A Novel Method for Segmentation-Based Semiautomatic Quantitative Evaluation of Metal Artifact Reduction Algorithms.
Thuy D Do, Christof M Sommer1, Claudius Melzig
1Clinic for Diagnostic and Interventional Radiology, Klinikum Stuttgart, Stuttgart.
Investigative Radiology
|May 4, 2019
Summary
Objective segmentation effectively evaluates metal artifact reduction algorithms in CT scans for microwave ablation. This method correlates well with subjective assessments, offering a precise and efficient approach.
Area of Science:
- Medical Imaging
- Radiology
- Interventional Radiology
Background:
- Percutaneous microwave ablation requires accurate imaging for applicator placement.
- Metal artifacts in CT scans can obscure critical structures.
- Evaluating metal artifact reduction (MAR) algorithms is crucial for image quality.
Purpose of the Study:
- To establish an objective, segmentation-based evaluation method for MAR algorithms.
- To assess MAR algorithm performance in a porcine model during microwave ablation.
- To compare objective and subjective evaluations of MAR effectiveness.
Main Methods:
- CT data from a porcine model undergoing microwave ablation were reconstructed using 6 algorithms (with/without MAR).
- 3D segmentation was used to quantify artifact volume in liver parenchyma.
- Objective artifact volume and subjective image quality were assessed and correlated.
Main Results:
- Dedicated MAR algorithms significantly reduced metal artifacts compared to standard reconstruction (P < 0.05).
- Iterative reconstruction alone did not significantly reduce artifacts (P > 0.05).
- A good correlation (rs = 0.65) was found between objective and subjective evaluations.
Conclusions:
- Segmentation-based evaluation provides a precise and efficient objective measure of MAR performance.
- This method aligns well with traditional subjective assessments.
- It offers a promising quantitative approach for evaluating MAR algorithms in interventional procedures.
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