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Purpose: To evaluate the effects of single-energy metal artifact reduction (SEMAR) on image quality of ultra-high-resolution CT-angiography (UHR-CTA) with intracranial implants after aneurysm treatment. Methods: Image quality of standard and SEMAR-reconstructed UHR-CT-angiography images of 54 patients who underwent coiling or clipping was retrospectively evaluated. Image noise (i.e., index for metal-artifact strength) was analyzed in close proximity to and more distally from the metal implant. Frequencies and intensities of metal artifacts were additionally measured and intensity-differences between both reconstructions were compared in different frequencies and distances. Qualitative analysis was performed by two radiologists using a four-point Likert-scale. All measured results from both quantitative and qualitative analysis were then compared between coils and clips. Results: Metal artifact index (MAI) and the intensity of coil-artifacts were significantly lower in SEMAR than in standard CTA in close vicinity to and more distally from the coil-package (p < 0.001, each). MAI and the intensity of clip-artifacts were significantly lower in close vicinity (p = 0.036; p < 0.001, respectively) and more distally from the clip (p = 0.007; p < 0.001, respectively). In patients with coils, SEMAR was significantly superior in all qualitative categories to standard images (p < 0.001), whereas in patients with clips, only artifacts were significantly less (p < 0.05) for SEMAR. Conclusion: SEMAR significantly reduces metal artifacts in UHR-CT-angiography images with intracranial implants and improves image quality and diagnostic confidence. SEMAR effects were strongest in patients with coils, whereas the effects were minor in patients with titanium-clips due to the absent of or minimal artifacts.

Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The 2022 intracranial hemorrhage segmentation on non-contrast head CT (INSTANCE 2022) was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing. The INSTANCE released a training set of 100 cases with ground-truth and a validation set with 30 cases without ground-truth labels that were available to the participants. A held-out testing set with 70 cases is utilized for the final evaluation and ranking. The methods from different participants are ranked based on four metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), Relative Volume Difference (RVD) and Normalized Surface Dice (NSD). A total of 13 teams submitted distinct solutions to resolve the challenges, making several baseline models, pre-processing strategies and anisotropic data processing techniques available to future researchers. The winner method achieved an average DSC of 0.6925, demonstrating a significant growth over our proposed baseline method. To the best of our knowledge, the proposed INSTANCE challenge releases the first intracranial hemorrhage segmentation benchmark, and is also the first challenge that intended to resolve the anisotropic problem in 3D medical image segmentation, which provides new alternatives in these research fields.

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