Magnetic resonance projection imaging

• US 10,791,958 B2
• Filed: 05/13/2019
• Issued: 10/06/2020
• 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): Ippolito, Nicole M
• Application Number:
  US16/410,298
• Publication Number:
  US 20190261885A1
• Time in Patent Office: 512 Days
• Field of Search: 2504921, 2504923
• 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 to control radiation therapy delivery to a subject, such as including receiving reference imaging information, generating a two-dimensional (2D) projection image using imaging information obtained via nuclear magnetic resonance (MR) imaging, the 2D projection image corresponding to a specified projection direction, the specified projection direction including a path traversing at least a portion of an imaging subject, determining a change between the generated 2D projection image and the reference imaging information, and controlling delivery of the radiation therapy at least in part using the determined change between the obtained 2D projection image and the reference imaging information.

Claims

1. A computer-implemented method comprising:

generating, using one or more processors, a plurality of two-dimensional (2D) magnetic resonance (MR) projection image data representing different projection angles;

binning, using the one or more processors, the plurality of 2D MR projection image data using information indicative of temporal position within a physiologic cycle; and

constructing, using the one or more processors, a three-dimensional (3D) image representation using the binned plurality of 2D MR projection image data.

2. The computer-implemented method of claim 1 further comprising constructing four-dimensional (4D) imaging information by aggregating 3D image representations including the 3D image representation.

3. The computer-implemented method of claim 1 further comprising:

receiving reference imaging information;

determining a change between the generated 3D image representation and the reference imaging information; and

generating a therapy protocol for delivery of radiation therapy at least in part using the determined change.

4. The computer-implemented method of claim 1, wherein generating the plurality of 2D MR projection image data comprises aggregating one-dimensional (1D) projection lines acquired from the MR projection imaging, wherein depth information along a given 1D projection line of the 1D projection lines is compressed into a single pixel value associated with the given 1D projection line.

5. The computer-implemented method of claim 4, wherein the 1D projection lines are oriented to spatially diverge from one another.

6. The computer-implemented of claim 1, wherein the projection angles are specified to capture projection directions around an imaging subject.

7. The computer-implemented of claim 1, wherein constructing the 3D image representation comprises updating a previous 3D image representation with new information.

8. The computer-implemented of claim 1, wherein binning the plurality of 2D MR projection image data comprises:

obtaining a function that represents the physiological cycle as a function of time;

determining a portion of time of the function on which a given 2D MR projection image data of the plurality of 2D MR projection image data falls; and

associating the given 2D MR projection image data with a bin associated with the determined portion of time.

9. The computer-implemented of claim 1, wherein binning the plurality of 2D MR projection image data comprises:

obtaining a function that represents the physiological cycle as a function of amplitude ranges;

determining an amplitude range of the function on which a given 2D MR projection image data of the plurality of 2D MR projection image data falls; and

associating the given 2D MR projection image data with a bin associated with the determined amplitude range.

10. The computer-implemented method of claim 1, wherein generating the plurality of 2D projection image data comprises at least one of acquiring a 2D MR imaging slice perpendicular to a projection angle without requiring a slice selection gradient, or acquiring a 2D MR imaging slice perpendicular to a projection angle using a slice selection gradient defining a slice sufficiently large in depth to encompass an entirety of a radiation therapy target extent in a dimension parallel to the projection angle.

11. A radiation therapy system, comprising:

one or more processors configured to;

generate a plurality of two-dimensional (2D) magnetic resonance (MR) projection image data representing different projection angles;

bin the plurality of 2D MR projection image data using information indicative of temporal position within a physiologic cycle; and

construct a three-dimensional (3D) image representation using the binned plurality of 2D MR projection image data.

12. The radiation therapy system of claim 11, wherein the one or more processors are further configured to construct four-dimensional (4D) imaging information by aggregating 3D images including the 3D image representation.

13. The radiation therapy system of claim 11, wherein the one or more processors are further configured to:

receive reference imaging information;

determine a change between the generated 3D image representation and the reference imaging information; and

generate a therapy protocol for delivery of radiation therapy at least in part using the determined change.

14. The radiation therapy system of claim 11, wherein the one or more processors are further configured to generate the plurality of 2D MR projection images by aggregating one-dimensional (1D) projection lines acquired from the MR projection imaging, wherein depth information along a given 1D projection line of the 1D projection lines is compressed into a single pixel value associated with the given 1D projection line.

15. The radiation therapy system of claim 11, wherein the projection angles are specified to capture projection directions around an imaging subject.

16. The radiation therapy system of claim 11, wherein the one or more processors are further configured to bin the plurality of 2D MR projection image data by:

obtaining a function that represents the physiological cycle as a function of time;

determining a portion of time of the function on which a given 2D MR projection image data of the plurality of 2D MR projection image data falls; and

associating the given 2D MR projection image data with a bin associated with the determined portion of time.

17. The radiation therapy system of claim 11, wherein the one or more processors are further configured to bin the plurality of 2D MR projection image data by:

obtaining a function that represents the physiological cycle as a function of amplitude ranges;

determining an amplitude range of the function on which a given 2D MR projection image data of the plurality of 2D MR projection image data falls; and

associating the given 2D MR projection image data with a bin associated with the determined amplitude range.

18. A non-transitory computer-readable medium encoded with non-transitory computer-readable instructions for instructing one or more processors to perform operations comprising:

generating a plurality of two-dimensional (2D) magnetic resonance (MR) projection image data representing different projection angles;

binning the plurality of 2D MR projection image data using information indicative of temporal position within a physiologic cycle; and

constructing a three-dimensional (3D) image representation using the binned plurality of 2D MR projection image data.

19. The non-transitory computer-readable medium of claim 18, wherein binning the plurality of 2D MR projection image data comprises:

obtaining a function that represents the physiological cycle as a function of time;

determining a portion of time of the function on which a given 2D MR projection image data of the plurality of 2D MR projection image data falls; and

associating the given 2D MR projection image data with a bin associated with the determined portion of time.

20. The non-transitory computer-readable medium of claim 18, wherein binning the plurality of 2D MR projection image data comprises:

obtaining a function that represents the physiological cycle as a function of amplitude ranges;

determining an amplitude range of the function on which a given 2D MR projection image data of the plurality of 2D MR projection image data falls; and

associating the given 2D MR projection image data with a bin associated with the determined amplitude range.