System and method for 3D visualization of lung perfusion or density and statistical analysis thereof

• US 20060056691A1
• Filed: 07/15/2005
• Published: 03/16/2006
• Est. Priority Date: 08/10/2004
• Status: Active Grant

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Novak, Carol L., Kiraly, Atilla Peter, Vaz, Michael
• Granted Patent: US 7,715,608 B2
• Time in Patent Office: Days
• US Class Current: 382/173
• CPC Class Codes: A61B 6/507 for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: G06T 15/08 Volume rendering
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/30061 Lung
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06T 7/155 involving morphological operators

Abstract

A system and method for 3D visualization of a pair of lungs are provided. The method comprises: segmenting image data of the pair of lungs and lung parenchyma; generating a 3D map as a function of the segmented image data; and rendering the 3D map as a color-coded semi-transparent 3D volume, wherein an opaque region highlights an area of interest.

Claims

1. A method for 3D visualization of a pair of lungs, comprising:

segmenting image data of the pair of lungs and lung parenchyma;

generating a 3D map as a function of the segmented image data; and

rendering the 3D map as a color-coded semi-transparent 3D volume, wherein an opaque region highlights an area of interest.

2. The method of claim 1, wherein the 3D map is one of a perfusion map or a density map.

3. The method of claim 1, wherein the 3D map is generated by one of adaptive smoothing or texture filtering.

4. The method of claim 1, wherein the area of interest indicates a region of abnormal perfusion or density.

5. The method of claim 4, wherein the region of abnormal perfusion is due to an embolus.

6. The method of claim 1, further comprising:

generating a histogram of the rendered 3D map; and

determining whether the histogram indicates a positive or negative presence of embolus.

7. The method of claim 6, further comprising:

classifying embolus as one of acute or chronic.

8. A method for 3D visualization of lung perfusion, comprising:

segmenting image data of lung parenchyma;

generating a perfusion map of the segmented image data; and

rendering the perfusion map as a color-coded semi-transparent 3D volume, wherein an opaque region of perfusion is visible.

9. The method of claim 8, wherein the step of generating a perfusion map of the segmented image data comprises:

segmenting the lung parenchyma;

performing a local smoothing; and

determining local-neighborhood mean densities of the lung parenchyma.

10. The method of claim 9, wherein the step of segmenting the lung parenchyma comprises:

segmenting a volume of the pair of lungs from a thoracic volume;

identifying airways and blood vessels in the segmented lung volume; and

generating a mask of the lung parenchyma by removing the airways and vascular structures from the segmented lung volume.

11. The method of claim 10, wherein the step of performing a local smoothing comprises:

shifting the image data;

masking the shifted image data with the parenchyma mask to obtain a parenchyma image;

performing a Guassian smoothing on the parenchyma mask and image to obtain a smoothed parenchyma mask and image;

masking the smoothed parenchyma image with the parenchyma mask;

equalizing the masked smoothed parenchyma image; and

shifting the equalized image to generate the perfusion map.

12. The method of claim 8, wherein the visible region of perfusion is an indication of one of pulmonary embolus or diffuse lung disease.

13. The method of claim 8, wherein the image data is acquired using one of a CT, helical CT or MR imaging technique.

14. The method of claim 8, further comprising:

adjusting a color map to observe high density regions of interest.

15. The method of claim 8, wherein the visible region of perfusion is one of low perfusion, high perfusion or abnormal perfusion.

16. A system for 3D visualization of a pair of lungs, comprising:

a memory device for storing a program;

a processor in communication with the memory device, the processor operative with the program to;

segment image data of the pair of lungs and lung parenchyma;

generate a 3D map as a function of the segmented image data; and

render the 3D map as a color-coded semi-transparent 3D volume, wherein an opaque region highlights an area of interest.

17. The system of claim 16, wherein the 3D map is one of a perfusion map or a density map.

18. The system of claim 16, wherein the area of interest indicates a region of abnormal perfusion or density.

19. The system of claim 18, wherein the region of abnormal perfusion is due to an embolus.

20. The system of claim 16, wherein the processor is further operative with the program code to:

generate a histogram of the rendered 3D map; and

determine whether the histogram indicates a positive or negative presence of embolus.

21. The system of claim 20, wherein the processor is further operative with the program code to:

classify embolus as one of acute or chronic.

22. A system for 3D visualization of lung perfusion, comprising:

a memory device for storing a program;

a processor in communication with the memory device, the processor operative with the program to;

segment image data of lung parenchyma;

generate a perfusion map of the segmented image data; and

render the perfusion map as a color-coded semi-transparent 3D volume, wherein an opaque region of perfusion is visible.

23. The system of claim 22, wherein when generating a perfusion map of the segmented image data the processor is further operative with the program code to:

segment the lung parenchyma;

perform a local smoothing; and

determine local-neighborhood mean densities of the lung parenchyma.

24. The system of claim 23, wherein when segmenting the lung parenchyma the processor is further operative with the program code to:

segment a volume of the pair of lungs from a thoracic volume;

identify airways and blood vessels in the segmented lung volume; and

generate a mask of the lung parenchyma by removing the airways and vascular structures from the segmented lung volume.

25. The system of claim 24, wherein when performing a local smoothing the processor is further operative with the program code to:

shift the image data;

mask the shifted image data with the parenchyma mask to obtain a parenchyma image;

perform a Guassian smoothing on the parenchyma mask and image to obtain a smoothed parenchyma mask and image;

mask the smoothed parenchyma image with the parenchyma mask;

equalize the masked smoothed parenchyma image; and

shift the equalized image to generate the perfusion map.

26. The system of claim 22, wherein the visible region of perfusion is an indication of one of pulmonary embolus or diffuse lung disease.

27. The system of claim 22, wherein the image data is acquired using one of a CT, helical CT or MR imaging device.

28. The system of claim 22, the processor is further operative with the program code to:

adjust a color map to observe high density regions of interest.

29. The system of claim 22, wherein the visible region of perfusion is one of low perfusion, high perfusion or abnormal perfusion.