Fujifilm Corporation

Fujifilm Healthcare Corporation

Taiga Goto

Hisashi Takahashi

Isao Takahashi

Kazuma Yokoi

Kana Tanaka

Shinichi Kojima

Kana Kobayashi

Ryosuke Tsuchida

Fuyuhiko Teramoto

Fumito Watanabe

Provided is an X-ray CT apparatus including a learned model generated by acquiring one or more learning data sets from one imaging without increasing exposure of a subject, and performing machine learning using the acquired learning set. The learned model is a model after learning in which a low-quality image is input data and a high-quality image is training data. The low-quality image and the high-quality image are obtained based on the same learning measurement data or learning projection data obtained by logarithmically converting the learning measurement data. The low-quality image is a CT image reconstructed from partial data obtained by dividing the learning measurement data or the learning projection data, and the high-quality image is a CT image obtained by reconstructing the learning projection data.

X-ray CT apparatus and high-quality image generation device

There is provided a PCCT apparatus capable of correcting a band artifact of one material decomposition image and a band artifact of another material decomposition image. The PCCT apparatus obtains projection data divided into plural energy bins by irradiating a subject with X-rays, and includes: a first correction unit that corrects a band artifact of a first material decomposition image among plural material decomposition images created on the basis of the projection data, and calculates a first correction amount that is a correction amount for the band artifact; an energy calculation unit that calculates an average energy of X-rays that transmit the subject; and a second correction unit that corrects the band artifact of a second material decomposition image using a second correction amount that is a correction amount calculated on the basis of the first correction amount and the average energy.

PCCT apparatus

There are a medical image processing apparatus and a medical image processing method that are capable of maintaining the estimation accuracy of noise intensity even though a system noise ratio rises. A medical image processing apparatus includes a difference image generating unit that divides projection data obtained by applying radiation to an examinee to create a difference image between tomographic images reconstructed for every divided piece of projection data; a local variance computing unit that computes local variance in the difference image; and a noise estimation unit that corrects the local variance using a correction function found in advance to estimate noise intensity of a tomographic image of the examinee. The correction function includes system noise.

Medical image processing apparatus and medical image processing method

Growing community of inventors

Kashiwa, Japan

Taiga Goto