公开(公告)号：US09672639B1

公开(公告)日：2017-06-06

申请号：US15108394

申请日：2015-07-21

Applicant: Beijing University of Technology

Inventor： Jinchao Feng ,  Kebin Jia ,  Huijun Wei

IPC: G06T11/00 ,  A61B5/00 ,  G06T15/50

CPC classification number: G06T11/005 ,  A61B5/0071 ,  A61B5/0073 ,  A61B5/7225 ,  A61B5/7267 ,  A61B2503/40 ,  A61B2576/00 ,  G06T15/506 ,  G06T2210/41

Abstract: Implementations of the present disclosure relate to methods for reconstruction for bioluminescence tomography based on a method of multitask Bayesian compressed sensing in the field of medical image processing. The method includes the following operations. Firstly the high order approximation model is used to model the law of light propagation in biological tissues, then the inner-correlation among multispectral measurements is researched based on multitask learning method and incorporated into a reconstruction algorithm of bioluminescence tomography as prior information to reduce ill-posedness of BLT reconstruction, and then on this basis, three-dimensional reconstruction of bioluminescent source is realized. Compared with other reconstruction algorithms for BLT, the correlation among multispectral measurements is incorporated into the disclosure and the ill-posedness of BLT reconstruction is reduced. The bioluminescent source can be reconstructed and located accurately using the proposed algorithm, and computational efficiency can be greatly improved.

公开(公告)号：US12089917B2

公开(公告)日：2024-09-17

申请号：US16620860

申请日：2018-12-21

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Jinchao Feng ,  Kebin Jia ,  Qiuwan Sun ,  Zhe Li ,  Zhonghua Sun

IPC: A61B5/00 ,  G06N3/084 ,  G06N5/046 ,  G06N20/00 ,  G06T11/00

CPC classification number: A61B5/0073 ,  A61B5/0075 ,  G06N3/084 ,  G06N5/046 ,  G06N20/00 ,  G06T11/006 ,  G06T2210/41

Abstract: The present disclosure disclose a near-infrared spectroscopy tomography reconstruction method based on neural network which belongs to the field of medical image processing. In the Boltzmann radiation transmission equation, transmission process of light is regarded as absorption and scattering process of photons in medium, and interaction between light and tissue is determined by absorption coefficient, scattering coefficient and phase function of the response scattering distribution. In the transmission, only the particle property of light is taken into account, not the fluctuation of light. Therefore, polarization and interference phenomena related to the fluctuation of light are not considered, and only the energy transmission of light is tracked. The reconstruction method based on BP neural network is used to reconstruct the distribution of optical absorption coefficient, reconstruction results of absorption coefficient distribution can be obtained by calculation. This method can not only reconstruct the absorption coefficient distribution accurately, but also has high computational efficiency.

公开(公告)号：US20170148193A1

公开(公告)日：2017-05-25

申请号：US15108394

申请日：2015-07-21

Applicant: Beijing University of Technology

Inventor： Jinchao Feng ,  Kebin Jia ,  Huijun Wei

IPC: G06T11/00 ,  G06T15/50 ,  A61B5/00

CPC classification number: G06T11/005 ,  A61B5/0071 ,  A61B5/0073 ,  A61B5/7225 ,  A61B5/7267 ,  A61B2503/40 ,  A61B2576/00 ,  G06T15/506 ,  G06T2210/41

Abstract: Implementations of the present disclosure relate to methods for reconstruction for bioluminescence tomography based on a method of multitask Bayesian compressed sensing in the field of medical image processing. The method includes the following operations. Firstly the high order approximation model is used to model the law of light propagation in biological tissues, then the inner-correlation among multispectral measurements is researched based on multitask learning method and incorporated into a reconstruction algorithm of bioluminescence tomography as prior information to reduce ill-posedness of BLT reconstruction, and then on this basis, three-dimensional reconstruction of bioluminescent source is realized. Compared with other reconstruction algorithms for BLT, the correlation among multispectral measurements is incorporated into the disclosure and the ill-posedness of BLT reconstruction is reduced. The bioluminescent source can be reconstructed and located accurately using the proposed algorithm, and computational efficiency can be greatly improved.