Method and system for B0 drift and respiratory motion compensation in echo-planar based magnetic resonance imaging

• US 9,715,726 B2
• Filed: 12/05/2013
• Issued: 07/25/2017
• Est. Priority Date: 12/05/2013
• Status: Active Grant

• Current Assignee: Siemens Healthcare GMBH (Siemens AG)
• Original Assignee: Siemens Healthcare GMBH (Siemens AG)
• Inventors: Patil, Sunil Goraksha, Xu, Di
• Primary Examiner(s): Laballe, Clayton E
• Assistant Examiner(s): Pu, Ruifeng
• Application Number:
  US14/097,583
• Publication Number:
  US 20150160321A1
• Time in Patent Office: 1,328 Days
• CPC Class Codes: G01R 33/4804 Spatially selective measurement of temperature or pH
• CPC Class Codes: G01R 33/5608 Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels image data processing or generation, in general G06T
• CPC Class Codes: G01R 33/5613 Generating steady state signals, e.g. low flip angle sequences [FLASH]
• CPC Class Codes: G01R 33/5616 using gradient refocusing, e.g. EPI
• CPC Class Codes: G01R 33/56509 due to motion, displacement or flow, e.g. gradient moment nulling G01R33/567 takes precedence
• CPC Class Codes: G01R 33/56563 caused by a distortion of the main magnetic field B0, e.g. temporal variation of the magnitude or spatial inhomogeneity of B0 G01R33/56509, G01R33/56518, G01R33/56536 take precedence
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A method and apparatus for B₀ correction in echo-planar (EP) based magnetic resonance image (MRI) is disclosed. Two phase images are obtained from each of a first echo-planar imaging (EPI) acquisition and a second EPI acquisition. A first susceptibility map is generated based on the two phase images obtained from the first EPI acquisition and a second susceptibility map is generated based on the two phase images obtained from the second EPI acquisition. A smooth polynomial function for modeling the B₀ drift and respiratory motion between the first EPI acquisition and the second EPI acquisition is initialized based on the first and second susceptibility maps. A compensated temperature map is then iteratively reconstructed based on the smooth polynomial function.

Claims

1. A method for B₀ drift and respiratory motion compensation in echo-planar based magnetic resonance imaging (MRI) comprising:

obtaining at least two phase images of a target region of a patient from each of a first echo-planar imaging (EPI) acquisition and a second EPI acquisition;

generating a first susceptibility map based on the at least two phase images of the target region of the patient obtained from the first EPI acquisition and a second susceptibility map based on the at least two phase images of the target region of the patient obtained from the second EPI acquisition;

initializing a smooth polynomial function for modeling the B₀ drift and respiratory motion between the first EPI acquisition and the second EPI acquisition based on the first and second susceptibility maps; and

iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion.

2. The method of claim 1, wherein iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion comprises:

(a) estimating susceptibility changes due to B₀ drift and respiratory motion using the smooth polynomial function;

(b) generating a compensated temperature map based on the estimated susceptibility changes due to B₀ drift and respiratory motion;

(c) updating the smooth polynomial function based on the compensated temperature map; and

(d) repeating steps (a)-(c) until the smooth polynomial function converges.

3. The method of claim 2, wherein generating a first susceptibility map based on the at least two phase images of the target region of the patient obtained from the first EPI acquisition and a second susceptibility map based on the at least two phase images of the target region of the patient obtained from the second EPI acquisition comprises:

generating the first susceptibility map (Δ

B₀^bk) as

4. The method of claim 3, wherein initializing a smooth polynomial function for modeling the B₀ drift and respiratory motion between the first EPI acquisition and the second EPI acquisition based on the first and second susceptibility maps comprises:

initializing smooth polynomial function (Xc) as Xc=Δ

B₀^therm−

Δ

B₀^bk.

5. The method of claim 4, wherein the smooth polynomial function is a fourth order polynomial function and estimating susceptibility changes due to B₀ drift and respiratory motion using the smooth polynomial function comprises:

modeling the susceptibility changes (δ

B′

) due to B₀ drift and respiratory motion for an image of n pixels at iteration i as;

6. The method of claim 5, wherein generating a compensated temperature map based on the estimated susceptibility changes due to B₀ drift and respiratory motion comprises:

generating the compensated temperature map at iteration i as;

7. The method of claim 6, wherein updating the smooth polynomial function based on the compensated temperature map comprises:

determining the coefficient vector c_i of the smooth polynomial function at iteration i by calculating a least square solution of Xc_i=Δ

B₀^therm−

Δ

B₀^bk−

B_c(Δ

T), where B_c(T)=B₀α

Δ

T.

