Abstract: One aspect of the present disclosure is a method for selective surface functionalization using a single-photon source. The method for selective functionalization using a single-photon source includes: (a) adding a single-photon source to a solution containing a photosensitizer and a monomer; and (b) emitting a single photon from the single-photon source. One aspect of the present disclosure is a selectively functionalized single-photon source prepared by the method.

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: The electronic device for diagnosing dementia with Lewy bodies (DLB) or predicting morbidity to DLB according to the present invention includes a processor that measures cortical thicknesses for a plurality of regions of the brain by using brain MRI images of a normal group and a DLB patient group, generates a DLB pattern matrix by using a residual matrix according to a difference between the average cortical thickness and the cortical thickness for each region, applies a first cortical thickness matrix generated by using a brain MRI image of the subject to the DLB pattern to calculate a first DLB pattern score, and diagnoses the subject as DLB or predicting morbidity to DLB by using the first DLB pattern score.

Abstract: One aspect of the present disclosure is a method for selective surface functionalization using a single-photon source. The method for selective functionalization using a single-photon source includes: (a) adding a single-photon source to a solution containing a photosensitizer and a monomer; and (b) emitting a single photon from the single-photon source. One aspect of the present disclosure is a selectively functionalized single-photon source prepared by the method.

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 method for preparing [18F]fallypride is disclosed, which comprises a first step for trapping a fluorine-18 to a polymer ion exchange cartridge; a second step for extraction of fluorine-18 by inputting low base concentrations: 5.0˜25 ?L of 40% TBAHCO3 or K2.2.2./K2CO3 (5˜25 mg/0.5˜3.0 mg) as a phase-transfer catalyst in a mixture of alcohol/water (1.0/0.2 (v/v)) or alcohol as a solvent into the polymer ion exchange cartridge trapped by the fluorine-18; a third step for preparing a [18F]fallypride product by removing the solvent from the trapped fluorine-18, by inputting tosylate precursor in CH3CN as a solvent into a reactor and by reacting the same for 5˜35 minutes at 50˜120° C.; and a fourth step for preparing a pure [18F]fallypride by purifying the prepared [18F]fallypride product.

Abstract: A method for preparing [18F]fallypride is disclosed, which comprises a first step for trapping a fluorine-18 to a polymer ion exchange cartridge; a second step for extraction of fluorine-18 by inputting low base concentrations: 5.0˜25 ?L of 40% TBAHCO3 or K2.2.2./K2CO3 (5˜25 mg/0.5˜3.0 mg) as a phase-transfer catalyst in a mixture of alcohol/water (1.0/0.2 (v/v)) or alcohol as a solvent into the polymer ion exchange cartridge trapped by the fluorine-18; a third step for preparing a [18F]fallypride product by removing the solvent from the trapped fluorine-18, by inputting tosylate precursor in CH3CN as a solvent into a reactor and by reacting the same for 5˜35 minutes at 50˜120° C.; and a fourth step for preparing a pure [18F]fallypride by purifying the prepared [18F]fallypride product.