Method for determining the condition of an object by magnetic resonance imaging

• US 7,660,619 B2
• Filed: 03/18/2006
• Issued: 02/09/2010
• Est. Priority Date: 08/12/2004
• Status: Active Grant

• Current Assignee: Esaote SpA
• Original Assignee: Esaote SpA
• Inventors: Satragno, Luigi, Biglieri, Eugenio
• Primary Examiner(s): Casler; Brian
• Assistant Examiner(s): KISH, JAMES M
• Application Number:
  US11/308,359
• Publication Number:
  US 20060183998A1
• Time in Patent Office: 1,424 Days
• Field of Search: 600/407, 600/410, 600/416, 600/419
• US Class Current: 600/410
• CPC Class Codes: A61B 5/0037 Performing a preliminary scan, e.g. a prescan for identifying a region of interest
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: G01N 24/08 by using nuclear magnetic resonance G01N24/12 takes precedence
• CPC Class Codes: G01R 33/56308 Characterization of motion or flow; Dynamic imaging

Abstract

A method for determining the condition of an animate or inanimate object by magnetic resonance imaging, MRI, particularly for determining the pathologic condition of rheumatoid arthritis (“arthritis rheumatoides”) and carrying out a patient follow-up.

Claims

1. A method for using magnetic resonance imaging (MRI) to evaluate a selected region of interest of an object, the method comprising:

acquiring at a plurality of successive acquisition times an MRI image of the selected region of interest of a sample object having a known condition;

generating for the sample object a function representing a mean intensity of each of the acquired MRI images for each of the successive acquisition times;

acquiring at a plurality of successive acquisition times an MRI image of the selected region of interest of a test object having an unknown condition, the test object being different from the sample object;

generating for the test object a function representing a mean intensity of each of the acquired MRI images for each of the successive acquisition times;

evaluating the unknown condition of the test object by comparing the generated function for the test object to the generated function for the sample object; and

constructing a development time table of conditions of the test object so as to monitor the development of a disease.

2. The method of claim 1, further comprising prior to the steps of acquiring the MRI images of the sample object and the test object, respectively:

treating the sample object to induce a response indicative of the known condition; and

treating the test object to induce a response indicative of the unknown condition.

3. The method of claim 2, wherein the treating steps comprise injecting a substance into the sample object and test object, respectively.

4. The method of claim 1, wherein the generating the sample object function and the test object function steps each comprises filling a perfusion curve to a plurality of data points, wherein each of the data points comprises a measured mean intensity and a corresponding one of the successive acquisition times.

5. The method of claim 4, wherein the filling the perfusion curve step comprises interpolating between at least two of the data points.

6. The method of claim 1, wherein the acquiring the MRI images steps each comprise acquiring a two-dimensional MRI image of the selected region of interest along a section plane of the sample object and the test object, respectively.

7. The method of claim 1, wherein the acquiring the MRI images steps each comprise acquiring a three-dimensional MRI image of the selected region of interest of the sample object and the test object, respectively.

8. The method of claim 7, wherein the acquiring a three-dimensional MRI image step comprises:

acquiring a three-dimensional image data array;

defining a section plane of the sample object and the test object, respectively; and

selecting image data points from the three-dimensional image data array that fall on the defined section plane.

9. The method of claim 1, further comprising tracking the development of the unknown condition by repeating for each of a plurality of follow-on sessions the acquiring the MRI images of the test object step, the generating the test object function step, and the evaluating step.

10. The method of claim 9, wherein the tracking step comprises comparing the generated function for the test object for a particular follow-on session to the generated function for the test object for one or more previous follow-on sessions.

11. The method of claim 1, wherein the sample object function and the test object function are non-linear functions.

12. The method of claim 1, further comprising providing the function for the test object having the unknown condition to a predictive algorithm developed based on the function of the sample object having the known condition, wherein the predictive algorithm determines the unknown condition of the test object.

13. A method for using magnetic resonance imaging (MRI) to evaluate a selected region of interest of an object, the method comprising:

acquiring for each of a plurality of sample objects an MRI image of the selected region of interest at a plurality of successive acquisition times, wherein each of the sample objects has an associated known condition;

generating for each of the sample objects a function representing a mean intensity of each of the acquired MRI images for each of the successive acquisition times;

approximating a parameter for each of the generated sample object functions, wherein each of the approximated parameters is indicative of a known condition of an associated one of the sample objects;

acquiring for a test object having an unknown condition an MRI image of the selected region of interest at a plurality of successive acquisition times, the test object being different from the plurality of sample objects;

generating for the test object a function representing a mean intensity of each of the acquired MRI images for each of the successive acquisition times;

approximating a parameter for the generated test object function, wherein the approximated parameter is indicative of the unknown condition; and

comparing the approximated parameter indicative of the unknown condition to the approximated parameters indicative of the known conditions to identify the unknown condition.

14. The method of claim 13, further comprising providing the approximated parameter indicative of the unknown condition to a predictive algorithm developed based on the approximated parameters indicative of the known conditions, wherein the predictive algorithm determines the unknown condition of the test object.

