Identification of potential perfusion defects
View Patent ↗A method is disclosed for identifying potential perfusion defects in a tissue region through which blood flows in an object under investigation, based on at least one high-energy image data set covering the tissue region and at least one low-energy image data set covering the tissue region. A virtual contrast medium image data set is established based on the high-energy image data set and the low-energy image data set. Furthermore, first candidate perfusion regions within the virtual contrast medium image data set, and second candidate perfusion defect regions within a further image data set based on the high-energy image data set and/or the low-energy image data are detected, the first candidate perfusion defect regions being compared with the second candidate perfusion defect regions and, based on the comparison, potential perfusion defects being identified. Further disclosed are a corresponding image analysis apparatus and a computed tomography system.
1. A method for the identification of potential perfusion defects in a tissue region through which blood flows in an object under investigation, based on at least one high-energy image data set covering the tissue region and at least one low-energy image data set covering the tissue region, the method comprising:
establishing a virtual contrast medium image data set based on the at least one high-energy image data set and the at least one low-energy image data set;
detecting first candidate perfusion defect regions within the virtual contrast medium image data set;
detecting second candidate perfusion defect regions within a further image data set based on the at least one high-energy image data set and the at least one low-energy image data set; and
comparing the first candidate perfusion defect regions with the second candidate perfusion defect regions and identifying potential perfusion defects based on the comparing.
2. The method of claim 1 , wherein a partial region of the tissue region is only identified as a potential perfusion defect if said partial region is detected as a first candidate perfusion defect region in the virtual contrast medium image data set and as a second candidate perfusion defect region in the further image data set.
3. The method of claim 2 , wherein the detection of the first candidate perfusion defect regions within the virtual contrast medium image data set and the detection of the second candidate perfusion defect regions within the further image data set take place independently of one another.
4. The method of claim 3 , wherein at least one of the detection of the first candidate perfusion defect regions within the virtual contrast medium image data set and the detection of the second candidate perfusion defect regions within the further image data set takes place automatically.
5. The method of claim 2 , wherein at least one of the detection of the first candidate perfusion defect regions within the virtual contrast medium image data set and the detection of the second candidate perfusion defect regions within the further image data set takes place automatically.
6. The method of claim 1 , wherein the detection of the first candidate perfusion defect regions within the virtual contrast medium image data set and the detection of the second candidate perfusion defect regions within the further image data set take place independently of one another.
7. The method of claim 6 , wherein at least one of the detection of the first candidate perfusion defect regions within the virtual contrast medium image data set and the detection of the second candidate perfusion defect regions within the further image data set takes place automatically.
8. The method of claim 1 , wherein at least one of the detection of the first candidate perfusion defect regions within the virtual contrast medium image data set and the detection of the second candidate perfusion defect regions within the further image data set takes place automatically.
9. The method of claim 1 , wherein, for identification of image points which is attributable to a potential perfusion defect, a threshold value analysis is carried out.
10. The method of claim 1 , wherein at least one of a detected first candidate perfusion defect region and a detected second candidate perfusion defect region are segmented.
11. The method of claim 1 , wherein the image data of the virtual contrast medium image data set and corresponding image data of the further image data set are output simultaneously to a display device.
12. The method of claim 11 , wherein on overlaid output, at least one of already detected first candidate perfusion defect regions and already detected second candidate perfusion defect regions are marked.
13. The method of claim 11 , wherein the image data of the virtual contrast medium image data set and corresponding image data of the further image data set are overlaid on one another.
14. The method of claim 1 , wherein the defined tissue region comprises a myocardial tissue.
15. The method of claim 1 , wherein initially at least one high-energy image data set of the tissue region of the object under investigation and at least one low-energy image data set of the tissue region or the object under investigation are recorded by way of X-ray measurements with different X-ray energies.
16. A non-transitory computer readable medium, loadable directly into a memory unit of an image analysis apparatus, comprising program code sections which, when executed in the image analysis apparatus, carry out the method of claim 1 .
17. A non-transitory computer readable medium including program segments for, when executed on a computer device, causing the computer device to implement the method of claim 1 .
18. An image analysis apparatus for identifying potential perfusion defects in a tissue region through which blood flows in an object under investigation, comprising:
a processor, the processor being configured to form
an image data set interface, configured to read in, from a memory, at least one high-energy image data set covering the tissue region of the object under investigation and at least one low-energy image data set covering the tissue region of the object under investigation, the at least one high-energy image data set covering a tissue region of the object under investigation and the at least one low-energy image data set covering the tissue region of the object under investigation being generated by a reconstruction unit and the memory storing the generated at least one high-energy image data set and at least one low-energy image data set;
a contrast medium image determination unit, designed to determine a virtual contrast medium image data set based on the at least one high-energy image data set and the at least one low-energy image data set; and
a perfusion defect identification unit, designed to
detect first candidate perfusion defect regions within the virtual contrast medium image data set,
detect second candidate perfusion defect regions within a further image data set based on at least one of the at least one high-energy image data set and the at least one low-energy image data set, and
identify potential perfusion defects, based on a comparison of the first candidate perfusion defect regions with the second candidate perfusion defect regions.
19. The image analysis apparatus of claim 18 , further comprising:
the reconstruction unit, configured to generate at least one high-energy image data set covering a tissue region of the object under investigation and the at least one low-energy image data set covering the tissue region of the object under investigation.
20. A computed tomography system comprising:
a scanner;
a detector; and
the image analysis apparatus of claim 19 .
21. A computed tomography system comprising;
a scanner;
a detector; and
the image analysis apparatus of claim 18 .