SignificanceLaparoscopic surgery presents challenges in localizing oncological margins due to poor contrast between healthy and malignant tissues. Optical properties can uniquely identify various tissue types and disease states with high sensitivity and specificity, making it a promising tool for surgical guidance. Although spatial frequency domain imaging (SFDI) effectively measures quantitative optical properties, its deployment in laparoscopy is challenging due to the constrained imaging environment. Thus, there is a need for compact structured illumination techniques to enable accurate, quantitative endogenous contrast in minimally invasive surgery.AimWe introduce a compact, two-camera laparoscope that incorporates both active stereo depth estimation and speckle-illumination SFDI (si-SFDI) to map profile-corrected, pixel-level absorption (μa), and reduced scattering (μs′) optical properties in images of tissues with complex geometries.ApproachWe used a multimode fiber-coupled 639-nm laser illumination to generate high-contrast speckle patterns on the object. These patterns were imaged through a modified commercial stereo laparoscope for optical property estimation via si-SFDI. Compared with the original si-SFDI work, which required ≥10 images of randomized speckle patterns for accurate optical property estimations, our approach approximates the DC response using a laser speckle reducer (LSR) and consequently requires only two images. In addition, we demonstrate 3D profilometry using active stereo from low-coherence RGB laser flood illumination. Sample topography was then used to correct for measured intensity variations caused by object height and surface angle differences with respect to a calibration phantom. The low-contrast RGB speckle pattern was blurred using an LSR to approximate incoherent white light illumination. We validated profile-corrected si-SFDI against conventional SFDI in phantoms with simple and complex geometries, as well as in a human finger in vivo time-series constriction study.ResultsLaparoscopic si-SFDI optical property measurements agreed with conventional SFDI measurements when measuring flat tissue phantoms, exhibiting an error of 6.4% for absorption and 5.8% for reduced scattering. Profile-correction improved the accuracy for measurements of phantoms with complex geometries, particularly for absorption, where it reduced the error by 23.7%. An in vivo finger constriction study further validated laparoscopic si-SFDI, demonstrating an error of 8.2% for absorption and 5.8% for reduced scattering compared with conventional SFDI. Moreover, the observed trends in optical properties due to physiological changes were consistent with previous studies.ConclusionsOur stereo-laparoscopic implementation of si-SFDI provides a simple method to obtain accurate optical property maps through a laparoscope for flat and complex geometries. This has the potential to provide quantitative endogenous contrast for minimally invasive surgical guidance.
Purpose. Finding desired scan planes in ultrasound (US) imaging is a critical first task that can be time-consuming, influenced by operator experience, and subject to inter-operator variability. To circumvent these problems, interventional US imaging often necessitates dedicated, experienced sonographers in the operating room. This work presents a new approach leveraging deep reinforcement learning (RL) to assist probe positioning. Methods. A deep Q-network (DQN) is applied and evaluated for renal imaging and is tasked with locating the dorsal US scan plane. To circumvent the need for large labeled datasets, images were resliced from a large dataset of CT volumes and synthesized to US images using Field II, CycleGAN, and U-GAT-IT. The algorithm was evaluated on both synthesized and real US images, and its performance was quantified in terms of the agent’s accuracy in reaching the target scan plane. Results. Learning-based synthesis methods performed better than the physics-based approach, achieving comparable image quality when qualitatively compared to real US images. The RL agent was successful in reaching target scan planes when adjusting the probe’s rotation, with the U-GAT-IT model demonstrating superior generalizability (80.3% reachability) compared to CycleGAN (54.8% reachability). Conclusions. The approach presents a novel RL training strategy using image synthesis for automated US probe positioning. Ongoing efforts aim to evaluate advanced DQN models, image-based reward functions, and support probe motion with higher degrees of freedom.
With the rise in minimally invasive surgery and machine learning, there are emerging opportunities to improve patient outcomes with endoscopic techniques that quantify tissue shape and optical properties. We introduce a speckle-illumination stereo endoscope (SSE) that utilizes structured illumination to enhance both depth and optical property mapping. An SSE prototype was constructed and applied to fresh pig colon samples. SSE-estimated depth and optical property maps compare favorably to gold standard techniques. Requiring only minor modifications to existing commercial stereoscopes, the SSE could provide surgeons with improved visual depth perception and maps of biomarkers in vivo.
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