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Related Experiment Video

Updated: Jun 1, 2026

Measuring the Complete-arch Distortion of an Optical Dental Impression

Measuring the Complete-arch Distortion of an Optical Dental Impression

Published on: May 30, 2019

An opposite view data replacement approach for reducing artifacts due to metallic dental objects.

Mehran Yazdi¹, Meghdad Asadi Lari, Gaston Bernier

¹Department of Electrical Engineering, School of Engineering, Shiraz University, Shiraz 7134851154, Iran.

Medical Physics

|June 2, 2011

Summary

A new metal artifact reduction (MAR) method, opposite view replacement, effectively reduces artifacts from small dental objects in CT scans. This technique improves image quality for patients with metallic dental work.

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Area of Science:

Medical Imaging
Radiology
Computational Imaging

Background:

Metal artifacts significantly degrade CT image quality, particularly in dental and head/neck imaging.
Existing metal artifact reduction (MAR) methods struggle with small, numerous metallic objects like dental fillings.

Purpose of the Study:

To introduce a novel metal artifact reduction (MAR) algorithm designed for small, multiple metallic objects in CT scans.
To address limitations of current MAR techniques in scenarios with dental fillings.

Main Methods:

Developed an 'opposite view replacement' algorithm to detect and replace metal-affected projection data.
Incorporated a fading process to ensure sinogram continuity at artifact boundaries.
Validated the algorithm using a skull phantom with dental inserts and a clinical head and neck case.

Main Results:

The opposite view replacement method significantly improved CT image quality compared to interpolation-based MAR algorithms.
Demonstrated effective recovery of CT numbers adjacent to metallic objects in phantom studies.
Clinical case showed substantial artifact reduction in head and neck imaging.

Conclusions:

The opposite view replacement technique offers an efficient solution for MAR in the presence of multiple small metallic objects.
Results from phantom and clinical data confirm the algorithm's efficacy and validate the approach.