Magnetic resonance projection for constructing four-dimensional image information

• US 11,064,899 B2
• Filed: 12/10/2015
• Issued: 07/20/2021
• Est. Priority Date: 12/10/2014
• Status: Active Grant

• Current Assignee: Elekta Incorporated (Elekta AB)
• Original Assignee: Elekta Incorporated (Elekta AB)
• Inventors: Lachaine, Martin Emile, Falco, Tony
• Primary Examiner(s): Jacob, Oommen
• Assistant Examiner(s): Mohammed, Shahdeep
• Application Number:
  US15/534,387
• Publication Number:
  US 20180256064A1
• Time in Patent Office: 2,049 Days
• Field of Search: None
• CPC Class Codes: A61B 2505/05 Surgical care
• CPC Class Codes: A61B 2576/00 Medical imaging apparatus involving image processing or analysis A61B1/00009, A61B6/52 and A61B8/52 take precedence; image processing per se G06T; details of algorithms for analysing medical images G06T7/0012
• 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 5/7257 using Fourier transforms
• CPC Class Codes: A61B 6/5229 combining image data of a patient, e.g. combining a functional image with an anatomical image
• CPC Class Codes: A61N 2005/1055 using magnetic resonance imaging [MRI]
• CPC Class Codes: A61N 2005/1061 using an x-ray imaging system having a separate imaging source
• CPC Class Codes: A61N 5/1037 taking into account the movement of the target, e.g. 4D-image based planning
• CPC Class Codes: A61N 5/1045 using a multi-leaf collimator, e.g. for intensity modulated radiation therapy or IMRT
• CPC Class Codes: A61N 5/1049 for verifying the position of the patient with respect to the radiation beam
• CPC Class Codes: A61N 5/1067 in real time, i.e. during treatment
• CPC Class Codes: G01R 33/4808 Multimodal MR, e.g. MR combined with positron emission tomography [PET], MR combined with ultrasound or MR combined with computed tomography [CT]
• CPC Class Codes: G01R 33/4824 using a non-Cartesian trajectory
• CPC Class Codes: G01R 33/4833 using spatially selective excitation of the volume of interest, e.g. selecting non-orthogonal or inclined slices
• 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 33/56308 Characterization of motion or flow; Dynamic imaging
• CPC Class Codes: G01R 33/56325 Cine imaging
• 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 33/567 gated by physiological signals , i.e. synchronization of acquired MR data with periodical motion of an object of interest, e.g. monitoring or triggering system for cardiac or respiratory gating
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06T 11/005 : Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• CPC Class Codes: G06T 2207/10088 : Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10116 : X-ray image
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06V 10/75 : Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
• CPC Class Codes: G16H 30/20 : for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 : for processing medical images, e.g. editing
• CPC Class Codes: G16H 40/60 : for the operation of medical equipment or devices
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

Apparatus and techniques are described herein for nuclear magnetic resonance (MR) projection imaging. Such projection imaging may be used for generating four-dimensional (4D) imaging information representative of a physiologic cycle of a subject, such as including generating two or more two-dimensional (2D) images, the 2D images comprising projection images representative of different projection angles, and the 2D images generated using imaging information obtained via nuclear magnetic resonance (MR) imaging, assigning the particular 2D images to bins at least in part using information indicative of temporal positions within the physiologic cycle corresponding to the particular 2D images, constructing three-dimensional (3D) images using the binned 2D images, and constructing the 4D imaging information, comprising aggregating the 3D images.

Claims

1. A computer-implemented method for generating four-dimensional (4D) imaging information representative of a physiologic cycle of a subject, the method comprising:

generating two or more two-dimensional (2D) projection images, the 2D projection images comprising projection images representative of different projection angles, and the 2D projection images generated using imaging information obtained via nuclear magnetic resonance (MR) projection imaging, wherein generating the two or more 2D projection images comprises at least one of acquiring a 2D MR imaging slice perpendicular to a projection angle without requiring a slice selection gradient or acquiring the 2D MR imaging slice using the slice selection gradient that defines a slice having a depth that encompasses a radiation therapy target extent in a dimension parallel to the projection angle;

assigning particular 2D images from the generated 2D projection images to bins at least in part using information indicative of temporal positions within the physiologic cycle corresponding to the particular 2D images;

constructing three-dimensional (3D) images using the binned 2D images; and

constructing the 4D imaging information, comprising aggregating the 3D images.

2. The computer-implemented method of claim 1, wherein:

the physiologic cycle comprises a respiration cycle; and

obtaining the two or more 2D projection images comprises obtaining 2D images representative of different projection angles over a duration spanning multiple respiration cycles.

3. The computer-implemented method of claim 1, further comprising causing acquisition of the 2D MR imaging slice perpendicular to the projection angle without requiring the slice selection gradient.

4. The computer-implemented method of claim 1, further comprising causing acquisition of the 2D MR imaging slice perpendicular to the projection angle using the slice selection gradient defining the slice sufficiently large in depth to encompass an entirety of the radiation therapy target extent in the dimension parallel to the projection angle.

5. The computer-implemented of claim 1, wherein the projection angles span an arc rotating about a specified central axis.

