A metal artifact reduction method for small field of view CT imaging

Abstract

Several sinogram inpainting based metal artifact reduction (MAR) methods have been proposed to reduce metal artifact in CT imaging. The sinogram inpainting method treats metal trace regions as missing data and estimates the missing information. However, a general assumption with these methods is that data truncation does not occur and that all metal objects still reside within the field-of-view (FOV). These assumptions are usually violated when the FOV is smaller than the object. Thus, existing inpainting based MAR methods are not effective. In this paper, we propose a new MAR method to effectively reduce metal artifact in the presence of data truncation. The main principle of the proposed method involves using a newly synthesized sinogram instead of the originally measured sinogram. The initial reconstruction step involves obtaining a small FOV image with the truncation artifact removed. The final step is to conduct sinogram inpainting based MAR methods, i.e., linear and normalized MAR methods, on the synthesized sinogram from the previous step. The proposed method was verified for extended cardiac-torso simulations, clinical data, and experimental data, and its performance was quantitatively compared with those of previous methods (i.e., linear and normalized MAR methods directly applied to the originally measured sinogram data). The effectiveness of the proposed method was further demonstrated by reducing the residual metal artifact that were present in the reconstructed images obtained using the previous method.

Virtual monochromatic imaging with projection-based material decomposition algorithm for metal artifacts reduction in photon-counting detector computed tomography

“…The logarithm projections P k = −ln(N k /N 0k ) can be approximately linearized with the effective thicknesses A m1 and A m2 [34]. The purpose of the projection-based MD method is to solve A m1 and A m2 in Eq (5).…”

“…The second approach is based on image post-processing. One of the most widely known methods is the sinogram inpainting algorithm, in which the metal trace areas are determined by the sinogram of metal items and replaced with suitable correct values [5]. Two major methods are used to fill in the metal trace areas: linear MAR (LMAR) that uses linear interpolation with the surrounding values [6] and normalized MAR (NMAR) [7] that uses the initially corrected images from LMAR as a prior image and normalizes the sinogram to minimize the interpolation errors within the metal trace areas.…”

Deep Learning Based Projection Domain Metal Segmentation for Metal Artifact Reduction in Cone Beam Computed Tomography

“…As forward projection of the image-domain metal is used to obtain the metal traces in the projections, it has several problems. When metal lies outside of the field-of-view (FOV), image-domain segmentation methods cannot segment metals in the projections [21]. This may cause image inhomogeneity in the center and among the different zones of the FOV [22], [23].…”