METHOD AND SYSTEM FOR GENERATING MR IMAGES OF A MOVING OBJECT IN ITS ENVIRONMENT

Abstract

The invention relates to a method for generating MR images (10, 20) of an object in its environment within a region of interest, said object executing motion comprising a plurality of moving phases within a period of time. According to several aspects of the invention, the method comprises the steps of: —providing a first dataset pertaining to one of the moving phases of the object (Si); —generating a first image (10) of a region of interest from the first dataset (S2); —identifying a dynamic region (12) and a static region (14) inside the first image (10), wherein the regions (12, 14) are predominantly dynamic or static respectively within the periodeperiod of time (S3); —editing the first image (10) by masking out the dynamic region (14) (S4); —performing an inverse Fourier transformation of the edited first image (16) showing the remaining static region (14) (S5); —providing a second dataset pertaining to one of the moving phases of the object (S6); —subtraction of the inverse Fourier transformation of the edited first image (16) with the remaining static region (14) from the second dataset (S7); —performing a Fourier transformation on the subtracted second dataset (18) (S8); and —generating a second image (20) of a reduced region of interest with respect to the region of interest of the first image (10), which reduced region of interest includes the dynamic region (12) (S9). The invention further relates to a corresponding MRI system for generating MR images of an object in its environment within a region of interest.

Claims

1. A method for generating magnetic resonance (MR) images of an object, said object executing motion comprising a plurality of moving phases within a period of time, the method comprising the steps of: acquiring a first dataset by using a plurality of receiver coils pertaining to one of the moving phases of the object using a SENSE undersampling scheme; reconstructing a first image of a region of interest from the first dataset by means of SENSE reconstruction; identifying a dynamic region and a static region inside the first image (10), wherein said regions are predominantly dynamic or static respectively within the period of time; editing the first image by masking out the dynamic region; weighing the edited first image by coil sensitivities of the multiple receive coils; performing an inverse Fourier transformation of the edited and weighed first image showing the remaining static region; acquiring a second dataset pertaining to one of the moving phases of the object by using the plurality of receive coils, wherein the second dataset is provided by means of undersampling during acquisition, resulting in a reduced field of view (rFoV) compared to a field of view of the first dataset and in addition using the same SENSE undersampling scheme as for the acquisition of the first dataset; subtracting the inverse Fourier transformation of the edited first image with the remaining static region from the second dataset; performing a Fourier transformation on the subtracted second dataset; and generating a second image of a reduced region of interest with respect to the region of interest of the first image, which reduced region of interest includes the dynamic region.

2. (canceled)

3. The method according to claim 1, wherein the moving object is a heart; the MR images are a cardiac MR images; and the moving phases of the object are cardiac phases from a cardiac region.

4. The method according to claim 1, wherein at least one further second image is generated by repeating steps six to nine of the nine steps respectively often.

5. The method according to claim 1, wherein the first image is generated by use of a full sampling image.

6. (canceled)

7. The method according to claim 1, wherein the edited first image is divided into a plurality of images, each image inverse Fourier transformed in the k-space domain.

8. (canceled)

9. (canceled)

10. A magnetic resonance imaging (MRI) system for generating MR images of an object, said object executing motion comprising a plurality of moving phases within a period of time, the system is established for performing the following steps: acquiring a first dataset by using a plurality of receiver coils pertaining to one of the moving phases of the object using a SENSE undersampling scheme; reconstructing a first image of a region of interest from the first dataset by means of SENSE reconstruction; identifying a dynamic region and a static region inside the first image, wherein said regions are predominantly dynamic or static respectively within the period of time; editing the first image by masking out the dynamic region; weighing the edited first image by coil sensitivities of the multiple receive coils; performing an inverse Fourier transformation of the edited and weighed first image showing the remaining static region; acquiring a second dataset pertaining to one of the moving phases of the object by using the plurality of receiver coils, wherein the second dataset is provided by means of undersampling during acquisition, resulting in a reduced field of view (rFoV) compared to a field of view of the first dataset and in addition using the same SENSE undersampling scheme as for the acquisition of the first dataset; subtracting the inverse Fourier transformation of the edited first image with the remaining static region from the second dataset; performing a Fourier transformation on the subtracted second dataset; and generating a second image of a reduced region of interest with respect to the region of interest of the first image, which reduced region of interest includes the dynamic region.

