Deep Learning Realizes Photoacoustic Imaging Artifact Removal

Abstract

Photoacoustic imaging integrates the strengths of optics and ultrasound, offering high resolution, depth penetration, and multimodal imaging capabilities. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their “view” of the imaging target, which result in image reconstruction artifacts degrading image quality. To address this problem, YOLOv8-Pix2Pix is proposed as a hybrid artifact-removal algorithm, which is advantageous in comprehensively eliminating various types of artifacts and effectively restoring image details compared to existing algorithms. The proposed algorithm demonstrates superior performance in artifact removal and segmentation of photoacoustic images of brain tumors. For the purpose of further expanding its application fields and aligning with actual clinical needs, an experimental system for photoacoustic detection is designed in this paper to be verified. The experimental results show that the processed images are better than the pre-processed images in terms of reconstruction metrics PSNR and SSIM, and also the segmentation performance is significantly improved, which provides an effective solution for the further development of photoacoustic imaging technology.

Keywords

• photoacoustic imaging
• artifact reduction
• Pix2Pix
• YOLOv8