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Updated: Jul 13, 2025

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

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Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts.

Alex Ling Yu Hung¹, Edward Chen², John Galeotti^1,2

¹Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.

|October 18, 2023

Summary

This summary is machine-generated.

This study introduces a new algorithm for segmenting ultrasound artifacts caused by needles. The method improves image analysis by accurately separating artifact pixels from tissue pixels.

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Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Reverberation artifacts from needles degrade ultrasound image quality.
These artifacts are challenging for current computer vision algorithms.
Artifact boundaries are ambiguous, causing expert labeling disagreements.

Purpose of the Study:

To develop a weakly- and semisupervised algorithm for segmenting needle and reverberation artifacts.
To separate tissue pixel values from superimposed artifacts.
To model artifact intensity decay and minimize human labeling error.

Main Methods:

A three-part learning-based framework was employed.
A probabilistic segmentation network generated soft labels from human inputs.
A transform function and subsequent network generated final artifact masks.

Main Results:

The algorithm differentiates between artifact-free and artifact regions.
It accurately models the intensity fall-off within artifacts.
Performance was compared favorably against other segmentation algorithms.

Conclusions:

The method achieves state-of-the-art artifact segmentation performance.
It provides a new standard for estimating per-pixel artifact contributions.
The algorithm enhances downstream medical image analysis tasks.