Non-invasive imaging for determination of global tissue characteristics

• US 8,874,192 B2
• Filed: 08/07/2013
• Issued: 10/28/2014
• Est. Priority Date: 02/06/2004
• Status: Expired due to Fees

• Current Assignee: Wake Forest University Health Sciences
• Original Assignee: Wake Forest University Health Sciences
• Inventors: Hundley, William Gregory, Hamilton, Craig A., Lane, Kimberly, Morgan, Tim, Torti, Frank
• Primary Examiner(s): MEHTA, PARIKHA SOLANKI
• Application Number:
  US13/961,581
• Publication Number:
  US 20130324831A1
• Time in Patent Office: 447 Days
• Field of Search: None
• US Class Current: 600/411
• CPC Class Codes: A61B 5/0036 including treatment, e.g., using an implantable medical device, ablating, ventilating
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/42 Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems A61B1/00, A61B5/4857, A61B5/486, A61B5/4866, A61B5/4869 take precedence
• CPC Class Codes: A61B 5/4519 Muscles A61B5/389, A61B5/224 take precedence
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/503 for diagnosis of the heart
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 6/506 for diagnosis of nerves
• CPC Class Codes: G01R 33/281 Means for the use of in vitro contrast agents G01R33/282 takes precedence; involving use of a contrast agent in MR imaging G01R33/5601; in vivo contrast agents A61K49/0002
• CPC Class Codes: G01R 33/56 Image enhancement or correction, e.g. subtraction or averaging techniques , e.g. improvement of signal-to-noise ratio and resolution
• CPC Class Codes: G06T 2200/24 involving graphical user interfaces [GUIs]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 7/0014 using an image reference approach
• CPC Class Codes: G06T 7/41 based on statistical description of texture

Abstract

Evaluating tissue characteristics including identification of injured tissue or alteration of the ratios of native tissue components such as shifting the amounts of normal myocytes and fibrotic tissue in the heart, identifying increases in the amount of extracellular components or fluid (like edema or extracellular matrix proteins), or detecting infiltration of tumor cells or mediators of inflammation into the tissue of interest in a patient, such as a human being, is provided by obtaining a first image of tissue including a region of interest from a first acquisition, and obtaining a second image of the tissue including the region of interest during a second, subsequent acquisition. The subsequent acquisition may be obtained after a period of time to determine if injury has occurred during that period of time. Such a comparison may include comparison of mean, average characteristics, histogram shape, such as skew and kurtosis, or distribution of intensities within the histogram.

Claims

1. A method of evaluating tissue characteristics in a patient, comprising:

electronically applying a statistical autocorrelation analysis to image data to determine a pattern of at least one pixel parameter within a three dimensional tissue volume of a region of interest to evaluate whether the pattern is (i) clustered or (ii) distributed or diffuse; and

determining a potential of a global injury, global abnormal tissue, or global abnormal accumulation of materials not found in normal ratios within native tissue based on the determined pattern, wherein the global injury, global abnormal tissue, and/or global abnormal accumulation is due to a change in tissue composition and/or function that is in a diffuse and/or distributed pattern in the region of interest,

wherein the electronically applying is carried out using at least one processor.

2. The method of claim 1, wherein the statistical autocorrelation analysis is carried out using an autocorrelation statistic, wherein the image data is derived from cardiac MRI images, and wherein the determining the potential is carried out to determine the potential of a future onset of global cardiac injury.

3. The method of claim 1, wherein the determining the potential is carried out electronically to determine the potential of a global cardiac injury by analyzing cardiac MRI image data using the at least one processor and is carried out to determine the potential of global injury associated with at least one of viral cardiomyopathy, alcoholic cardiomyopathy, postpartum cardiomyopathy, idiopathic dilated cardiomyopathy, cardiac necrosis, inflammation, infection, cardiac edema or cardiac fibrosis.

4. The method of claim 2, wherein the determining step is carried out electronically, the method further comprising electronically displaying the determined potential of the future onset of global cardiac injury to a workstation having a display associated with a clinician or other user.

5. The method of claim 1, wherein the image data comprises at least three short axis cardiac MRI slices of a left ventricle, wherein the at least one pixel parameter is signal intensity, and wherein the applying the autocorrelation is carried out to evaluate whether heightened signal intensities are associated with myocardial infarction based or whether the heightened signal intensities are tightly clustered in an infarct zone or whether heightened signal intensities are distributed through the left ventricle so as to be associated with the global injury, global abnormal tissue, and/or global abnormal accumulation.

6. The method of claim 1, wherein the three dimensional tissue volume of the region of interest comprises cardiac tissue and the image data comprises MRI cardiac image data, and wherein the applying the autocorrelation analysis comprises an autocorrelation statistic number (I), with a respective clustered pattern indicated by an autocorrelation statistic number different than that associated with a respective distributed pattern.

7. The method of claim 1, wherein the statistical autocorrelation analysis is carried out using an autocorrelation statistic, and wherein the at least one pixel parameter includes MRI signal intensity.

