Method for evaluating novel, stroke treatments using a tissue risk map

• US 7,020,578 B2
• Filed: 02/02/2001
• Issued: 03/28/2006
• Est. Priority Date: 02/02/2000
• Status: Expired due to Term

• Current Assignee: The General Hospital Corporation (Mass General Brigham, Inc.)
• Original Assignee: The General Hospital Corporation (Mass General Brigham, Inc.)
• Inventors: Wu, Ona, Sorensen, Gregory A.
• Primary Examiner(s): Tsai, Carol S. W.
• Application Number:
  US10/182,978
• Publication Number:
  US 20040127799A1
• Time in Patent Office: 1,880 Days
• Field of Search: 702/181, 600/410, 600/411, 600/419, 600/420, 600/407, 424/9.1, 424/9.2, 424/9.3, 424/9.34, 424/9.36
• US Class Current: 702/181
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A method of evaluating novel stroke treatments includes generating a risk map indicative of the probability of tissue infarction on voxel-by-voxel basis and selecting a probability range for evaluating the therapeutic effect of the novel treatment. In one particular embodiment, tissue having a fifty percent probability of tissue infarction is selected. A novel treatment that has a reduced level of overall actual infarction as compared to the predicted value is indicative of therapeutic effect.

Claims

1. A method of evaluating a novel stroke treatment, comprising:

generating a risk map indicating voxel-by-voxel statistical probability of tissue infarction;

selecting a probability risk threshold for tissue infarction risk in the risk map;

evaluating the therapeutic effect of the novel stroke treatment by comparing the selected probability risk threshold and actual tissue infarction state on a voxel-by-voxel basis.

2. The method according to claim 1, further including combining DWI and PWI data to generate the risk map.

3. The method according to claim 2, wherein the DWI data is selected from one or more types selected from the group consisting of elements of the diffusion tensor such as ADC, low-b EPI T2 image, anisotropy indices, eigenvalues and eigenvectors.

4. The method according to claim 2, wherein the PWI data is selected from one or more types selected from the group consisting of CBF, CBV, MTT and other parameters of the dynamic susceptibility contrast-enhanced images.

5. The method according to claim 2, wherein the risk map is generated using a GLM algorithm combining EPI T2, ADC, DWI, CBF, CBV, and/or MTT data.

6. The method according to claim 1, further including utilizing a GLM algorithm to generate the risk map.

7. The method according to claim 1, further including utilizing a GAM algorithm to generate the risk map.

8. The method according to claim 1, further including determining whether the novel treatment achieves a statically significant difference as compared to a conventional therapy.

9. The method according to claim 8, further including determining whether the novel therapy is effective.

10. The method according to claim 1, further including calculating true positive, true negative, false positive, and false negative values.

11. The method according to claim 1, further including generating a plurality of risk maps, each of which corresponds to a particular therapy.

12. The method according to claim 11, further including selecting one of the plurality of therapy based upon the plurality of risk maps.

13. The method according to claim 12, further including selecting a therapy corresponding to a respective one of the plurality of risk maps having a smallest volume of tissue risk of infarction.

14. The method according to claim 12, further including selecting a therapy corresponding to a respective one of the plurality of risk maps that minimizes a risk threshold of infarction.

15. The method according to claim 1, further including selecting the risk threshold based upon which tissue is likely to infarct if a patient receives conventional therapy.

16. The method according to claim 1, further including selecting the risk threshold based upon an optimal operating point for discriminating tissue likely to infarct front tissue not likely to infarct.

17. The method according to claim 1, further including selecting the risk threshold of about thirty percent.

18. The method according to claim 1, further including selecting the risk threshold based upon a risk level of tissue targeted by the novel treatment.