The content discusses the challenges in 2D/3D image registration for aortic DSA and CTA, proposing an unsupervised method called UDCR using deep reinforcement learning. The method leverages overlap degree calculation to assess accuracy and achieved promising results with a Mean Absolute Error of 2.85 mm in translation and 4.35° in rotation. Experimental comparisons between different reinforcement learning algorithms and configurations were conducted to evaluate performance.
The paper highlights the importance of accurate spatial fusion between preoperative CTA 3D images and real-time intra-interventional DSA images for precise interventional surgical treatment of vascular diseases. Traditional methods face limitations due to divergent imaging principles of DSA and CTA, leading to suboptimal results. The proposed UDCR method overcomes these limitations by utilizing deep reinforcement learning without the need for manual annotations or synthetic data.
By introducing a novel reward function based on overlap degree calculation, the UDCR method quantitatively evaluates registration accuracy between segmentation maps and DSA images. The flexibility of the approach allows for pre-training with satisfactory results and online learning for continuous improvement. The study emphasizes the potential of UDCR in enhancing clinical applications through accurate image registration.
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by Wentao Liu,B... at arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.05753.pdfDeeper Inquiries