US 12,408,836 B2
Method and system for extracting multi-dimensional disconnection network region of symptom mapping
Zixiao Li, Beijing (CN); Tao Liu, Beijing (CN); Hao Liu, Beijing (CN); Lingling Ding, Beijing (CN); and Yongjun Wang, Beijing (CN)
Assigned to Beijing Tiantan Hospital, Capital Medical University, (CN); and Beihang University, (CN)
Filed by Beijing Tiantan Hospital, Capital Medical University, Beijing (CN); and Beihang University, Beijing (CN)
Filed on Feb. 2, 2023, as Appl. No. 18/163,425.
Claims priority of application No. 202210918696.0 (CN), filed on Aug. 2, 2022.
Prior Publication US 2024/0046454 A1, Feb. 8, 2024
Int. Cl. A61B 5/00 (2006.01); G06T 7/00 (2017.01); G06T 7/73 (2017.01)
CPC A61B 5/0042 (2013.01) [A61B 5/4064 (2013.01); A61B 5/7425 (2013.01); G06T 7/0012 (2013.01); G06T 7/73 (2017.01); G06T 2207/10088 (2013.01); G06T 2207/30016 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A method for extracting a multi-dimensional disconnection network region of symptom mapping, comprising:
obtaining a lesion image, and registering the lesion image to a brain standard space;
using brain images of healthy people with multiple ages and a balanced sex ratio as healthy control groups, and obtaining diffusion magnetic resonance images and resting-state functional magnetic resonance images of the healthy control groups;
constructing a structural disconnection weighting network corresponding to lesions using a fiber tracking method according to the lesion image in the brain standard space and the diffusion magnetic resonance images, wherein the fiber tracking method comprises deterministic fiber tracking and probabilistic fiber tracking;
constructing a functional significant disconnection network corresponding to the lesions using a cross-correlation verification method according to the lesion image in the brain standard space and the resting-state functional magnetic resonance images; and
determining the multi-dimensional disconnection network region of the lesions of symptom mapping according to the structural disconnection weighting network and the functional significant disconnection network, wherein the multi-dimensional disconnection network region of the lesions is configured to locate network mapping of a brain lesion in the brain.