公开(公告)号：US12117508B1

公开(公告)日：2024-10-15

申请号：US18744685

申请日：2024-06-16

Applicant: INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES

Inventor： Jie Tian ,  Zechen Wei ,  Hui Hui ,  Liwen Zhang ,  Xin Yang ,  Tao Zhu

IPC: G01R33/12 ,  A61B5/0515 ,  G06T11/00

CPC classification number: G01R33/1276 ,  A61B5/0515 ,  G06T11/006 ,  G06T2211/441

Abstract: A system for reconstructing a magnetic particle image based on adaptive optimization of regularization terms includes: a MPI device for scanning an imaging object to acquire a voltage response signal; a signal processor for constructing a system matrix; and a control processor for reconstructing the magnetic particle image based on an arbitrarily selected regularization term, inputting the reconstructed magnetic particle image to a regularization-term adaptive optimization neural network model for enhancement processing, taking the enhanced magnetic particle image as a first image, and calculating a loss value between the first image and an initial image to acquire a final reconstructed magnetic particle image. The system adopts a neural network model-based automatic learning approach, instead of the approach of manually selecting regularization terms and adjusting parameters, to improve the reconstruction efficiency and quality of the magnetic particle image.

公开(公告)号：US11816767B1

公开(公告)日：2023-11-14

申请号：US18218115

申请日：2023-07-05

Applicant: INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES

Inventor： Jie Tian ,  Zechen Wei ,  Hui Hui ,  Xin Yang ,  Huiling Peng

IPC: G06K9/00 ,  G06T11/00 ,  G06T5/00 ,  G06T5/10 ,  A61B5/0515

CPC classification number: G06T11/006 ,  A61B5/0515 ,  G06T5/002 ,  G06T5/10 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A method and system for reconstructing a magnetic particle distribution model based on time-frequency spectrum enhancement are provided. The method includes: scanning, by a magnetic particle imaging (MPI) device, a scan target to acquire a one-dimensional time-domain signal of the scan target; performing short-time Fourier transform to acquire a time-frequency spectrum; acquiring, by a deep neural network (DNN) fused with a self-attention mechanism, a denoised time-frequency spectrum; acquiring a high-quality magnetic particle time-domain signal; and reconstructing a magnetic particle distribution model. The method learns global and local information in the time-frequency spectrum through the DNN fused with the self-attention mechanism, thereby learning a relationship between different harmonics to distinguish between a particle signal and a noise signal. The method combines the global and local information to complete denoising of the time-frequency spectrum, thereby acquiring the high-quality magnetic particle time-domain signal.

公开(公告)号：US12136146B1

公开(公告)日：2024-11-05

申请号：US18752758

申请日：2024-06-24

Applicant: INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES

Inventor： Jie Tian ,  Zechen Wei ,  Hui Hui ,  Xin Yang

IPC: G06T11/00 ,  A61B5/0515 ,  G06T3/4046

Abstract: A system for reconstructing a magnetic particle image based on a pre-trained model aims to address the influence by point spread function and reduce the computational and time costs, which results in low reconstruction accuracy, or high acquisition time and computational costs for high-precision images. The system is implemented by: generating a simulation system matrix; pre-training a pre-constructed neural network model, and fine-tuning a pre-trained neural network model by performing a downstream task; and inputting real data corresponding to the downstream task into the pre-trained neural network model after fine-tuning, thereby playing an auxiliary role to acquire a high-quality reconstructed MPI image. The system fits the relationship between different harmonics, which helps enhance frequency-domain information. The system has certain universality and can be generalized to a plurality of downstream tasks related to MPI image reconstruction, thereby acquiring high-quality reconstructed images through simple model fine-tuning.