8. The method of claim 7, wherein repeating steps (a)-(c) until the smooth polynomial function converges comprises:

repeating steps (a)-(c) until |c_i−

c_i-1|<

ε

, where ε

s a threshold value.

9. The method of claim 1, wherein the first EPI acquisition is a baseline EPI acquisition prior to a thermal therapy procedure and the second EPI acquisition is an EPI acquisition during the thermal therapy procedure, and iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion comprises:

iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient due to the thermal therapy procedure based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion.

10. The method of claim 1, wherein the first and second EPI acquisitions are multi-pathway EPI (MP-EPI) acquisitions and obtaining at least two phase images of a target region of a patient from each of a first echo-planar imaging (EPI) acquisition and a second EPI acquisition comprises:

reconstructing a phase image of the target region of the patient from each of a Fast Image with Steady-state Precession (FISP) pathway and a Reversed FISP (PSIF) pathway in each of the first and second EPI acquisitions.

11. The method of claim 2, wherein initializing a smooth polynomial function for modeling the B₀ drift and respiratory motion between the first EPI acquisition and the second EPI acquisition based on the first and second susceptibility maps comprises:

initializing the smooth polynomial function as a total susceptibility change between the first and second susceptibility maps.

12. The method of claim 11, wherein updating the smooth polynomial function based on the compensated temperature map comprises:

estimating susceptibility change due to a change in tissue temperature between the first acquisition time and the second acquisition time based on the compensated temperature map; and

updating the smooth polynomial function to model a remaining portion of the to the total susceptibility change between the first and second susceptibility maps other than the estimated susceptibility change due to a change in tissue temperature.

13. An apparatus for B₀ drift and respiratory motion compensation in echo-planar based magnetic resonance imaging (MRI) comprising:

a processor; and

a memory storing computer program instructions which when executed by the processor cause the processor to perform operations comprising;

obtaining at least two phase images of a target region of a patient from each of a first echo-planar imaging (EPI) acquisition and a second EPI acquisition;

generating a first susceptibility map based on the at least two phase images of the target region of the patient obtained from the first EPI acquisition and a second susceptibility map based on the at least two phase images of the target region of the patient obtained from the second EPI acquisition;

initializing a smooth polynomial function for modeling the B₀ drift and respiratory motion between the first EPI acquisition and the second EPI acquisition based on the first and second susceptibility maps; and

iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion.

14. The apparatus of claim 13, wherein iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion comprises:

estimating susceptibility changes due to B₀ drift and respiratory motion using the smooth polynomial function;

generating a compensated temperature map based on the estimated susceptibility changes due to B₀ drift and respiratory motion;

updating the smooth polynomial function based on the compensated temperature map; and

determining whether the smooth polynomial function has converged.

15. The apparatus of claim 13, wherein the first EPI acquisition is a baseline EPI acquisition prior to a thermal therapy procedure and the second EPI acquisition is an EPI acquisition during the thermal therapy procedure, and iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion comprises:

iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient due to the thermal therapy procedure based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion.

16. The apparatus of claim 14, wherein initializing a smooth polynomial function for modeling the B₀ drift and respiratory motion between the first EPI acquisition and the second EPI acquisition based on the first and second susceptibility maps comprises:

initializing the smooth polynomial function as a total susceptibility change between the first and second susceptibility maps.

17. The apparatus of claim 16, wherein updating the smooth polynomial function based on the compensated temperature map comprises:

estimating susceptibility change due to a change in tissue temperature between the first acquisition time and the second acquisition time based on the compensated temperature map; and

updating the smooth polynomial function to model a remaining portion of the to the total susceptibility change between the first and second susceptibility maps other than the estimated susceptibility change due to a change in tissue temperature.

