Artificial intelligence-based metal artefact correction algorithm for radiotherapy patients with dental hardware in
Xiaoli Yu1,2, Sihua Zhong3, Guozhi Zhang3
1Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou 510000, China.
Objectives:
To investigate the clinical efficiency of an artificial intelligence-based metal artefact correction algorithm (AI-MAC) for reducing dental metal artefacts in head and neck CT, compared to conventional interpolation-based metal artefact correction (MAC).
Methods:
We retrospectively collected 41 patients with non-removal dental hardware who underwent non-contrast head and neck CT prior to radiotherapy. All images were reconstructed with the standard reconstruction algorithm (SRA) and were additionally processed with both conventional MAC and AI-MAC. The image quality of SRA, MAC, and AI-MAC was compared by qualitative scoring on a 5-point scale, with scores ≥ 3 considered interpretable. This was followed by a quantitative evaluation, including signal-to-noise ratio (SNR) and artefact index (Idxartefact). Organ contouring accuracy was quantified via calculating the dice similarity coefficient (DSC) and hausdorff distance (HD) for the oral cavity and teeth, using the clinically accepted contouring as reference. Moreover, the treatment planning dose distribution for the oral cavity was assessed.
Results:
AI-MAC yielded superior qualitative image quality as well as quantitative metrics, including SNR and Idxartefact, to SRA and MAC. The image interpretability significantly improved from 41.46% for SRA and 56.10% for MAC to 92.68% for AI-MAC (P < .05). Compared to SRA and MAC, the best DSC and HD for both oral cavity and teeth were obtained on AI-MAC (all P < .05). No significant differences for dose distribution were found among the 3 image sets.
Conclusion:
AI-MAC outperforms conventional MAC in metal artefact reduction, achieving superior image quality with high image interpretability for patients with dental hardware undergoing head and neck CT. Furthermore, the use of AI-MAC improves the accuracy of organ contouring while providing consistent dose calculation against metal artefacts in radiotherapy.
Advances In Knowledge:
AI-MAC is a novel deep learning-based technique for reducing metal artefacts on CT. This in vivo study demonstrated its capability of reducing metal artefacts while preserving organ visualization, as compared with conventional MAC.
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