6. The computer-implemented method of claim 1, comprising:

determining a phase of a portion of the physiologic cycle corresponding to the particular 2D images; and

assigning the particular 2D images to bins using information indicative of the determined phase.

7. The computer-implemented method of claim 1, comprising:

determining an amplitude of a portion of the physiologic cycle corresponding to the particular 2D images; and

assigning the particular 2D images to bins using information indicative of the determined amplitude.

8. The computer-implemented method of claim 1, wherein one or more of a phase or an amplitude of a portion of the physiologic cycle corresponding to the particular 2D images is determined using a feature extracted from the particular 2D images.

9. The computer-implemented method of claim 1, wherein assigning the particular 2D images to bins includes using a dimensionality reduction of acquired imaging information.

10. The computer-implemented method of claim 1, wherein assigning the particular 2D images to bins includes using a Fourier Transform of the particular 2D images.

11. The computer-implemented method of claim 1, wherein constructing the 3D images from acquired 2D projection images includes using a tomographic image reconstruction technique.

12. The computer-implemented method of claim 1, wherein constructing the 3D image from the binned 2D images includes performing the 3D image construction using transformed imaging information represented in a Fourier space.

13. The computer-implemented method of claim 1, wherein constructing the 3D image from the binned 2D images includes performing the 3D image construction using a filtered back-projection technique.

14. The computer-implemented method of claim 1, wherein constructing the 3D image from the binned 2D images includes performing the 3D image construction using a compressed sensing technique.

15. The computer-implemented method of claim 1, wherein constructing the 3D image from the binned 2D images includes performing the 3D image construction using Feldman-Davi s-Kress construction.

16. The computer-implemented method of claim 1, wherein constructing the 3D image from the binned 2D images includes performing the 3D image construction using an iterative approach.

17. The computer-implemented method of claim 1, comprising providing the 4D imaging information for use in generating or adapting a radiation therapy treatment plan.

18. The computer-implemented method of claim 1, comprising using the 4D imaging information to assign or determine a position of the subject prior to delivery of a radiation therapy treatment fraction.

19. A computer-implemented method for generating four-dimensional (4D) imaging information representative of a physiologic cycle of a subject, the method comprising:

generating two or more two-dimensional (2D ) projection images, the 2D projection images comprising projection images representative of different projection angles, and the 2D images generated using imaging information obtained via nuclear magnetic resonance (MR) projection imaging, wherein generating the two or more 2D projection images comprises at least one of acquiring a 2D MR imaging slice perpendicular to a projection angle without requiring a slice selection gradient or acquiring the 2D MR imaging slice using the slice selection gradient that defines a slice having a depth that encompasses a radiation therapy target extent in a dimension parallel to the projection angle;

assigning particular 2D images from the generated 2D projection images to bins at least in part using information indicative of temporal positions within the physiologic cycle corresponding to the particular 2D images;

constructing three-dimensional (3D) images using the binned 2D images; and

constructing the 4D imaging information, comprising aggregating the 3D images;

wherein the physiologic cycle comprises a respiration cycle;

wherein obtaining the two or more 2D images comprises obtaining 2D images representative of different projection angles over a duration spanning multiple respiration cycles; and

wherein one or more of a phase or amplitude of a portion of the physiologic cycle corresponding to the particular 2D images is determined using a feature extracted from the particular 2D images.

20. The computer-implemented method of claim 19, further comprising causing acquisition of the 2D MR imaging slice perpendicular to the projection angle without requiring the slice selection gradient.

21. The computer-implemented method of claim 19, further comprising causing acquisition of the 2D MR imaging slice perpendicular to the projection angle using the slice selection gradient defining the slice sufficiently large in depth to encompass an entirety of the radiation therapy target extent in the dimension parallel to the projection angle.

22. An imaging system, comprising:

at least one processor circuit and a processor-readable storage medium, the processor readable storage medium including instructions that, when performed by the processor circuit, cause the processor circuit to generate four-dimensional (4D) imaging information representative of a physiologic cycle of a subject, including performing operations comprising;

generating two or more two-dimensional (2D ) projection images, the 2D projection images comprising projection images representative of different projection angles, and the 2D images generated using imaging information obtained via nuclear magnetic resonance (MR) projection imaging, wherein generating the two or more 2D projection images comprises at least one of acquiring a 2D MR imaging slice perpendicular to a projection angle without requiring a slice selection gradient or acquiring the 2D MR imaging slice using the slice selection gradient that defines a slice having a depth that encompasses a radiation therapy target extent in a dimension parallel to the projection angle;

assigning particular 2D images from the generated 2D images to bins at least in part using information indicative of temporal positions within the physiologic cycle corresponding to the particular 2D images;

constructing three-dimensional (3D) images using the binned 2D images; and

constructing the 4D imaging information, comprising aggregating the 3D images.

23. The imaging system of claim 22, further comprising operations for causing acquisition of the 2D MR imaging slice perpendicular to the projection angle without requiring the slice selection gradient.

24. The imaging system of claim 22, further comprising operations for causing acquisition of the 2D MR imaging slice perpendicular to the projection angle using the slice selection gradient defining the slice sufficiently large in depth to encompass an entirety of the radiation therapy target extent in the dimension parallel to the projection angle.