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1. Polarization-sensitive optical coherence tomography for renal tumor detection in ex vivo human kidneys

   https://doi.org/10.1016/j.optlaseng.2023.107900  

   Yan, Feng; Wang, Chen; Yan, Yuyang; Zhang, Qinghao; Yu, Zhongxin; Patel, Sanjay G.; Fung, Kar-Ming; Tang, Qinggong (February 2024, Optics and Lasers in Engineering)

   Kidney cancer is a kind of high mortality cancer because of the difficulty in early diagnosis and the high metastatic dissemination in treatments. The surgical resection of tumors is the most effective treatment for renal cancer patients. However, precise assessment of tumor margins is a challenge during surgical resection. The objective of this study is to demonstrate an optical imaging tool in precisely distinguishing kidney tumor borders and identifying tumor zones from normal tissues to assist surgeons in accurately resecting tumors from kidneys during the surgery. 30 samples from six human kidneys were imaged using polarization-sensitive optical coherence tomography (PS-OCT). Cross-sectional, enface, and spatial information of kidney samples were obtained for microenvironment reconstruction. Polarization parameters (phase retardation, optic axis direction, and degree of polarization uniformity (DOPU) and Stokes parameters (Q, U, and V) were utilized for multiparameter analysis. To verify the detection accuracy of PS-OCT, H&E histology staining and dice-coefficient were utilized to quantify the performance of PS-OCT in identifying tumor borders and regions. In this study, tumor borders were clearly identified by PS-OCT imaging, which outperformed the conventional intensity-based OCT. With H&E histological staining as golden standard, PS-OCT precisely identified the tumor regions and tissue distributions at different locations and different depths based on polarization and Stokes parameters. Compared to the traditional attenuation coefficient quantification method, PS-OCT demonstrated enhanced contrast of tissue characteristics between normal and cancerous tissues due to the birefringence effects. Our results demonstrated that PS-OCT was promising to provide imaging guidance for the surgical resection of kidney tumors and had the potential to be used for other human kidney surgeries in clinics such as renal biopsy. 

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2. Enhancing epidural needle guidance using a polarization‐sensitive optical coherence tomography probe with convolutional neural networks

   https://doi.org/10.1002/jbio.202300330  

   Wang, Chen; Liu, Yunlong; Calle, Paul; Li, Xinwei; Liu, Ronghao; Zhang, Qinghao; Yan, Feng; Fung, Kar‐ming; Conner, Andrew K.; Chen, Sixia; et al (October 2023, Journal of Biophotonics)

   Abstract Epidural anesthesia helps manage pain during different surgeries. Nonetheless, the precise placement of the epidural needle remains a challenge. In this study, we developed a probe based on polarization‐sensitive optical coherence tomography (PS‐OCT) to enhance the epidural anesthesia needle placement. The probe was tested on six porcine spinal samples. The multimodal imaging guidance used the OCT intensity mode and three distinct PS‐OCT modes: (1) phase retardation, (2) optic axis, and (3) degree of polarization uniformity (DOPU). Each mode enabled the classification of different epidural tissues through distinct imaging characteristics. To further streamline the tissue recognition procedure, convolutional neural network (CNN) were used to autonomously identify the tissue types within the probe's field of view. ResNet50 models were developed for all four imaging modes. DOPU imaging was found to provide the highest cross‐testing accuracy of 91.53%. These results showed the improved precision by PS‐OCT in guiding epidural anesthesia needle placement. 

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3. Collaborative Suturing: A Reinforcement Learning Approach to Automate Hand-off Task in Suturing for Surgical Robots

   https://doi.org/10.1109/RO-MAN47096.2020.9223543  

   Varier, Vignesh Manoj; Rajamani, Dhruv Kool; Goldfarb, Nathaniel; Tavakkolmoghaddam, Farid; Munawar, Adnan; Fischer, Gregory S (August 2020, 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN))

   null (Ed.)

   Over the past decade, Robot-Assisted Surgeries (RAS), have become more prevalent in facilitating successful operations. Of the various types of RAS, the domain of collaborative surgery has gained traction in medical research. Prominent examples include providing haptic feedback to sense tissue consistency, and automating sub-tasks during surgery such as cutting or needle hand-off - pulling and reorienting the needle after insertion during suturing. By fragmenting suturing into automated and manual tasks the surgeon could essentially control the process with one hand and also circumvent workspace restrictions imposed by the control interface present at the surgeon's side during the operation. This paper presents an exploration of a discrete reinforcement learning-based approach to automate the needle hand-off task. Users were asked to perform a simple running suture using the da Vinci Research Kit. The user trajectory was learnt by generating a sparse reward function and deriving an optimal policy using Q-learning. Trajectories obtained from three learnt policies were compared to the user defined trajectory. The results showed a root-mean-square error of [0.0044mm, 0.0027mm, 0.0020mm] in ℝ 3 . Additional trajectories from varying initial positions were produced from a single policy to simulate repeated passes of the hand-off task. 

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4. Deep learning-based transformation of H&E stained tissues into special stains

   https://doi.org/10.1038/s41467-021-25221-2  

   de Haan, Kevin; Zhang, Yijie; Zuckerman, Jonathan E.; Liu, Tairan; Sisk, Anthony E.; Diaz, Miguel F. P.; Jen, Kuang-Yu; Nobori, Alexander; Liou, Sofia; Zhang, Sarah; et al (August 2021, Nature Communications)

   Abstract Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson’s Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost. 

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5. An Analytical Model for Predicting the Deflection of Hollow Surgical Needle in Soft Tissue

   https://doi.org/10.1115/IMECE2021-71532  

   Al-Safadi, Samer; Hutapea, Parsaoran (November 2021, Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition)

   Abstract Considerable research efforts have been devoted for studying the interaction between surgical needles and soft tissues which can be used to evaluate the deflection of a bevel-tip needle inside a tissue. The development of an analytical model to predict the steering behavior of the needle during needle-tissue interactions could improve the performance of many percutaneous needle-based procedures. In this study, Euler-Bernoulli beam elastic foundation theory was utilized to model the needle as a cantilever beam moving along its longitudinal axis and undergoing various external loads. The external loads are the result of the interaction between the tissue and the needle during insertion, they can be modeled as a concentrated tissue cutting force acting at the needle bevel, and needle-tissue interaction forces acting along the needle length and tangent to the needle shaft. The accuracy of the analytical predictions offered by the model are verified by comparing them to the experimental data. Due to the assumption of the elastic tissue material, the difference between the analytical model and the experimental results was between 15% to 33%. Current work is ongoing to consider tissue viscoelastic properties to improve the analytical prediction. 

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