11. The system according to claim 10, wherein the system is established for performing a method for generating magnetic resonance images of an object, said object executing motion comprising a plurality of moving phases within a period of time, the method comprising: acquiring a first dataset by using a plurality of receiver coils pertaining to one of the moving phases of the object using a SENSE undersampling scheme; reconstructing a first image of a region of interest from the first dataset by means of SENSE reconstruction; identifying a dynamic region and a static region inside the first image, wherein said regions are predominantly dynamic or static respectively within the period of time; editing the first image by masking out the dynamic region; weighing the edited first image by coil sensitivities of the multiple receive coils; performing an inverse Fourier transformation of the edited and weighed first image showing the remaining static region; acquiring a second dataset pertaining to one of the moving phases of the object by using the plurality of receive coils, wherein the second dataset is provided by means of undersampling during acquisition, resulting in a reduced field of view (rFoV) compared to a field of view of the first dataset and in addition using the same SENSE undersampling scheme as for the acquisition of the first dataset; subtracting the inverse Fourier transformation of the edited first image with the remaining static region from the second dataset; performing a Fourier transformation on the subtracted second dataset; and generating a second image of a reduced region of interest with respect to the region of interest of the first image, which reduced region of interest includes the dynamic region.

12. The system according to claim 10, comprising a computer based data processing unit for image generation of magnetic resonance images.

13. (canceled)

14. A computer program product to execute the method according to claim 1.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0043] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0044] In the drawings:

[0045] FIG. 1 shows a graphic illustration of a procedure for displaying of generating cardiac MR images according to a preferred embodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS

[0046] In the following discussion reference is made to the heart as the object to be imaged. The invention is however applicable to other objects like other organs as well. The heart is selected merely as an example.

[0047] FIG. 1 shows a schematic representation of all the reconstruction steps using the static information and SENSE acquisition at the same time. This schematic representation represents a procedure for generating cardiac MR (CMR) images 10, 20 of the chest with a non-angulated coronal volume within a ROI and within a total period of time.

[0048] The procedure comprises the steps of:

[0049] Step 1 (S1): Providing a first dataset of a (first) cardiac phase from a cardiac region;

[0050] Step 2 (S2): Generating a first image 10 of a ROI from the first dataset; Step 3 (S3): Identifying a dynamic region 12 and a static region 14 inside the first image 10, wherein these regions 12, 14 are predominantly dynamic or static respectively within the total period of time (S3);

[0051] Step 4 (S4): Editing the first image 10 by masking out the dynamic region 12;

[0052] Step 5 (S5): Performing an inverse Fourier transformation (FFT−1) of the edited first image 16 showing the remaining static region 14;

[0053] Step 6 (S6): Providing a second dataset pertaining to a (second) cardiac phase from the cardiac region S6;

[0054] Step 7 (S7): Subtraction of the inverse Fourier transformation (FFT.sup.−1) of the edited first image 16 with the remaining static region 14 from the second dataset;

[0055] Step 8 (S8): Performing a Fourier transformation (FFT) on the subtracted second dataset 18; and

[0056] Step 9 (S9): Generating a second image 20 of a reduced region of interest (rROI) at least including the dynamic region 12.

[0057] Preferably, the identification is performed by manually selecting the static and dynamic region on the first image. For example a box around the heart could be selected as the dynamic region and thereby the region outside the box will be selected as the static region.

[0058] The data of the provided first and second dataset are previously acquired by data acquisition. The above mentioned total period of time is a total acquisition time. In preparation of Step 1 (S1), e.g. a full FoV single acquisition is performed to remove folds over artifacts. In Preparation of Step 6 (S6) an undersampling acquisition of the ky-kz space is performed for updating the reduced FoV (rFoV) for every cardiac phase. The term full FoV corresponds to the (complete) ROI and the term rFoV corresponds to the reduced ROI.

