Feasibility of robotic-assisted optical coherence tomography with extended scanning area for pre-transplant kidney monitoring

Ma, Xihan; Moradi, Mousa; Mustafa, Haithem A. B.; Hunter, Martin; Chen, Yu; Zhang, Haichong K.

doi:10.1117/12.2612677

Cited by 8 publications

(8 citation statements)

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“…Our R-OCT system 40 consists of a seven DoF robotic arm (Panda, Franka Emika, Germany) and a customized end-effector which houses a compact OCT probe (OCTG13 Telesto, Thorlabs, USA). The OCT system was configured to provide a lateral resolution of 2.73 m per pixel and axial resolution of 2.68 m per pixel, respectively.…”

Section: R-oct For Large Area Imagingmentioning

confidence: 99%

Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Zhang,

Ma,

Moradi

et al. 2023

Preprint

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Optical coherence tomography (OCT) is a high-resolution imaging modality that can be used to image microstructures of human kidneys. These images can be analyzed to evaluate the viability of the organ for transplantation. However, current OCT devices suffer from insufficient field-of-view, leading to biased examination outcomes when only small portions of the kidney can be assessed. Here we present a robotic OCT system where an OCT probe is integrated with a robotic manipulator, enabling wider area spatially-resolved imaging. With the proposed system, it becomes possible to comprehensively scan the kidney surface and provide large area parameterization of the microstructures. We verified the probe tracking accuracy with a phantom as 0.0762 ± 0.0727 mm and demonstrated its clinical feasibility by scanning ex vivo kidneys. The parametric map exhibits fine vasculatures beneath the kidney surface. Quantitative analysis on the proximal convoluted tubule from the ex vivo human kidney yields highly clinical-relevant information.

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Section: R-oct For Large Area Imagingmentioning

confidence: 99%

Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Zhang,

Ma,

Moradi

et al. 2023

Preprint

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“…Huang et al presented an R-OCT system that could generate a large FOV with a 7 degree-of-freedom (DoF) robotic arm [ 14 ]. Ma et al demonstrated another R-OCT system with extended FOV and high resolution that enable OCT imaging especially for kidney pre-transplant evaluation [ 13 , 15 ]. However, the above studies utilize B-mode or C-mode OCT imaging system that requires a large scanning module, making it hard for accessing internal organs directly in minimally invasive surgery.…”

Section: Introductionmentioning

confidence: 99%

Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography

Wang,

Wei,

Zuo

et al. 2024

Biomed. Opt. Express

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Anastomosis is a common and critical part of reconstructive procedures within gastrointestinal, urologic, and gynecologic surgery. The use of autonomous surgical robots such as the smart tissue autonomous robot (STAR) system demonstrates an improved efficiency and consistency of the laparoscopic small bowel anastomosis over the current da Vinci surgical system. However, the STAR workflow requires auxiliary manual monitoring during the suturing procedure to avoid missed or wrong stitches. To eliminate this monitoring task from the operators, we integrated an optical coherence tomography (OCT) fiber sensor with the suture tool and developed an automatic tissue classification algorithm for detecting missed or wrong stitches in real time. The classification results were updated and sent to the control loop of STAR robot in real time. The suture tool was guided to approach the object by a dual-camera system. If the tissue inside the tool jaw was inconsistent with the desired suture pattern, a warning message would be generated. The proposed hybrid multilayer perceptron dual-channel convolutional neural network (MLP-DC-CNN) classification platform can automatically classify eight different abdominal tissue types that require different suture strategies for anastomosis. In MLP, numerous handcrafted features (∼1955) were utilized including optical properties and morphological features of one-dimensional (1D) OCT A-line signals. In DC-CNN, intensity-based features and depth-resolved tissues’ attenuation coefficients were fully exploited. A decision fusion technique was applied to leverage the information collected from both classifiers to further increase the accuracy. The algorithm was evaluated on 69,773 testing A-line data. The results showed that our model can classify the 1D OCT signals of small bowels in real time with an accuracy of 90.06%, a precision of 88.34%, and a sensitivity of 87.29%, respectively. The refresh rate of the displayed A-line signals was set as 300 Hz, the maximum sensing depth of the fiber was 3.6 mm, and the running time of the image processing algorithm was ∼1.56 s for 1,024 A-lines. The proposed fully automated tissue sensing model outperformed the single classifier of CNN, MLP, or SVM with optimized architectures, showing the complementarity of different feature sets and network architectures in classifying intestinal OCT A-line signals. It can potentially reduce the manual involvement of robotic laparoscopic surgery, which is a crucial step towards a fully autonomous STAR system.

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“…Even though our proposed application seems less complicated and requires fewer safety regulations than ophthalmic imaging, skin imaging is especially challenging because large 3D datasets with file sizes beyond 1 TB and various shapes need to be acquired. In contrast to previous approaches for automatic large area scanning [4][5][6][7], we developed an intrinsic, online probe-to-surface control for probe positioning and orientation while OCT imaging at real-time update rates. It features a combined surfacedistance and surface-orientation closed-loop control algorithm, which enables automatic positioning of the probe solely based on the spatial surface information extracted from the acquired OCT image.…”

Section: Introductionmentioning

confidence: 99%

Large area robotically assisted optical coherence tomography (LARA-OCT) for skin imaging with MHz-OCT surface tracking

Göb¹,

Lotz²,

Ha-Wissel³

et al. 2023

Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII

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Optical coherence tomography (OCT) is a powerful imaging technique to non-invasively differentiate between healthy skin and pathological conditions. Unfortunately, commercially available OCT-systems are typically slow and not capable of scanning large areas at reasonable speed. Since skin lesions may extend over several square centimeters, potential inflammatory infiltrates remain undetected. Here, we present large area robotically assisted OCT (LARA-OCT) for skin imaging. Therefor a collaborative robot is combined with an existing, home-built 3.3 MHz-OCT-system and for surface tracking an online probe-to-surface control is implemented which is solely based on the OCT surface signal. It features a combined surface-distance and surface-orientation closed-loop control algorithm, which enables automatic positioning and alignment of the probe across the target while imaging. This allows to acquire coherent OCT images of skin areas beyond 10 cm².

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Feasibility of robotic-assisted optical coherence tomography with extended scanning area for pre-transplant kidney monitoring

Cited by 8 publications

References 0 publications

Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography

Large area robotically assisted optical coherence tomography (LARA-OCT) for skin imaging with MHz-OCT surface tracking

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