Cerebral arteries segmentation based on projection domain in single exposure computed tomographic angiography.

摘要

BACKGROUND: Cerebrovascular diseases are major causes of death and disability worldwide, highlighting the critical importance of early diagnosis and accurate acquisition of vascular information. However, conventional imaging techniques using direct subtraction computed tomography (CT) and dual energy CT have limitations, including invasiveness, radiation exposure, and artifacts caused by metal and bone. PURPOSE: This study investigated the feasibility of cerebral artery segmentation in single-exposure CT angiography (CTA) using a projection-domain framework derived from patient-based CTA data. METHODS: The proposed method employs the DeepLab V3+ model to segment brain vessels directly in the projection. A total of 103 patients were included in the dataset, and 61, 17, and 25 patients were allocated to the training, validation, and test sets, respectively. This approach eliminates the risk of double exposure and motion artifacts while preserving clinical information. Additionally, this approach minimizes beam-hardening artifacts from high-density materials and reduces the operator's workload. RESULTS: The cerebral artery images reconstructed using the proposed method were quantitatively compared to those of the labeled images, and the intersection over union, Dice similarity coefficient, bidirectional Hausdorff distance, bfscore, F1-score, and precision were measured to be approximately 0.89 [95% CI, 0.87-0.91], 0.90 [0.88-0.91], 219.12 pixels [201.54-236.70], 0.90 [0.89-0.92], 0.90 [0.89-0.92], and 0.89 [0.88-0.91], respectively. Performance metrics consistently demonstrated high agreement between reconstructed vessel maps and reference labels. In addition, the proposed method confirmed that reconstructing cerebral arteries and metallic implant components may yield clinically relevant vascular image information with limited information loss. CONCLUSIONS: These results support the feasibility of the proposed method for generating cerebral arterial 3D images in CTA systems and suggest potential utility for improved vascular visualization and image quality, pending further clinical validation.