公开(公告)号：US20240393417A1

公开(公告)日：2024-11-28

申请号：US18796233

申请日：2024-08-06

Applicant: ZHEJIANG LAB

Inventor： Yu ZHANG ,  Zhichao WANG ,  Chaoliang SUN ,  Huan ZHANG ,  Haotian QIAN ,  Junyang ZHANG ,  Tianzi JIANG

IPC: G01R33/561 ,  G01R33/56 ,  G01R33/565

Abstract: A method and a system for simulating magnetic resonance echo-planar imaging artifacts. Firstly, for K-space artifacts, K-space data are restored through normal magnetic resonance images, and the K-space data are modified pertinently, and then images with artifacts are reconstructed; for susceptibility artifacts, a susceptibility model is constructed through normal magnetic resonance images, and the magnetic field distribution is reconstructed, and then the images with distortion artifacts are reconstructed. According to the present disclosure, a large number of artifact data sets with different artifact types and artifact degrees can be quickly created through a small number of normal images, thus laying a foundation for the research of identifying artifacts, eliminating or weakening artifacts. A simulation algorithm is designed according to the principle of generation of EPI sequence artifacts, and the obtained images such as stripe artifacts, Moer artifacts, Nyquist artifacts, susceptibility artifacts and the like have good scientificity, accuracy and interpretability.

公开(公告)号：US20250013615A1

公开(公告)日：2025-01-09

申请号：US18595474

申请日：2024-03-05

Applicant: ZHEJIANG LAB

Inventor： Junyang ZHANG ,  Ruonan ZHENG ,  Zhaoxiang WANG ,  Zhichao WANG ,  Chen WANG ,  Yu ZHANG ,  Tianzi JIANG

IPC: G06F16/21

Abstract: An information recommendation method, an apparatus, a device, and a medium based on embedding table compression are provided. The method includes: determining, based on a preset compression ratio, to-be-compressed features and non-compressed features in a to-be-compressed embedding table of a recommendation model, generating a similarity index matrix based on a similarity between the to-be-compressed features and the uncompressed features; generating an index dictionary based on the similarity index matrix; substituting a first feature mapping dictionary based on the index dictionary to generate a second feature mapping dictionary, wherein the first feature mapping dictionary is generated based on a data set; and acquiring to-be-recommended data, replacing features in the to-be-recommended data according to the second feature mapping dictionary, inputting replaced features into the recommendation model, and outputting a prediction result.