Here we proposed a designed, built, and evaluated 20G vertically inserted razor edge cannula (VIREC) robotic device guided by optical coherence tomography (OCT) for pneumatic dissection. The fiber sensor was glued inside the needle at a fixed offset of ~500 um. During the experiment, the robotic needle driver precisely moves the VIREC based on the surgeon input which is carefully monitored by the M-mode OCT system. Once the needle is inserted into the desired depth, the air is injected by the surgeon to separate stroma from Descemet’s membrane (DM). During in vivo study (N=8), the “big bubble” was effectively generated in six of eight eyes tested and DM was perforated in two eyes. This demonstrated the reliability and effectiveness of VIREC for “big bubble” DALK.
Real-time fringe projection profilometry (FPP) is developed as a 3D vision system to plan and guide autonomous robotic intestinal suturing. Conventional FPP requires sinusoidal patterns with multiple frequencies, and phase shifts to generate tissue point clouds, resulting in a slow frame rate. Therefore, although FPP can reconstruct dense and accurate tissue point clouds, it is often too slow for dynamic measurements. To address this problem, we propose a deep learning-based single-shot FPP algorithm, which reconstructs tissue point clouds with a single sinusoidal pattern using a Swin-Unet. With this approach, we have achieved a FPP imaging frame rate of 50Hz while maintaining high point cloud measurement accuracy. System performance was trained and evaluated both by synthesized and an experimental dataset. An overall relative error of 1~3% was achieved.
We reported a design and evaluation of an optical coherence tomography (OCT) sensor-integrated 27 gauge vertically inserted razor edge cannula (VIREC) for pneumatic dissection of Descemet’s membrane (DM) from the stromal layer. The VIREC was inserted vertically at the apex of the cornea to the desired depth near DM. The study was performed using ex vivo bovine corneas (N = 5) and rabbit corneas (N = 5). A clean penumodissection of a stromal layer was successfully performed using VIREC without any stomal blanching on bovine eyes. The “big bubble” was generated in all five tests without perforation. Only micro bubbles were observed on rabbit eyes. The results proved that VIREC can be an effective surgical option for “big bubble” DALK.
Fringe projection profilometry (FPP) is being developed as a 3D vision system to assist robotic surgery and autonomous suturing. Conventionally, fluorescence markers are placed on a target tissue to indicate suturing landmarks, which not only increase the system complexity, but also impose safety concerns. To address these problems, we propose a numerical landmark detection algorithm based on deep learning. A landmark heatmap is regressed using an adopted U-Net from the four channel data generated by the FPP. A Markov random field leveraging the structure prior is developed to search the correct set of landmarks from the heatmap. The accuracy of the proposed method is verified through ex-vivo porcine intestine landmark detection experiments.
We developed a fully automated abdominal tissue classification algorithm for swept-source OCT imaging using a hybrid multilayer perceptron (MLP) and convolutional neural network (CNN) classifier. For MLP, we incorporated an extensive set of features and a subset was chosen to improve network efficiency. For CNN, we designed a threechannel model combining the intensity information with depth-dependent optical properties of tissues. A rule-based decision fusion approach was applied to find more convincing predictions between these two portions. Our model was trained using ex vivo porcine samples, (~200 B-mode images, ~200,000 A-line signals), evaluated by a hold-out dataset. Compared to other algorithms, our classifiers achieve the highest accuracy of 0.9114 and precision of 0.9106. The promising results showed its feasibility for real-time abdominal tissue sensing during robotic-assisted laparoscopic OCT surgery.
Deep anterior lamellar keratoplasty (DALK) is a partial-thickness cornea transplant procedure in which only the recipient’s stroma is replaced, leaving the host’s Descemet’s membrane (DM) and endothelium intact. This highly challenging “Big Bubble” procedure requires micron accuracy to insert a hydro-dissection needle as close as possible to the DM. Here, we report the design and evaluation of a downward viewing common-path optical coherence tomography (OCT) guided hydro-dissection needle for DALK. This design offers the flexibility of using different insertion angles and needle sizes. With the fiber situated outside the needle and eye, the needle can use its’ full lumen for a smoother air/fluid injection and image quality is improved. The common-path OCT probe uses a bare optical fiber with its tip cleaved at the right angle for both reference and sample arm which is encapsulated in a 25-gauge stainless still tube. The fiber was set up vertically with a half-ball epoxy lens at the end to provide an A-scan with an 11-degree downward field of view. The hydro dissection needle was set up at 70 degrees from vertical and the relative position between the fiber end and the needle tip remained constant during the insertion. The fiber and needle were aligned by a customized needle driver to allow the needle tip and tissue underneath to both be imaged within the same A-scan. Fresh porcine eyes (N = 5) were used for the studies. The needle tip position, the stroma, and DM were successfully identified from the A-scan during the whole insertion process. The results showed the downward viewing OCT distal sensor can accurately guide the needle insertion for DALK and improved the average insertion depth compared to freehand insertion.
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