US 12,361,669 B2
Personalized target selection method for non-invasive neuromodulation technology
Xizhe Zhang, Nanjing (CN); and Fei Wang, Nanjing (CN)
Assigned to THE AFFILIATED BRAIN HOSPITAL OF NANJING MEDICAL UNIVERSITY, Nanjing (CN)
Filed by THE AFFILIATED BRAIN HOSPITAL OF NANJING MEDICAL UNIVERSITY, Nanjing (CN)
Filed on Jan. 17, 2025, as Appl. No. 19/026,895.
Application 19/026,895 is a continuation of application No. PCT/CN2024/079519, filed on Mar. 1, 2024.
Claims priority of application No. 202310279372.1 (CN), filed on Mar. 21, 2023.
Prior Publication US 2025/0157175 A1, May 15, 2025
Int. Cl. G06K 9/00 (2022.01); A61B 5/00 (2006.01); A61B 5/055 (2006.01); G06T 7/00 (2017.01); G06T 7/73 (2017.01); G06V 10/25 (2022.01); G06V 10/26 (2022.01); G06V 10/32 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 20/70 (2022.01)
CPC G06V 10/25 (2022.01) [A61B 5/0042 (2013.01); A61B 5/055 (2013.01); G06T 7/0014 (2013.01); G06T 7/73 (2017.01); G06V 10/26 (2022.01); G06V 10/32 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 20/70 (2022.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30242 (2013.01); G06V 2201/031 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A personalized target selection method for a non-invasive neuromodulation technology, comprising:
S10: preprocessing functional magnetic resonance imaging (fMRI) data from MRI scan data of a current patient to acquire preprocessed fMRI brain image feature data;
S20: inputting the preprocessed fMRI brain image feature data into a pre-trained inter-subtype classification model to acquire a subtype label of the current patient and all feature voxels of the subtype label;
S30: preprocessing T1-weighted MRI data of structural magnetic resonance imaging (sMRI) data from the MRI scan data of the current patient to acquire a skull outline; and registering the sMRI and fMRI data to acquire a transformation matrix T; and
S40: performing, based on the transformation matrix T, coordinate transformation on all the feature voxels of the subtype label; calculating a distance between each voxel b on the skull outline and each feature voxel after coordinate transformation; marking a feature voxel with a distance less than a predetermined length as a response feature voxel of b; counting a number of response feature voxels corresponding to each voxel on the skull outline; and selecting a voxel with a largest number of response feature voxels on the skull outline as a candidate target for a corresponding personalized target;
wherein the pre-trained inter-subtype classification model is acquired by training a given classification model based on MRI scan data of a preset number of patients.