Metal Artifact Reduction by Morphological Image Filtering for Computed Tomography

Abstract

When metal implants are present in the field of measurement, artifacts degrade image quality. Metal artifact reduction (MAR) methods produce images with improved quality leading to confident and reliable clinical diagnosis. Currently, there are many methods developed, but no generally accepted solution to this issue has been found. In this work we propose a morphological image filtering approach for metal artifact reduction (MIFMAR) algorithm for image quality improvement. MIFMAR performance was compared with three well-known MAR methods, which are linear interpolation (LI), normalized metal artifact reduction (NMAR) and frequency split metal artifact reduction (FSMAR), using clinical studies. The methods were applied to images acquired from 30 clinical studies of patients with metallic implants. Image quality was evaluated by three experienced radiologists completely blinded to the information about if the image was processed or not to suppress the artifacts. They graded image quality in a five points-scale, where zero is an index of clear artifact presence, and five, a whole artifact suppression. Image quality on images were compared using the non-parametric Friedman-ANOVA test. Inter-observer agreement was evaluated using linear-weighted κ test. MIFMAR ensures efficient reduction of metal artifacts with high image quality, preserving all of tissues and details in CT images. Image quality and diagnostic scores improved significantly (p < 0.01) with good inter-observer agreement. MIFMAR is computationally inexpensive compared with other methods and does not use raw CT data.