Abstract: A device for spatially normalizing a medical image includes: an adaptive template generation unit configured such that when a plurality of functional medical images are input to a deep learning architecture, the adaptive template generation unit generates, based on prestored learning data, an adaptive template for spatially normalizing the plurality of functional medical images; a learning unit configured to learn by repeating a process of generating an image from an input functional medical image of a user based on the adaptive template through a generative adversarial network (GAN) and determine authenticity of the generated image; and a spatial normalization unit configured to provide the functional medical image of the user which is spatially normalized based on results of the learning.

Abstract: The present invention relates to a three-dimensional reconstructing method of a 2D medical image. A three-dimensional reconstructing device includes: a communicator for receiving sequential 2D images with an arbitrary slice gap; a sliced image generator for generating at least one sliced image positioned between the 2D images based on a feature point of the adjacent 2D images; and a controller for reconstructing the 2D image into a 3D image by use of the generated sliced image and providing the 3D image.

Abstract: A device for spatially normalizing a medical image includes: an adaptive template generation unit configured such that when a plurality of functional medical images are input to a deep learning architecture, the adaptive template generation unit generates, based on prestored learning data, an adaptive template for spatially normalizing the plurality of functional medical images; a learning unit configured to learn by repeating a process of generating an image from an input functional medical image of a user based on the adaptive template through a generative adversarial network (GAN) and determine authenticity of the generated image; and a spatial normalization unit configured to provide the functional medical image of the user which is spatially normalized based on results of the learning.

Abstract: A high frequency X-ray generator includes an anode voltage generator, a cathode voltage generator and a X-ray tube. The anode voltage generator is configured to generate a first high voltage. The cathode voltage generator is configured to generate a second high voltage having a same waveform as the first high voltage. The second high voltage has a different phase by 180 degrees compared to the first high voltage. The X-ray tube is configured to generate a X-ray by the first high voltage and the second high voltage. The first high voltage is applied through an anode terminal. The second high voltage is applied through a cathode terminal.

Abstract: A high frequency X-ray generator includes an anode voltage generator, a cathode voltage generator and a X-ray tube. The anode voltage generator is configured to generate a first high voltage. The cathode voltage generator is configured to generate a second high voltage having a same waveform as the first high voltage. The second high voltage has a different phase by 180 degrees compared to the first high voltage. The X-ray tube is configured to generate a X-ray by the first high voltage and the second high voltage. The first high voltage is applied through an anode terminal. The second high voltage is applied through a cathode terminal.