Korea Advanced Institute of Science and Technology

Samsung Electronics Co., Ltd.

Vatech Ewoo Holdings Co., Ltd.

Vatech Co., Ltd.

Jong Chul Ye

Young Hun Sung

Kyong Hwan Jin

Min Ji Lee

Kye Young Jeong

Eun Hee Kang

Kyung Sang Kim

Jae Duck Jang

Tae Woo Kim

Min Woo Kim

Jae Hak Lee

Jong Ha Lee

Dong Goo Kang

Sung Il Choi

Ju Young Lee

Woong Bae

Ji Young Choi

Yo Seob Han

Ja Wook Gu

Sung Ii Choi

A method for processing an X-ray computed tomography (CT) image using a neural network and an apparatus therefor are provided. An image reconstruction method includes receiving low-dose X-ray CT data, obtaining an initial reconstruction image for the received low-dose X-ray CT data using a predetermined analytic algorithm, and reconstructing a denoised final image using the obtained initial reconstruction image and a previously trained neural network.

Method for processing X-ray computed tomography image using neural network and apparatus therefor

A method of reconstructing a magnetic resonance image includes receiving echo planar imaging (EPI) data, acquiring an even scan line image and an odd scan line image from k-space data of the EPI data, and reconstructing missing portions of the even scan line image and the odd scan line image.

Method of removing ghost artifacts from magnetic resonance image, and magnetic resonance apparatus therefor

Disclosed is an apparatus for computed tomography (CT) image data reconstruction based on motion compensation. The apparatus for computed tomography (CT) data reconstruction based on motion compensation, the apparatus including a storage configured to store projection radiographs and an image processor configured to obtain pairs of opposite projection radiographs from the projection radiographs, and compensate a movement of a radiography subject based on a correlation between the projection radiographs of each pair of opposite projection radiographs.

Apparatus and method for CT data reconstruction based on motion compensation

Disclosed is a method and apparatus for restoring an image. The method and apparatus may detect boundary information associated with a boundary in an image, generate a reproducing kernel used to restore a hole in the image based on the detected boundary information, estimate hole information using the generated reproducing kernel, and restore the hole based on the estimated hole information.

Method and apparatus for restoring image

A method and apparatus for reconstructing an image using a low rank Fourier interpolation scheme are provided. The method may reconstruct information of a k-space domain based on the information of the k-space domain using an imaging apparatus, for example, a magnetic resonance imaging (MRI) apparatus, a computed tomography (CT) apparatus, and a diffraction tomography apparatus. The method may generate a block Hankel matrix based on the information of the k-space domain, and may complete the block Hankel matrix using a low rank matrix completion algorithm, to reconstruct corresponding information of the k-space domain.

Method and apparatus for reconstructing image using low rank fourier interpolation scheme

Disclosed is a method of reconstructing an image. The method of reconstructing an image includes receiving low dose X-ray computed tomography (CT) data, applying an analytic principle to an optimization approach for low dose imaging to transform the low dose X-ray CT data, and removing a noise included in the low dose X-ray CT data to reconstruct a high-quality image.

Development of iterative reconstruction framework using analytic principle for low dose X-ray CT

An X-ray imaging apparatus includes a back projected image generator configured to generate a back projected image with respect to a projected image of a field of view (FOV), and an image restorer configured to obtain frequency components of the back projected image, generate restored images for frequencies based on the frequency components of the back projected image, and generate a restored image with respect to the back projected image by synthesizing the restored images for the frequencies.

X-ray imaging apparatus and control method for the same

An imaging method includes calculating a derivative back projection (DBP) result value using a DBP method with respect to a projection image of a field of view (FOV) inside an object, and reconstructing an image of the FOV by applying a regulation function to the FOV while reconstructing the image of the FOV using the DBP result value.

Image reconstruction unit, X-ray imaging apparatus, and method of reconstructing image

A method and apparatus for reconstructing an image by generating a scatter-corrected sinogram are provided. In the reconstructed image in which initial scattering is not corrected, an emission position of a photon is determined. A graphics processing unit (GPU) calculates migration of the photon using a Monte-Carlo simulation. A 3-dimensional (3D) line of response (LoR) may be calculated through a position in which the photon reaches a detector. The calculated LoR may be converted into a 3D sinogram form and stored in a prompt sinogram and a scatter-corrected sinogram. A final scatter-corrected sinogram may be generated by adjusting a scale of the scatter-corrected sinogram.

Method and apparatus for estimating monte-carlo simulation gamma-ray scattering in positron emission tomography using graphics processing unit

A nano-scale resolution fluorescence microscopy system and a method of obtaining an image using the nano-scale resolution microscopy system, and more particularly, a method and a microscopy system, capable of observing fluorescence probes in high resolution by radiating an irregular diffused light to have an incoherent speckle pattern that has low correlation in an adjacent space are disclosed. According to embodiments of the present invention, a diffraction limit of a fluorescence microscope may be overcome, and a super high resolution image on a nanometer scale may be obtained.

Super-resolution microscopy system using speckle illumination and array signal processing

An apparatus and method of reconstructing a three-dimensional (3D) image from two-dimensional (2D) images are disclosed. Three dimensional (3D) data may be reconstructed in an x-ray generation tube at a limited angle, and repeatedly updated for each pixel. A median from among each pixel of reconstruction data may be selected. Backprojecting may be performed using a search direction weight calculated using a reprojection image and residual image. A 3D image satisfying a Level 1 (L1) norm fidelity and sparsity constraint of the reconstruction data may be reconstructed.

Growing community of inventors

Daejeon, South Korea

Jong Chul Ye