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1. WO2020193549 - SLICE ALIGNMENT FOR SHORT AXIS CARDIAC MR CINE SLICE STACKS

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS

1. A system (100) for slice alignment of short axis cardiac magnetic resonance cine slice stacks, comprising:

an input interface (120) for accessing image data (030) of an input set of image slices acquired using a short axis cardiac magnetic resonance cine protocol;

a processor subsystem (140) configured to:

access trained model data (050) defining a machine trained model, wherein the machine trained model is trained on training data comprising image data of a training set of image slices acquired using a short axis cardiac magnetic resonance cine protocol, wherein one or more adjacent image slices are mutually misaligned, and wherein the training data further comprises shift values for reducing said mutual misalignment by shifting one or more of the image slices;

apply the machine trained model to sets of adjacent image slices (400) of the input set of image slices, thereby obtaining at least one shift value for at least one of the image slices of the sets of adjacent image slices; and

shift said image slice based on the shift value.

2. The system (100) according to claim 1, wherein the processor subsystem (140) is configured to:

apply the machine trained model to the sets of adjacent image slices (400) of the input set of image slices to obtain a series of shift values;

remove an offset or linear trend from the series of shift values;

shift respective image slices of the sets of adjacent image slices based on respective shift values of the series of shift values.

3. The system (100) according to claim 1 or 2, wherein the machine trained model is configured and trained to use as further input positional information which is indicative of a position of respective sets of adjacent image slices relative to a cardiac structure which is shown in the input set of image slices.

4. The system (100) according to any one of claims 1 to 3, wherein the machine trained model is configured and trained to use as further input angular information which is indicative of an orientation of a cardiac structure which is shown in the input set of image slices, relative to a coordinate system associated with the input set of image slices.

5. The system (100) according to claim 3 or 4, wherein the processor subsystem (140) is configured to obtain at least one of the positional information and the angular information by segmenting the cardiac structure in the input set of image slices, for example by applying a deformable surface model to the image data.

6. The system (100) according to claim 5, wherein the processor subsystem (140) is configured to mask a part of the image data of the input set of image slices which does not belong to the cardiac structure before applying the machine trained model to the sets of adjacent image slices of the input set of image slices.

7. The system (100) according to any one of claims 1 to 6, wherein the input set of image slices is a first set of image slices (400), wherein the input interface is configured for accessing image data of a second set of image slices (402) acquired during a different cardiac phase than the first set of image slices, and wherein the machine trained model is configured and trained to use spatially corresponding samples of the first set of image slices and the second set of image slices as joint input.

8. A computer-readable medium (800) comprising transitory or non-transitory data (810) defining a machine trained model, wherein the machine trained model is configured and trained to be applied to a set of adjacent image slices of a set of image slices acquired using a short axis cardiac magnetic resonance cine protocol, wherein the machine trained model is trained to output a shift value if the set of adjacent image slices is mutually misaligned for reducing mutual misalignment.

9. A system (100) for slice alignment of short axis cardiac magnetic resonance cine slice stacks, comprising:

an input interface (120) for accessing image data (030) representing a set of image slices acquired using a short axis cardiac magnetic resonance cine protocol;

a processor subsystem (140) configured to:

access surface model data (060) defining a deformable surface model for segmenting a cardiac structure in short axis cardiac MR cine slice stacks, wherein deformability of the surface model is constrained by shape regularization;

adapt the surface model to the cardiac structure by detecting boundary points of the cardiac structure in the image data and deforming the surface model towards the boundary points to obtain an adapted surface model which is adapted in shape to the cardiac structure in the image data; and

shift (SI -S3) at least one image slice relative to other image slices so that the boundary points in the image slice obtain an improved match with a cross-sectional representation of the surface model in the respective image slice.

10. The system (100) according to claim 9, wherein the processor subsystem (140) is configured to deform the surface model towards the boundary points of the cardiac structure based on a cost function penalizing a distance of the surface model to the boundary points, and to shift the at least one image slice relative to the other image slices so that the match is improved according to the cost function.

11. The system (100) according to claim 9 or 10, wherein the processor subsystem (140) is configured for iterative slice alignment by repeating said adapting of the surface model and said shifting of the at least one image slice at least twice.

12. The system (100) according to any one of claims 9 to 11, wherein the processor subsystem (140) is configured to:

after adapting the surface model, obtain a series of shift values for respective image slices of the set of image slices to obtain the improved match with the sectional representation of the surface model in the respective image slices;

remove an offset or linear trend from the series of shift values;

shift the respective image slices based on the respective shift values of the series of shift values.

13. A computer-implemented method (600) for slice alignment of short axis cardiac magnetic resonance cine slice stacks, comprising:

accessing (610) image data of an input set of image slices acquired using a short axis cardiac magnetic resonance cine protocol;

accessing (620) trained model data defining a machine trained model, wherein the machine trained model is trained on training data comprising image data of a training set of image slices acquired using a short axis cardiac magnetic resonance cine protocol, wherein one or more adjacent image slices are mutually misaligned, and wherein the training data further comprises shift values for reducing said mutual misalignment by shifting one or more of the image slices;

applying (630) the machine trained model to sets of adjacent image slices of the input set of image slices, thereby obtaining at least one shift value for at least one of the image slices of the sets of adjacent image slices; and

shifting (640) said image slice based on the shift value.

14. A computer-implemented method (700) for slice alignment of short axis cardiac magnetic resonance cine slice stacks, comprising:

accessing (710) image data representing a set of image slices acquired using a short axis cardiac magnetic resonance cine protocol;

accessing (720) surface model data defining a deformable surface model for segmenting a cardiac structure in short axis cardiac MR cine slice stacks, wherein

deformability of the surface model is constrained by shape regularization;

adapting (730) the surface model to the cardiac structure by detecting boundary points of the cardiac structure in the image data and deforming the surface model towards the boundary points to obtain an adapted surface model which is adapted in shape to the cardiac structure in the image data; and

shifting (740) at least one image slice relative to other image slices so that the boundary points in the image slice obtain an improved match with a cross-sectional representation of the surface model in the respective image slice.

15. A computer-readable medium (800) comprising transitory or non-transitory data (810) representing a computer program, the computer program comprising instructions for causing a processor system to perform the method according to claim 13 or 14.