8. The method of claim 1, wherein the statistical autocorrelation analysis is carried out using an autocorrelation statistic, and wherein the at least one pixel parameter includes color saturation of MRI image data.

9. The method of claim 1, wherein the statistical autocorrelation analysis is carried out using an autocorrelation statistic, and wherein the image data comprises MRI image data, and wherein the at least one pixel parameter includes a relative characteristic of multiple pixels/voxels.

10. The method of claim 9, wherein the relative characteristic includes contrast ratios.

11. The method of claim 1, wherein the image data is derived from at least one MRI image taken after administration of a cytotoxic drug to the patient.

12. The method of claim 1, wherein the statistical autocorrelation analysis is carried out using an autocorrelation statistic, and wherein the region of interest is the heart, and wherein the image data is derived from MRI perfusion image slices or are from MRI images acquired using blood oxygenation level dependent (BOLD) cardiac imaging.

13. The method of claim 1, wherein the image data comprises cardiac MRI image data, wherein the statistical autocorrelation analysis is carried out using an autocorrelation statistic, and wherein the global injury, global abnormal tissue or global accumulation is associated with at least one of cardiomyopathy, cardiac edema, inflammation, infection or cardiac fibrosis.

14. The method of claim 1, wherein the image data is associated with first and second images obtained at intervals prior to or after delivery of a planned cancer therapy, and wherein the determining the potential is carried out to evaluate whether myocellular necrosis is likely to result in a drop in LVEF (left ventricular ejection fraction).

15. The method of claim 14, wherein the determining the autocorrelation analysis is carried out electronically to determine whether a distribution pattern of voxels or pixels of high intensity in a left ventricle myocardium is distributed rather than clustered to predict whether a myocellular injury secondary to a cancer therapy is likely to be associated with a fall in LVEF (left ventricular ejection fraction).

16. The method of claim 1, wherein the image data comprises MRI image data, and wherein the determining the potential is carried out electronically by assessing the pattern of the at least one pixel parameter in image data of myocardial perfusion and/or injury obtained without administration of a contrast agent.

17. A system for evaluating tissue characteristics in a patient, comprising:

at least one processor configured to;

(i) apply an autocorrelation to a three dimensional tissue volume of a region of interest in at least one patient image to evaluate whether at least one defined pixel/voxel characteristic is (a) clustered or (b) distributed or diffuse; and

(ii) determine if there is a potential of a global injury, global abnormal tissue, or global abnormal accumulation of materials not found in normal ratios within native tissue based on whether the pixel/voxel characteristic is distributed rather than clustered.

18. The system of claim 17, wherein the patient image comprises cardiac MRI images taken at different points of time, wherein the at least one processor is configured to determine a potential for a future onset of global cardiac injury as the potential of the global injury.

19. The system of claim 17, wherein the at least one processor is configured to determine the potential of the future onset of the global cardiac injury associated with at least one of viral cardiomyopathy, alcoholic cardiomyopathy, postpartum cardiomyopathy, idiopathic dilated cardiomyopathy, cardiac necrosis, cardiac edema or cardiac fibrosis.

20. The system of claim 17, further comprising a display in communication with the at least one processor configured to display the potential of the future onset of global cardiac injury.

21. The system of claim 17, wherein the at least one image comprises image data from three short axis cardiac MRI slices of a left ventricle, wherein the at least one defined pixel/voxel characteristic comprises signal intensity, and wherein the autocorrelation is carried out to evaluate whether heightened signal intensities are associated with myocardial infarction based on whether the heightened signal intensities are tightly clustered in an infarct zone or whether heightened signal intensities are associated with cytotoxicity such that the heightened intensities are distributed through the left ventricle.

22. The system of claim 17, wherein the at least one processor is configured to determine a potential for a global cardiac injury as the potential of the global injury, and wherein the at least one processor is configured to carry out the autocorrelation by applying an autocorrelation statistic to generate an autocorrelation statistic number (I), with a clustered pattern indicated by an autocorrelation statistic number greater than that associated with a distributed pattern.

23. The system of claim 17, wherein the region of interest is the heart, and wherein the at least one patient image comprises cardiac MRI image slices.

24. The system of claim 17, wherein the at least one processor is configured to detect a global cardiac injury associated with at least one of cardiomyopathy, inflammation, infection, edema or fibrosis.

25. The system of claim 17, wherein the at least one patient image is a plurality of MRI images obtained at intervals prior to and/or after administration of a chemotherapeutic drug, and wherein the at least one processor is configured to apply the autocorrelation to evaluate whether myocellular necrosis is likely to result in a drop in LVEF (left ventricular ejection fraction) as a future onset of the potential global injury.

26. The system of claim 17, wherein the at least onepatient image comprises data from MRI cardiac image slices, and wherein the autocorrelation is carried out to evaluate high intensity voxels/pixels in a left ventricle myocardium, and wherein the at least one processor is configured to identify the potential for global injury when the high intensity voxels/pixels are distributed rather than clustered and predict whether a myocellular injury secondary to a cancer therapy is likely to be associated with a fall in LVEF (left ventricular ejection fraction).