18. A non-transitory computer readable medium storing computer program instructions for B₀ drift and respiratory motion compensation in echo-planar based magnetic resonance imaging (MRI), the computer program instructions when executed by a processor cause the processor to perform operations comprising:

obtaining at least two phase images of a target region of a patient from each of a first echo-planar imaging (EPI) acquisition and a second EPI acquisition;

generating a first susceptibility map based on the at least two phase images of the target region of the patient obtained from the first EPI acquisition and a second susceptibility map based on the at least two phase images of the target region of the patient obtained from the second EPI acquisition;

initializing a smooth polynomial function for modeling the B₀ drift and respiratory motion between the first EPI acquisition and the second EPI acquisition based on the first and second susceptibility maps; and

iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion.

19. The non-transitory computer readable medium of claim 18, wherein iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion comprises:

(a) estimating susceptibility changes due to B₀ drift and respiratory motion using the smooth polynomial function;

(b) generating a compensated temperature map based on the estimated susceptibility changes due to B₀ drift and respiratory motion;

(c) updating the smooth polynomial function based on the compensated temperature map; and

(d) repeating operations (a)-(c) until the smooth polynomial function converges.

20. The non-transitory computer readable medium of claim 19, wherein generating a first susceptibility map based on the at least two phase images of the target region of the patient obtained from the first EPI acquisition and a second susceptibility map based on the at least two phase of the target region of the patient images obtained from the second EPI acquisition comprises:

generating the first susceptibility map (Δ

B₀^bk) as

21. The non-transitory computer readable medium of claim 20, wherein initializing a smooth polynomial function for modeling the B₀ drift and respiratory motion between the first EPI acquisition and the second EPI acquisition based on the first and second susceptibility maps comprises:

initializing smooth polynomial function (Xc) as Xc=Δ

B₀^therm−

Δ

B₀^bk.

22. The non-transitory computer readable medium of claim 21, wherein the smooth polynomial function is a fourth order polynomial function and estimating susceptibility changes due to B₀ drift and respiratory motion using the smooth polynomial function comprises:

modeling the susceptibility changes (δ

B′

) due to B₀ drift and respiratory motion for an image of n pixels at iteration i as;

23. The non-transitory computer readable medium of claim 22, wherein generating a compensated temperature map based on the estimated susceptibility changes due to B₀ drift and respiratory motion comprises:

generating the compensated temperature map at iteration i as;

24. The non-transitory computer readable medium of claim 23, wherein updating the smooth polynomial function based on the compensated temperature map comprises:

determining the coefficient vector c_i of the smooth polynomial function at iteration i by calculating a least square solution of Xc_i=Δ

B₀^therm−

Δ

B₀^bk−

B_c(Δ

T), where B_c(T)=B₀α

Δ

T.

25. The non-transitory computer readable medium of claim 18, wherein the first EPI acquisition is a baseline EPI acquisition prior to a thermal therapy procedure and the second EPI acquisition is an EPI acquisition during the thermal therapy procedure and iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion comprises:

iteratively reconstructing a compensated temperature map showing temperature changes in tissue in the target region of the patient due to the thermal therapy procedure based on the smooth polynomial function to compensate for temperature map inaccuracies due to B₀ drift and respiratory motion.

26. The non-transitory computer readable medium of claim 18, wherein the first and second EPI acquisitions are multi-pathway EPI (MP-EPI) acquisitions and obtaining at least two phase images of a target region of a patient from each of a first echo-planar imaging (EPI) acquisition and a second EPI acquisition comprises:

reconstructing a phase image of the target region of the patient from each of a Fast Image with Steady-state Precession (FISP) pathway and a Reversed FISP (PSIF) pathway in each of the first and second EPI acquisitions.

27. The non-transitory computer readable medium of claim 19, wherein initializing a smooth polynomial function for modeling the B₀ drift and respiratory motion between the first EPI acquisition and the second EPI acquisition based on the first and second susceptibility maps comprises:

initializing the smooth polynomial function as a total susceptibility change between the first and second susceptibility maps.

28. The non-transitory computer readable medium of claim 27, wherein updating the smooth polynomial function based on the compensated temperature map comprises:

estimating susceptibility change due to a change in tissue temperature between the first acquisition time and the second acquisition time based on the compensated temperature map; and

updating the smooth polynomial function to model a remaining portion of the to the total susceptibility change between the first and second susceptibility maps other than the estimated susceptibility change due to a change in tissue temperature.