公开(公告)号：US11918335B2

公开(公告)日：2024-03-05

申请号：US17010870

申请日：2020-09-03

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Haifeng Wang ,  Dong Liang ,  Hairong Zheng ,  Xin Liu ,  Shi Su ,  Zhilang Qiu

IPC: A61B5/055 ,  G01R33/28 ,  G01R33/48

CPC classification number: A61B5/055 ,  G01R33/28 ,  G01R33/48

Abstract: A magnetic resonance imaging method includes: obtaining three-dimensional under-sampling data of a target object based on a first three-dimensional magnetic resonance imaging sequence; obtaining a three-dimensional point spread function based on the three-dimensional under-sampling data or a two-dimensional mapping data of the target object; obtaining a sensitivity map of the target object based on the data collected by three-dimensional low-resolution complete sampling; performing imaging reconstruction to the three-dimensional under-sampling data based on the three-dimensional point spread function and the sensitivity map to obtain a reconstructed magnetic resonance image. The first three-dimensional magnetic resonance imaging sequence has a first sinusoidal gradient field on a phase direction and a second sinusoidal gradient field on a layer selection direction. 0-order moments of the first and the second three-dimensional magnetic resonance imaging sequences are 0. A phase difference between the first and the second three-dimensional magnetic resonance imaging sequence is π/2.

公开(公告)号：US11579229B2

公开(公告)日：2023-02-14

申请号：US16473806

申请日：2018-07-24

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Haifeng Wang ,  Dong Liang ,  Sen Jia ,  Xin Liu ,  Hairong Zheng

IPC: G01V3/00 ,  G01R33/561 ,  G01R33/56 ,  G01R33/58

Abstract: There are provided a parallel rapid imaging method and device based on a complex number conjugate symmetry of multi-channel coil data and nonlinear GRAPPA image reconstruction, and a medium. The imaging method includes: obtaining virtual conjugate coil data by expanding the actual multi-channel coil data; combining actual multi-channel coil data and virtual multi-channel coil data to obtain a linear data term and a nonlinear data term; calibrating weighting factors of the linear data term and the nonlinear data term by using combined low-frequency full-sampling data (margins of the low-frequency full-sampling data includes parts of high-frequency data); reconstructing data which is under-sampled in a high-frequency region according to the calibrated weighting factors; fusing the low-frequency full-sampling data and the reconstructed data for the high-frequency region.