Apparatus and method for the processing of sectional images

Abstract

The invention relates to an apparatus and a method for the processing of reconstructed 3D images (I) in C-arm based volume imaging which often exhibit spatially slowly varying inhomogeneities caused by inconsistent projection data. To correct the images (I), a retrospective homogenization procedure is proposed. The image (I) is segmented (11, 12) into principal classes like bone, tissue and air based on their gray values. Only the tissue-regions (M) are then used as support in order to fit (14) a spatially slowly varying 2D baseline (B) representing the smooth shape of cupping or other inhomogeneities. Finally the inverse of the estimated 2D baseline is subtracted from the original slice (I) to correct for the inhomogeneities.

Description

technical field [0001] The present invention relates to a method and a device for processing cross-sectional images reconstructed from X-ray projections of an object, preferably from cone beam projections. Background technique [0002] Reconstructed 3D cross-sectional images in C-arm-based volumetric imaging usually spatially exhibit slowly varying inhomogeneities caused by inconsistent projection data for various reasons. Firstly, scattered radiation constitutes one of the main problems. Especially for system geometries with large cone angles and thus large illuminated areas, such as C-arm based volume imaging, scattered radiation can create a significant background that is added to the desired detected signal, and this can lead to significant cupping Inhomogeneities in cupping shape appear within the reconstructed body. Other sources of inhomogeneity are beam hardening (cupping) and insufficient truncation correction (cupping and anti-cupping). [0003] When applying a ...

AI Technical Summary

Problems solved by technology

So in the presence of inhomogeneities, it is difficult to find low-contrast details

[0004] The anti-scatter grid reduces the amount of detected scattered radiation, but such reduction is not sufficient for typical system geometries for volumetric imaging due to the additional increase in signal-to-noise ratio

Various algorithms have been proposed for projection-based scatter compensation (see "Computerized scatter correction indiagnostic radiology" by Maher K.P., Malone J.F., Contemporary Physics, vol.38, no.2, pp. 131-148, 1997) , but it is difficult to achieve accurate quantitative scattering estimation

Truncation correction schemes like projected elliptic extension (introduced in "Processing of incompletemeasurement data in computed tomography" by R.M.Lewitt, Med. Phys., vol.5, no.5, pp. 412-417, 1979)) can Prevents severe occlusion artifacts at FOV boundaries, but does not fully correct global occlusion

Beam hardening correction is possible but requires a deep knowledge of the acquisition parameters and is often not possible

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