Intraoperative laparoscopic photoacoustic image guidance system in the da Vinci surgical system

Cardiac-gated spectroscopic photoacoustic imaging for ablation-induced necrotic lesion visualization

Radiofrequency (RF) ablation is a minimally invasive therapy for atrial fibrillation. Conventional RF procedures lack intraoperative monitoring of ablation-induced necrosis, complicating assessment of completeness. While spectroscopic photoacoustic (sPA) imaging shows promise in distinguishing ablated tissue, multi-spectral imaging is challenging in vivo due to low imaging quality caused by motion. Here, we introduce a cardiac-gated sPA imaging (CG-sPA) framework to enhance image quality using a motion-gated averaging filter, relying on image similarity. Necrotic extent was calculated based on the ratio between spectral unmixed ablated tissue contrast and total tissue contrast, visualizing as a continuous color map to highlight necrotic area. The validation of the concept was conducted in both ex vivo and in vivo swine models. The ablation-induced necrotic lesion was successfully detected throughout the cardiac cycle through CG-sPA imaging. The results suggest the CG-sPA imaging framework has great potential to be incorporated into clinical workflow to guide ablation procedures intraoperatively.

Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth

Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.