Method and system for automatic analysis of blood vessel structures to identify calcium or soft plaque pathologies

1. A method of identifying a calcium or a soft plaque deposit in a blood vessel using imaging data of the blood vessel, the method comprising:

(a) applying a first threshold to a slice of three-dimensional imaging data of a blood vessel to define voxels above the first threshold;

(b) identifying a maximum intensity of the blood vessel from the defined voxels above the first threshold;

(c) calculating a distance from the identified maximum intensity to a center of the blood vessel;

(d) comparing the calculated distance with a distance threshold;

(e) if the calculated distance is greater than the distance threshold, identifying a calcium deposit;

(f) applying a second threshold to the slice to define voxels below the second threshold;

(g) if a calcium deposit is identified, identifying a soft plaque deposit from the defined voxels below the second threshold;

(h) if a calcium deposit is not identified, determining if the defined voxels below the second threshold have a half-moon shape;

(i) if the defined voxels below the second threshold have a half-moon shape, identifying a soft plaque deposit;

(j) calculating a calcium area of the identified calcium deposit;

(k) calculating a plaque area of the identified soft plaque deposit; and

(l) calculating an obstruction parameter based on the calculated plaque area and on the calculated calcium area.

2. The method of claim 1 , further comprising repeating (a)-(k) for a plurality of slices.

3. The method of claim 2 , further comprising calculating an obstruction parameter based on a sum of the calculated plaque area and the calculated calcium area.

4. The method of claim 3 , wherein calculating the obstruction parameter is further based on the sum divided by a total area of the blood vessel.

5. The method of claim 4 , wherein an obstruction severity is determined based on the calculated obstruction parameter.

6. The method of claim 5 , wherein the obstruction severity is labeled severe if the obstruction parameter is greater than a severity threshold for a plurality of slices.

7. The method of claim 6 , wherein the severity threshold is approximately 0.5.

8. The method of claim 1 , further comprising:

receiving imaging data including a heart region, a pulmonary artery region, an ascending aorta region, and a thoraco-abdominal aorta region of a patient;

identifying the heart region from the imaging data;

analyzing the identified heart region to detect a coronary pathology;

identifying an ascending aorta object from the ascending aorta region of the imaging data;

analyzing the identified ascending aorta object to detect an aortic dissection;

identifying an abdominal aorta object from the thoraco-abdominal aorta region;

analyzing the identified abdominal aorta object to detect an aortic dissection;

identifying a left main pulmonary artery object and a right main pulmonary artery object from the pulmonary artery region of the imaging data;

analyzing the identified left main pulmonary artery object and the identified right main pulmonary artery object to detect a pulmonary embolism; and

generating a report including any detected coronary pathology, any detected aortic dissection, and any detected pulmonary embolism.

9. The method of claim 8 , wherein identifying the heart region comprises:

projecting the received imaging data into a first plane of three dimensions of the received imaging data;

applying a first threshold to the first plane of data, wherein the first threshold is selected to eliminate a first pixel associated with air;

identifying a largest first connected component from the first threshold applied data;

calculating a first center of mass of the identified largest first connected component to define a first coordinate and a second coordinate;

projecting the received imaging data into a second plane of the three dimensions, wherein the second plane is perpendicular to the first plane;

applying a second threshold to the second plane of data, wherein the second threshold is selected to eliminate a second pixel associated with air;

identifying a largest second connected component from the second threshold applied data; and

calculating a second center of mass of the identified largest second connected component to define a third coordinate;

wherein a center of a heart region is defined from the defined first coordinate, the defined second coordinate, and the defined third coordinate; and

further wherein the heart region has a width in each of the three dimensions.