Fujifilm Healthcare Corporation

Hitachi, Ltd.

Taiga Gotou

Hisashi Takahashi

Koichi Hirokawa

Isao Takahashi

Fuyuhiko Teramoto

Kazuma Yokoi

Keisuke Yamakawa

Fumito Watanabe

Shinichi Kojima

Mariko Yamamoto

A material decomposition apparatus for performing decomposition of a material in an object. The apparatus includes a data storage section for storing correction data preliminarily generated by decomposing one of three or more materials into the other two materials, a data input section to which radiation data of the object is inputted, the radiation data being divided into a plurality of energy levels, and a decomposition processing section for repeatedly performing two-material decomposition for decomposition of the other two materials of the three or more materials using the radiation data at different energy levels and the correction data to perform decomposition of the inside of the object into the three or more materials.

Material decomposition apparatus, PCCT apparatus, and material decomposition method

A medical image processing apparatus and a medical image processing method capable of improving structural component detectability with respect to a noise component in a medical image. The medical image processing apparatus includes: a separation unit that separates the medical image into a first component including a structural component as a main component and a second component including a noise component as a main component; a modulation unit that modulates the second component with a spatial frequency; and a correction unit that generates a corrected image based on the first component and the modulated second component.

Medical image processing apparatus and medical image processing method

A medical image processing apparatus reducing noise of a medical image acquired includes a smoothing unit forming a smoothed image of the medical image, a route forming unit forming a route that is a pixel group positioned continuously in the smoothed image and fulfills a route condition, and a noise reducing unit extracting a pixel group corresponding to the route in the medical image and reducing noise of the medical image based on the extracted pixel group.

Noise is reduced for a medical image for which noise cannot be quantified by a general-purpose image quality evaluation index. An image processor has a preprocessor that generates input images including an original image and one or more images with reduced noise compared with the original image; and a noise reduction processor outputs an image, which is obtained by reducing noise from the original image based on the input images, by applying a learned network. The learned network used in the noise reduction processor is constructed by performing deep learning using a plurality of learning sets in which one or more of a medical image including noise, a noise-reduced image obtained by performing noise reduction processing on the medical image, and an intermediate image obtained during the noise reduction processing are input images and a correct image is obtained based on the input images an output image.

Image processing apparatus, image processing method, and X-ray CT apparatus

There are provided a medical image processing apparatus and a medical image processing method making it possible to reduce metal artifacts with no loss of a detailed structure in a metallic region. The medical image processing apparatus is configured to reconstruct a tomographic image from projection data of a subject in which metal material is included and includes a projection space correction section which corrects the projection data and thereby generates corrected projection data, an image space correction section which corrects the tomographic image by using the corrected projection data, and a coefficient setting section which sets a projection space coefficient which is used in the projection space correction section and an image space coefficient which is used in the image space correction section.

The present invention provides an image reconstruction apparatus and an image reconstruction method, both of which achieve reduction in a granular noise and a streak-like artifact both contained in a reconstruction image, and a clear structure boundary as well. The image reconstruction apparatus configured to generate a reconstruction image includes a noise reduction section that generates a streak reduction image as an image obtained by reducing a streak-like artifact in the reconstruction image, a noise reduction ratio acquisition section that acquires a first noise reduction ratio as a noise reduction ratio to be used for reducing a granular noise while retaining a structure boundary in the reconstruction image, and a second noise reduction ratio as a noise reduction ratio to be used for generating the streak reduction image, and an image addition section that adds an addition image to be generated based on the first noise reduction ratio and the second noise reduction ratio to the streak reduction image.

Image reconstruction apparatus and image reconstruction method

A medical image processing device and a corresponding method are provided that can extract and enhance a signal in a medical image. A medical image processing device that applies image processing to a medical image, the device including an acquiring unit that acquires an original image, a first extraction unit that extracts a local bias component image, a creating unit that decomposes a difference image between the original image and the local bias component image into at least two frequency bands and creates a low-frequency component image formed of a component in the lowest frequency band, a second extraction unit that extracts a local bias component low-frequency image that is a local bias component of the low-frequency component image, and a computing unit that multiplies the local bias component low-frequency image by a gain, adds to the local bias component image, and computes a contrast enhanced image.

Medical image processing device and medical image processing method

An image is generated where an edge of a structure in a subject is sharpened with reducing streak artifacts, within a short amount of time. X-rays at multiple angles are applied to the subject placed in imaging space, and a distribution of X-rays strength passing through the subject is detected to obtain raw data associated with multiple views. After smoothing the raw data associated with multiple views, image reconstruction is performed to generate a smoothed image of a predetermined region to be imaged in the imaging space. In the smoothed image, more intense sharpening process is applied to pixels in a region of a central part of the subject, than the pixels in a region of a peripheral part of the subject.

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Tokyo, Japan

Taiga Gotou