Method for determining corrected acquisition geometries of projection images

Abstract

A method is provided for determining corrected acquisition geometries of projection images. The method includes providing a projection image dataset that has a plurality of projection images of an object under examination acquired by an acquisition device in different acquisition geometries. The method further includes determining a provisional acquisition geometry for each of the projection images by a first optimization method by minimizing a first cost function by varying the provisional acquisition geometry, wherein the first cost function is contingent on a plurality of consistency measures determined based on the provisional acquisition geometry for a respective pair of projection images. The method further includes determining the respective corrected acquisition geometry for each of the projection images by a second optimization method by minimizing a second cost function by varying the corrected acquisition geometries, wherein the second cost function is contingent on a measure for an image quality of image data reconstructed based on the projection images and the corrected acquisition geometries, and wherein, in a first iteration act of the second optimization method, the provisional acquisition geometries determined by the first optimization method are used as corrected acquisition geometries.

Description

[0001]The present patent document claims the benefit of European Patent Application No. 19161295.1, filed Mar. 7, 2019, which is hereby incorporated by reference.TECHNICAL FIELD[0002]The disclosure relates to a method for determining corrected acquisition geometries of projection images. The disclosure also relates to a processing device, a computer program, and a computer-readable medium.BACKGROUND[0003]Particularly in the field of medical imaging, methods are used in which a plurality of projection images are acquired in order to reconstruct three-dimensional image data from them. For example, x-ray based three-dimensional imaging is used as an important source of data in the context of medical diagnostics. In order to generate sufficient data for a representation that is as accurate as possible, an acquisition device, (e.g., an X-ray detector with an associated x-ray source), is moved around an object under examination on a circular, circular-segment-shaped, or spiral trajectory....

AI Technical Summary

Benefits of technology

[0013]Within the scope of the disclosure, it is recognized that by sequentially using the two optimization methods cited, surprisingly high correction quality may be achieved with relatively low computational overhead. It is realized that the weaknesses of image-quality-based optimization methods, namely a lack of robustness in particular application situations and relatively high computational overhead, may be reduced or even completely avoided by pre-optimization being performed by a method that corrects the acquisition geometry based on the expected consistencies between different projection images.

[0014]As consistencies between the projection images are evaluated in the first optimization method, compute-intensive reconstruction, e.g., a back-projection, and forward projections may be dispensed with, so that the first optimization method may be carried out with low computational overhead. At the same time, however, such an optimization method, as explained in the introduction, cannot robustly compensate all the acquisition geometry errors. If all the acquisition geometries describe a movement of the acquisition device within a plane in each case, the displacements or rotations within that plane may only be detected / corrected to a limited extent. However, this weakness of this optimization method may in turn be compensated by following it with the second optimization method. Because of the preliminary correction by the first optimization method, the search space for the correct acquisition geometry is significantly reduced within the framework of the second optimization method, so that the second optimization method may also be carried out with acceptable computational overhead. The two optimization methods therefore interact synergistically to provide robust and accurate correction of acquisition geometries with minimal complexity.

Problems solved by technology

It is realized that the weaknesses of image-quality-based optimization methods, namely a lack of robustness in particular application situations and relatively high computational overhead, may be reduced or even completely avoided by pre-optimization being performed by a method that corrects the acquisition geometry based on the expected consistencies between different projection images.

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