Algorithm-based artifact reduction in patients with arms-down positioning in computed tomography

Kawashima, Hiroki; Ichikawa, Katsuhiro; Takata, Takushi; Mitsui, Wataru

doi:10.1016/j.ejmp.2019.11.019

Cited by 5 publications

(1 citation statement)

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“…These cases are the same ones used in a previous study of our laboratory team [ 14 ] that purposed improvement of image texture in the MAR image generated using original projection data. To quantitatively compare the artifact strengths of the two NMAR methods, we measured the normalized artifact index AI n , which is defined as follows: where SD A denotes the standard deviation (SD) measured of the CT image with metal artifacts; SD B is the SD measured of the CT image with no metal artifacts [ 15 ]. The normalization using SD B was adopted because the spatial resolution of the image reconstructed based on the FP-data is slightly different from that of the image reconstructed from the original projection data.…”

Section: Methodsmentioning

confidence: 99%

An image-based metal artifact reduction technique utilizing forward projection in computed tomography

Ichikawa,

Kawashima,

Takata

2024

Radiol Phys Technol

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The projection data generated via the forward projection of a computed tomography (CT) image (FP-data) have useful potentials in cases where only image data are available. However, there is a question of whether the FP-data generated from an image severely corrupted by metal artifacts can be used for the metal artifact reduction (MAR). The aim of this study was to investigate the feasibility of a MAR technique using FP-data by comparing its performance with that of a conventional robust MAR using projection data normalization (NMARconv). The NMARconv was modified to make use of FP-data (FPNMAR). A graphics processing unit was used to reduce the time required to generate FP-data and subsequent processes. The performances of FPNMAR and NMARconv were quantitatively compared using a normalized artifact index (AIn) for two cases each of hip prosthesis and dental fillings. Several clinical CT images with metal artifacts were processed by FPNMAR. The AIn values of FPNMAR and NMARconv were not significantly different from each other, showing almost the same performance between these two techniques. For all the clinical cases tested, FPNMAR significantly reduced the metal artifacts; thereby, the images of the soft tissues and bones obscured by the artifacts were notably recovered. The computation time per image was ~ 56 ms. FPNMAR, which can be applied to CT images without accessing the projection data, exhibited almost the same performance as that of NMARconv, while consuming significantly shorter processing time. This capability testifies the potential of FPNMAR for wider use in clinical settings.

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Section: Methodsmentioning

confidence: 99%