US 12,465,294 B2
Local spectral-covariance or local spectral covariance deficits computation and display for highlighting of relevant material transitions in spectral CT and MR
Rafael Wiemker, Kisdorf (DE); Liran Goshen, Pardes-Hanna (IL); Hannes Nickisch, Hamburg (DE); Claas Bontus, Hamburg (DE); Tom Brosch, Hamburg (DE); Jochen Peters, Norderstedt (DE); and Rolf Jürgen Weese, Norderstedt (DE)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Appl. No. 18/038,546
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
PCT Filed Nov. 28, 2021, PCT No. PCT/EP2021/083262
§ 371(c)(1), (2) Date May 24, 2023,
PCT Pub. No. WO2022/117468, PCT Pub. Date Jun. 9, 2022.
Claims priority of application No. 20210977 (EP), filed on Dec. 1, 2020.
Prior Publication US 2024/0090849 A1, Mar. 21, 2024
Int. Cl. A61B 5/00 (2006.01); A61B 5/055 (2006.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01)
CPC A61B 5/7425 (2013.01) [A61B 5/055 (2013.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01)] 11 Claims
OG exemplary drawing
 
1. A computer-implemented method for processing image data of an object of interest, comprising:
receiving the image data of the object of interest;
determining local covariance matrices at a plurality of image positions in at least two mono-energetic images acquired from different spectral channels, wherein each local covariance matrix is a matrix of local variances and local covariances between image intensities at one of the plurality image positions in the at least two mono-energetic images;
determining local multispectral covariance deficits based on a comparison between a product of local variances and a product of local covariances through all spectral channel combinations; and
providing the local covariance deficits as a machine-learning feature for performing material classification.