includes: a detection section that detects, from the medical image, a candidate region which is a region including a lesion candidate or target tissue, and that calculates an identification score for the candidate region; a designated region acquisition section that acquires a designated region which is a region designated by an operator; and a redetection section that calculates an identification score for the designated region based on the designated region and multiple predefined regions which are regions used in a process of detecting the candidate region.

Medical image processing apparatus and medical image processing method

In medical examination of breast cancer, a lesion computer-aided detection is performed in real time and with high accuracy, and a burden on a medical worker is reduced. A medical image processing apparatus that processes a medical image includes: a detection unit configured to detect a lesion candidate region; a validity evaluation unit configured to evaluate validity of the lesion candidate region by using a normal tissue region corresponding to the detected lesion candidate region; and a display unit configured to determine display content to a user by using an evaluation result.

Medical image processing apparatus, and medical imaging apparatus

To acquire a higher-quality image with high-speed imaging in an MRI apparatus or the like to which compressed sensing is applied. Included are: an observation unit that does not observe, when any one of two points being point-symmetric with respect to the origin is observed, the other point in observation of a high frequency component of a K-space of the MRI apparatus; and a reconstruction unit that reconstructs an image from a component of the K-space observed by the observation unit. The reconstruction process of the reconstruction unit includes an image correction process based on an observation pattern of the observation unit.

Image capturing apparatus and method

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.

Medical imaging apparatus, medical image processing device, and medical image processing program

Provided is a medical imaging device capable of obtaining a medical image with high image quality in a short time by using a novel image reconstruction technique in which DL is applied to medical image processing and by significantly reducing a total imaging time including an image reconstruction time. The medical imaging device classifies a medical image into any of a predetermined plurality of classes, selects an optimal one or a plurality of restorers from a plurality of restorers respectively corresponding to the plurality of classes according to classification results, and reconstructs the medical image using the selected restorers. The medical image is divided into, for example, a plurality of patches, and is reconstructed for each patch and integrated. The restorer can include a CNN.

Medical imaging device and medical image processing method using a neutral network

In a medical imaging device that performs compressed sensing, it is possible to shorten a reconstruction time while maintaining image quality. The medical imaging device includes an image reconstructing unit that reconstructs an image by performing an iterative optimization operation of compressed sensing and a base selecting unit that selects a base transform which is used for the optimization every iteration. The base selecting unit may select a base on the basis of a predetermined base sequence or may select a base using weighting factors which are set for the bases in advance. The invention is applied to a medical imaging device such as an MRI apparatus, an ultrasonic imaging apparatus, or a CT apparatus.

Medical imaging device, image processing method, and program

In a diagnostic imaging device, such as an MRI device, correction processing improves an image to be a high-quality image and an imaging time is shortened. In the diagnostic imaging device, a noise reduction unitreduces a noise in observation data acquired with an observation unitand converted into an image, and the image correction unitcorrects the noise reduced data by correction processing that uses visual characteristics of human. The image correction unitseparates the noise reduced data into a broad luminance component and a local variation component, and generates a correction level map using the broad luminance component. Correcting the noise reduced data using this correction level map and the local variation component acquires a high-quality image that is competent in the clinical field.

Diagnostic imaging device and image acquisition method

An ultrasonography generation device includes an ultrasonic wave transceiver (), a user inputter () which inputs input by an operator, a monitor () capable of displaying an image, an image processor () which generates tomographic image data of a fetus and the placenta based on signals acquired from the ultrasonic wave transceiver and sets a region of interest including a region between the fetus and the placenta according to the input from the inputter when the tomographic image data is displayed on the display, a 3D-ROI corrector () which corrects the region of interest using the region of interest set by the operator and the tomographic image data and determines validity of the corrected region of interest, and a presentation part () which presents the determination result from 3D-ROI corrector. The ultrasonography generation device generates a 3D image of the fetus using the corrected region of interest.

Ultrasound diagnostic image generating device and method

A three-dimensional region of interest (ROI) is established with a high degree of accuracy, by a simple method without increasing a burden on the operator, in generating a three-dimensional projected image from medical volume data according to rendering, achieving more efficient interpretation of three-dimensional image and streamlining of diagnostic flow, with the use of the diagnostic image generation apparatus. An energy map is generated on a predetermined tomographic plane, assuming a preset start point as a reference and searching for a path that minimizes the energy, and then the path is set as a boundary of the three-dimensional ROI. The start point may be decided on the basis of the boundary inputted by a user, or the user may set the start point. The user may be allowed to adjust the boundary having been set. The boundary may also be determined on another plane orthogonal to the predetermined tomographic plane.

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Tokyo, Japan

Yoshimi Noguchi