Longitudinal display of coronary artery calcium burden
The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.
1. A method, comprising:
receiving, by one or more processors, one or more frames including image data of a blood vessel segment;
detecting, by the one or more processors, an arterial feature in each of the one or more frames, wherein the arterial feature is an under-expansion risk;
scoring, by the one or more processors, the arterial feature in each of the one or more frames;
determining, by the one or more processors using a machine learning model, the under-expansion risk, wherein the machine learning model compares pre-percutaneous intervention (“PCI”) data and post-PCI data for a plurality of cases;
identifying, by the one or more processors based on the arterial feature score, a region of interest; and
outputting, by the one or more processors based on the arterial feature score, a representation of the blood vessel segment including a visual indication of the score for the region of interest.
2. The method of claim 1 , wherein the detected arterial feature is a calcium burden.
3. The method of claim 1 , wherein the under-expansion is a stent under-expansion risk.
4. The method of claim 3 , wherein the visual indication of the stent under-expansion risk is a color-coded indication.
5. The method of claim 4 , wherein the color-coded indication is based on a severity of the under-expansion risk.
6. The method of claim 1 , wherein scoring the arterial feature in each of the one or more frames is based on a sliding window measure.
7. The method of claim 1 , wherein the visual indication of the score is a bar parallel to a longitudinal axis of the representation of the blood vessel and extends along the region of interest.
8. The method of claim 7 , wherein the bar is color-coded based on the arterial feature score.
9. The method of claim 8 , wherein the arterial feature score is a stent under-expansion risk.
10. A device, comprising:
one or more processors configured to:
receive one or more frames including image data of a blood vessel segment; detect an arterial feature in each of the one or more frames, wherein the arterial feature is an under-expansion risk;
score the arterial feature in each of the one or more frames;
determine, using a machine learning model, the under-expansion risk, wherein the machine learning model compares pre-percutaneous intervention (“PCI”) data and post-PCI data for a plurality of cases;
identify, based on the arterial feature score, a region of interest; and
output, based on the arterial feature score, a representation of the blood vessel segment including a visual indication of the score for the region of interest.
11. The device of claim 10 , wherein the detected arterial feature is a calcium burden or an under-expansion risk.
12. The device of claim 11 , wherein the under-expansion is a stent under-expansion risk.
13. The device of claim 10 , wherein scoring the arterial feature in each of the one or more frames is based on a sliding window measure.
14. The device of claim 10 , wherein the visual indication of the score is a bar parallel to a longitudinal axis of the representation of the blood vessel and extends along the region of interest.
15. A non-transitory computer-readable medium storing instructions executable by one or more processors for performing a method, comprising:
receiving one or more frames including image data of a blood vessel segment;
detecting an arterial feature in each of the one or more frames, wherein the arterial feature is an under-expansion risk;
scoring the arterial feature in each of the one or more frames;
determining, using a machine learning model, the under-expansion risk, wherein the machine learning model compares pre-percutaneous intervention (“PCI”) data and post-PCI data for a plurality of cases:
identifying, based on the arterial feature score, a region of interest; and
outputting, based on the arterial feature score, a representation of the blood vessel segment including a visual indication of the score for the region of interest.