Quantification and analysis of angiography and perfusion
A method to visualize, display, analyze and quantify angiography, perfusion, and the change in angiography and perfusion in real time, is provided. This method captures image data sequences from indocyanine green near infra-red fluorescence imaging used in a variety of surgical procedure applications, where angiography and perfusion are critical for intraoperative decisions.
1. A method for visualizing blood flow in tissue before and after a surgical intervention, the method comprising:
receiving a first image data sequence from indocyanine green near-infrared fluorescence angiography (ICG-NIR-FA) comprising a plurality of fluorescence images of the tissue before the surgical intervention, wherein the first image data sequence encompasses at least one full phase angiography cycle of blood flow through the tissue;
deriving a first average intensity v. time curve from the first image data sequence;
receiving a second image data sequence from ICG-NIR-FA comprising a plurality of fluorescence images of the tissue after the surgical intervention, wherein the second image data sequence encompasses at least one full phase angiography cycle of blood flow through the tissue;
deriving a second average intensity v. time curve from the second image data sequence;
synchronizing the first and second average intensity v. time curves based on peak fluorescence intensities to align at least a portion of the full phase angiography cycle of the first image data sequence with the corresponding portion of the full angiography cycle of the second image data sequence;
determining, in real time, an amount of perfusion change in the tissue based on the synchronized curves; and
displaying, in real time, the synchronized average intensity v. time curves and the determined amount of perfusion change on a display.
2. The method of claim 1 , wherein the tissue is selected from the group consisting of myocardium during revascularization, breast tissue during reconstruction and bowel tissue during anastomosis.
3. The method of claim 1 , wherein the full phase angiography cycle of blood flow comprises an arterial phase, a microvascular phase, and a venous phase.
4. The method of claim 3 , wherein synchronizing the first and second image data sequences comprises correlating, between the first and second image data sequences, at least one of the arterial phase, microvascular phase, and venous phase.
5. The method of claim 1 , wherein synchronizing the first and second image data sequences comprises calculating a correlation coefficient at each alignment time position and synchronizing the first and second image data based on the largest correlation coefficient.
6. The method of claim 1 , wherein the tissue is a blood vessel.
7. The method of claim 6 , wherein the vessel is a coronary artery bypass graft.
8. The method of claim 1 , wherein determining, in real time, an amount of perfusion change in the tissue based on the synchronized curves comprises comparing relative fluorescence intensity between the synchronized first and second image data sequences.
9. The method of claim 1 , wherein determining, in real time, an amount of perfusion change in the tissue based on the synchronized curves comprises estimating a change of baseline fluorescence intensity difference over the synchronized first and second image data sequences, and adjusting the synchronized first and second image data sequences based on the change in baseline fluorescence intensity.
10. The method of claim 1 , wherein determining, in real time, an amount of perfusion change in the tissue based on the synchronized curves comprises estimating the perfusion level based on average fluorescence intensity above a threshold.
11. The method of claim 10 , wherein determining, in real time, an amount of perfusion change in the tissue based on the synchronized curves further comprises estimating the number of image pixels having fluorescence intensity above the threshold.
12. A system for visualizing blood flow in tissue before and after a surgical intervention, comprising:
a processor that
receives a first image data sequence from indocyanine green near-infrared fluorescence angiography (ICG-NIR-FA) comprising a plurality of fluorescence images of the tissue before the surgical intervention, wherein the first image data sequence encompasses at least one full phase angiography cycle of blood flow through the tissue;
derives a first average intensity v. time curve from the first image data sequence;
receives a second image data sequence from ICG-NIR-FA comprising a plurality of fluorescence images of the tissue after the surgical intervention, wherein the second image data sequence encompasses at least one full phase angiography cycle of blood flow through the tissue;
derives a second average intensity v. time curve from the second image data sequence;
synchronizes the first and second image data sequences average intensity v. time curves based on peak fluorescence intensities of average intensity vs. time curves of the first and second image data sequences to align at least a portion of the full phase angiography cycle of the first image data sequence with the corresponding portion of the full phase angiography cycle of the second image data sequence;
determines, in real time, an amount of perfusion change in the tissue based on the synchronized curves; and a display that displays, in real time, the synchronized average intensity v. time curves and the determined amount of perfusion change on a display.
13. The system of claim 12 , wherein the full angiography cycle of blood flow comprises an arterial phase, a microvascular phase, and a venous phase, and the processor synchronizes the first and second image data sequences by correlating, between the first and second image data sequences, at least one of the arterial phase, microvascular phase, and venous phase.
14. The system of claim 12 , wherein the processor synchronizes the first and second image data sequences by calculating a correlation coefficient at each alignment time position and synchronizing the first and second image data based on the largest correlation coefficient.
15. The system of claim 12 , wherein the tissue is a blood vessel and the processor determines, in real time, an amount of perfusion change in the tissue based on the synchronized curves by comparing relative fluorescence intensity between the synchronized first and second image data sequences.
16. The system of claim 12 , wherein the processor determines, in real time, an amount of perfusion change in the tissue based on the synchronized curves at least in part by estimating the perfusion level based on average fluorescence intensity above a threshold.
17. The method of claim 16 , wherein the processor determines, in real time, an amount of perfusion change in the tissue based on the synchronized curves at least in part by estimating the number of image pixels having fluorescence intensity above the threshold.