US 11,867,783 B2
Apparatus and method for analysing functional MRI data
Michael Hutel, London (GB); and Sebastien Ourselin, London (GB)
Assigned to King's College London, London (GB)
Appl. No. 17/058,714
Filed by King's College London, London (GB)
PCT Filed May 31, 2019, PCT No. PCT/GB2019/051495
§ 371(c)(1), (2) Date Nov. 25, 2020,
PCT Pub. No. WO2019/229454, PCT Pub. Date Dec. 5, 2019.
Claims priority of application No. 1808904 (GB), filed on May 31, 2018.
Prior Publication US 2021/0208224 A1, Jul. 8, 2021
Int. Cl. G06K 9/00 (2022.01); G01R 33/48 (2006.01); G06T 7/30 (2017.01); A61B 5/00 (2006.01); G01R 33/56 (2006.01); G01R 33/565 (2006.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01)
CPC G01R 33/4806 (2013.01) [A61B 5/4064 (2013.01); G01R 33/5608 (2013.01); G01R 33/56509 (2013.01); G06T 7/0014 (2013.01); G06T 7/30 (2017.01); G06T 11/006 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30016 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A method for analysing a functional magnetic resonance imaging (fMRI) scan representing a time-sequence, comprising t time points, of images comprising v voxels, where each scan can be represented by a data matrix X∈custom charactert×v the method comprising:
modelling the scan data X as the convolution of a neural activation time course N∈custom character+t×v and a haemodynamic filter Ψ, and performing an inverse operation to estimate N from X and Ψ; and
decomposing the neural activation time course N into multiple brain network components by representing N as the product of a first matrix H defining, for each component, a spatial map of the voxels belonging to that component, and a second matrix W defining, for each component, the time sequence of activation of that component during the scan.