Fujifilm Corporation

Fujifilm Healthcare Corporation

Atsuro Suzuki

Tomoki Amemiya

Yukio Kaneko

Toru Shirai

Suguru Yokosawa

Hiroki Shoji

Chizue Tanaka

Keisuke Nishio

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.

Image processing device, image processing method, and magnetic resonance imaging device

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.

Medical image processing device, medical imaging apparatus, and noise reduction method for medical image

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.

Magnetic resonance imaging apparatus, image processing apparatus, and image processing method

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.

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Chiba, Japan

Atsuro Suzuki