[0059] Those skilled in the art will understand in cases that the second dataset (rFoV) is undersampled compared to the first dataset, preferably also a Step 5a is applied between Step 5 and Step 7. In Step 5a, the inverse Fourier transformation (FFT.sup.−1) of the edited first image 16 will be undersampled in the same way as the undersampling used when providing (acquiring) the second dataset. So the procedure comprises the following steps: Step 1

(S1): Acquiring a first dataset of a (first) cardiac phase from a cardiac region;

[0060] Step 2 (S2): Generating a first image 10 of a ROI from the first dataset;

[0061] Step 3 (S3): Identifying a dynamic region 12 and a static region 14 inside the first image 10, wherein these regions 12, 14 are predominantly dynamic or static respectively within the total period of time (S3);

[0062] Step 4 (S4): Editing the first image 10 by masking out the dynamic region 12;

[0063] Step 5 (S5): Performing an inverse Fourier transformation (FFT−1) of the edited first image 16 showing the remaining static region 14;

[0064] Step 5a (S5a): Undersampling the inverse Fourier transformation (FFT−1) of the edited first image 16 using a undersampling strategy and thereby providing an undersampled first dataset 30

[0065] Step 6 (S6): Acquiring a second dataset pertaining to a (second) cardiac phase from the cardiac region S6, wherein the second dataset is undersampled compared to the first dataset by using the undersampling strategy during acquisition of the second dataset;

[0066] Step 7 (S7): Subtraction of the undersampled first dataset 30 from the second dataset;

[0067] Step 8 (S8): Performing a Fourier transformation (FFT) on the subtracted second dataset 18; and

[0068] Step 9 (S9): Generating a second image 20 of a reduced region of interest (rROI) at least including the dynamic region 12.

[0069] As in the PINOT acquisition, the depicted procedure can distinguish two different data sets. In the first data set it is acquired those k-space lines that will gather the static and dynamic information. In the second data set a subgroup of k-space lines are acquired for every cardiac phase (heart phase) base on the idea that it can be recovered some information from the full data set to be able to do the final reconstruction. In the approach—as a difference with PINOT—it is not necessary to build the whole matrix formulation as is described in Eq. (1). In contrast the reconstruction is spitted in three different stages.

[0070] 1st Stage: In this reconstruction stage a full image 10 is generated for a single cardiac phase using the first data set described above (S2). This image 10 can be generated using conventional SENSE reconstruction or full sampling image to improve signal accuracy.

[0071] 2nd Stage: In this reconstruction stage in the full reconstructed image the previously defined dynamic region is set to 0 (S4) in order to get the information just from those static regions 14 that remain equal along all cardiac phases. In this reconstruction stage the images 16 are weighted by coil sensitivities of each coils and inverse Fourier transformed in the k-space domain (S5). The Fourier transformed images for each coil are under-sampled following the same sampling scheme as the second data set described above (indicated by the unlabeled arrow between S5 and S7). Finally, the generated k-space lines from the static region are subtracted from the updated k-space lines in every cardiac phase (S7).

[0072] 3rd Stage: In this reconstruction stage the images generated in the 2nd stage are Fourier transformed into the image space (S8) and reconstructed using conventional SENSE reconstruction but just taken the information from the reduced FoV (S9).

[0073] Following this approach the SENSE reconstruction and the NoQUIST-like reconstruction are much separated. SENSE information is just used in the third stage while the NoQUIST-like information is just used in the second stage of the reconstruction. Moreover, in the third stage of the reconstruction just a reduced region is reconstructed improving the reconstruction speed compared to conventional SENSE reconstruction for 3D cases.

[0074] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

[0075] The work leading to this invention has received funding from the European Union Seventh Framework Programme (FP-7-HEALTH-2009) under grant agreement number 242038.