Non-invasive measurement of fibrous cap thickness
A system including a hierarchical analytics framework that can utilize a first set of machine learned algorithms to identify and quantify a set of biological properties utilizing medical imaging data is provided. System can segment the medical imaging data based on the quantified biological properties to delineate existence of perivascular adipose tissue. The system can also segment the medical imaging data based on the quantified biological properties to determine a lumen boundary and/or determine a cap thickness based on a minimum distance between the lumen boundary and LRNC regions.
1. A system comprising a processor and a non-transient storage medium including processor executable instructions implementing an analyzer module including a hierarchical analytics framework configured to:
utilize a first set of machine learned algorithms to identify and quantify a set of biological properties utilizing medical imaging data, wherein the biological properties include LRNC regions of a blood vessel, wherein the medical imaging data is computer tomography (CT) data;
segment the medical imaging data based on the quantified biological properties to determine a lumen boundary; and
determine a cap thickness based on a minimum distance between the lumen boundary and LRNC regions, wherein the determining the minimum distance between the lumen boundary and the LRNC regions comprises:
creating first vector between a first voxel in the lumen and first voxel in the LNRC,
determining a first distance of the first vector,
creating a second vector between a second voxel point in the lumen and a second voxel in the LNRC, wherein the second voxel point in the lumen is different then the first voxel in the lumen and the second voxel in the LRNC is different then the second voxel in the LRNC,
determining a second distance of the second vector, and
assigning the minimum distance to the smallest of the first distance and the second distance.
2. The system of claim 1 wherein segmenting the medical imaging data further comprises segmenting the medical imaging data into an outer wall boundary.
3. The system of claim 2 further wherein the analyzer module is configured to partition a lumen and an outer wall based on the segmented lumen boundary and outer wall boundary into one or more vessel boundaries.
4. The system of claim 3 wherein the biological properties include calcified regions, LRNC regions, intra-plaque regions, matrix regions, or any combination thereof.
5. A method for a hierarchical analytics framework, the method comprising:
utilizing a first set of machine learned algorithms to identify and quantify a set of biological properties utilizing medical imaging data, wherein the biological properties include LRNC regions of a blood vessel, wherein the medical imaging data is computer tomography (CT) data; and
segmenting the medical imaging data based on the quantified biological properties to determine a lumen boundary; and
determining a cap thickness based on a minimum distance between the lumen boundary and LRNC regions, wherein the determining the minimum distance between the lumen boundary and the LRNC regions comprises:
creating first vector between a first voxel in the lumen and first voxel in the LNRC,
determining a first distance of the first vector,
creating a second vector between a second voxel point in the lumen and a second voxel in the LNRC, wherein the second voxel point in the lumen is different then the first voxel in the lumen and the second voxel in the LRNC is different then the second voxel in the LRNC,
determining a second distance of the second vector, and
assigning the minimum distance to the smallest of the first distance and the second distance.
6. The method of claim 5 wherein segmenting the medical imaging data further comprises segmenting the medical imaging data into an outer wall boundary.
7. The method of claim 6 further wherein the analyzer module is configured to partition a lumen and an outer wall based on the segmented lumen boundary and outer wall boundary into one or more vessel boundaries.
8. The method of claim 7 wherein the biological properties include calcified regions, LRNC regions, intra-plaque regions, matrix regions, or any combination thereof.