Physics-informed sinogram completion for metal artifact reduction in non-contrast brain CT images with neurovascular coils: comparison with traditional and deep learning-based methods.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, 510282, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510282, China.
- Pazhou Lab (Huangpu), Guangzhou, Guangdong, 510282, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510282, China. [email protected].
- Pazhou Lab (Huangpu), Guangzhou, Guangdong, 510282, China. [email protected].
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, 510282, China. [email protected].
Abstract
Metal artifacts from neurovascular coil affect image quality in computed tomography (CT), we aim to use Physics-informed sinogram completion (PISC) to reduce metal artifacts, and compare with two traditional metal artifact reduction (MAR) methods -- Normalized MAR (NMAR), Metal Artifact Reduction for Orthopedic Implants (O-MAR) and two deep learning (DL) based methods -- convolutional neural network based metal artifact reduction (CNN-MAR) and dual domain network (DuDoNet). 40 consecutive patients who underwent endovascular coil embolization for intracranial aneurysms between July 2021 to December 2022 were included, all of whom underwent brain CT examinations. Above methods were compared quantitatively by calculating the artifact index (AI). Two blinded radiologists independently evaluated these MAR methods using a five-point scale, assessing metal artifact severity and diagnose confidence, resolution, new artifacts and the contours of different soft tissue interfaces. Friedman M test was used for quantitative and qualitative evaluation. The AI value were significantly lower in DuDoNet images when compared with FBP, NMAR, O-MAR, CNN-MAR images (<i>p <</i> 0.001), although no statistically significant when compared with PISC images (<i>p =</i> 0.181). For metal artifact severity and diagnosis confidence score, PISC method was significantly higher than FBP, NMAR, O-MAR and DuDoNet methods (all <i>p <</i> 0.05), although no statistically significant when compared with CNN-MAR method (<i>p =</i> 1.000). The resolution and contours of different soft tissue interfaces score were lower in DuDoNet images when compared with other images (all <i>p</i> < 0.001). The PISC method introduces the least new artifacts among these MAR methods. In addition, using traditional or DL based methods, we found new lesion obscured by metal artifacts in two cases. For quantitative image analysis, DuDoNet achieved the best image quality. For qualitative image analysis, PISC achieved the best image quality. DuDoNet method can cause over-smoothing and blurring effect. PISC method introduce least new artifact.