15. The method of claim 13, further comprising tracking the development of the unknown condition by repeating for each of a plurality of follow-on sessions the acquiring the MRI images of the test object step, the generating the test object function step, and the approximating a parameter indicative of the unknown condition step.

16. The method of claim 15, wherein the tracking step comprises comparing the approximated parameter indicative of the unknown condition for a particular follow-on session to the approximated parameter indicative of the unknown condition for one or more previous follow-on sessions.

17. The method of claim 15, further comprising using at least one of an opaque marker and an anatomical marker to identify a section plane of the test object for each of the follow-on sessions along which the MRI images are acquired.

18. The method of claim 17, further comprising applying a registration algorithm to spatially align the section plane along which the MRI images are acquired relative to the test object for each of the follow-on sessions.

19. The method of claim 18, wherein the at least one marker is used to identify a three-dimensional region of interest of the test object for each of the follow-on sessions, and wherein the registration algorithm is applied to spatially align the three-dimensional region of interest of which the MRI images are acquired relative to the test object for each of the follow-on sessions.

20. The method of claim 13, wherein the acquiring the MRI images for each of the sample objects step comprises acquiring for each of the sample objects a two-dimensional MRI image along a section plane of the sample object at each of the successive acquisition times, and wherein the acquiring the MRI images for the test object step comprises acquiring a two-dimensional MRI image along the section plane of the test object at each of the successive acquisition times.

21. The method of claim 13, wherein the acquiring the MRI images for each of the sample objects step comprises acquiring for each of the sample objects a three-dimensional MRI image of the selected region of interest of the sample object at each of the successive acquisition times, and wherein the acquiring the MRI images for the test object step comprises acquiring a three-dimensional MRI image of the selected region of interest of the test object at each of the successive acquisition times.

22. The method of claim 21, wherein the acquiring the three-dimensional MRI images steps comprise:

acquiring a three-dimensional image data array;

defining a section plane of the sample object and the test object, respectively; and

selecting image data points from the three-dimensional image data array that fall on the defined section plane.

23. A method for using magnetic resonance imaging (MRI) to evaluate a selected region of interest of an object, the method comprising:

a) acquiring at least one image by MRI along at least one slice or along one section plane or a volume of a selected three-dimensional region within the volume of the biological tissue(s) having a known condition at different successive times within a predetermined time period;

b) determining the mean intensity of the MRI signal for each image or for a selected region of each image, which selected region is identical for each image or refers to an identical part of the imaged sample biological tissue(s) under examination;

c) constructing an empirical time dependency function of the mean intensity by using the data pairs consisting of the mean intensity of each image or of the part of each image and the corresponding time of acquisition;

d) analytically determining a parameter of a function approximating the empirical time dependency function;

e) providing at least a second biological tissue to be examined having an unknown condition and carrying out the steps a) to d) using the said second biological tissue;

f) comparing the parameters of the functions approximating the empirical time dependency function relating to the sample biological tissue(s) and to the second biological tissue(s) having an unknown condition in order to detect differences of the condition of the second object from the known conditions of the sample object.

24. The method of claim 23, further comprising repeating, at several different times, the steps e) and f) for determining changes in the degree of disease activity over time and/or during a therapeutic treatment.

25. A method for using magnetic resonance imaging (MRI) to evaluate a selected region of interest of an object, the method comprising:

a) generating a database of contrast media perfusion curves each one uniquely associated to a well-defined degree of disease activity;

b) the database being generated by acquiring at least one image of an identical region of interest of the same anatomical region by MRI along at least one slice or one section plane of the anatomical region in patients or a three-dimensional MRI image of the anatomical region in more than one patient, each patient having a known degree of disease activity;

c) for each patient having a well-defined degree of disease activity, acquiring a sequence of MRI images which sequence comprises a certain number of MRI images taken at different times, one from the other, within a certain period of time;

d) the period of time starting immediately after or at the injection of a contrast medium in the anatomical region and terminating after a certain time which is determined as a typical duration of contrast media perfusion in the anatomical region;

e) determining for each image or for a selected region of each image, which selected region is identical for each image or refers to an identical part of the imaged anatomical region under examination, the mean intensity of the acquired image data;

f) constructing an empirical time dependency function of the mean intensity by using the data pairs consisting of the mean intensity of each image or of the part of each image and the corresponding time of acquisition;

g) analytically determining a parameter of a function approximating the empirical time dependency function;

h) determining the disease activity in the same region of interest of the same anatomical region of a patient having an unknown level of disease activity by carrying out the steps of injecting the contrast media in the anatomical region of the patient and by acquiring the sequence of MRI images of the anatomical region for the predetermined period of time and finally constructing the empirical time dependency function of the mean intensity by using the data pairs consisting of the mean intensity of each image or of the part of each image and the corresponding time of acquisition and analytically determining the parameter of a function approximating the empirical time dependency function;

i) comparing the parameters of the function approximating the empirical time dependency function relating to the database in order to detect the disease activity level; and

,

j) repeating at several different times the steps h) and i) for determining changes in the degree of disease activity over time and/or during a therapeutic treatment.