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1. Imaging of subendocardial adipose tissue and fiber orientation distributions in the human left atrium using optical coherence tomography

   https://doi.org/10.1111/jce.14255  

   Lye, Theresa H. ; Marboe, Charles C. ; Hendon, Christine P. ( November 2019 , Journal of Cardiovascular Electrophysiology)

   Optical coherence tomography (OCT) has the potential to provide real‐time imaging guidance for atrial fibrillation ablation, with promising results for lesion monitoring. OCT can also offer high‐resolution imaging of tissue composition, but there is insufficient cardiac OCT data to inform the use of OCT to reveal important tissue architecture of the human left atrium. Thus, the objective of this study was to define OCT imaging data throughout the human left atrium, focusing on the distribution of adipose tissue and fiber orientation as seen from the endocardium.

   Human hearts (n = 7) were acquired for imaging the left atrium with OCT. A spectral‐domain OCT system with 1325 nm center wavelength, 6.5 μm axial resolution, 15 μm lateral resolution, and a maximum imaging depth of 2.51 mm in the air was used. Large‐scale OCT image maps of human left atrial tissue were developed, with adipose thickness and fiber orientation extracted from the imaging data. OCT imaging showed scattered distributions of adipose tissue around the septal and pulmonary vein regions, up to a depth of about 0.43 mm from the endocardial surface. The total volume of adipose tissue detected by OCT over one left atrium ranged from 1.42 to 28.74 mm³. Limited fiber orientation information primarily around the pulmonary veins and the septum could be identified.

   OCT imaging could provide adjunctive information on the distribution of subendocardial adipose tissue, particularly around thin areas around the pulmonary veins and septal regions. Variations in OCT‐detected tissue composition could potentially assist ablation guidance.

    

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2. Intra‐operative imaging of surgical margins of canine soft tissue sarcoma using optical coherence tomography

   https://doi.org/10.1111/vco.12448  

   Selmic, Laura E. ; Samuelson, Jonathan ; Reagan, Jennifer K. ; Mesa, Kelly J. ; Driskell, Elizabeth ; Li, Joanne ; Marjanovic, Marina ; Boppart, Stephen A. ( November 2018 , Veterinary and Comparative Oncology)

   Optical coherence tomography (OCT) is a rapid non‐invasive imaging technique that has shown high sensitivity for intra‐operative surgical margin assessment in human breast cancer clinical trials. This promising technology has not been evaluated in veterinary medicine. The objective of this study was to correlate normal and abnormal histological features with OCT images for surgical margins from excised canine soft tissue sarcoma (STS) and to establish image evaluation criteria for identifying positive surgical margins. Fourteen client‐owned dogs underwent surgical resection of a STS and OCT imaging of 2 to 4 areas of interest on the resected specimen were performed. Following imaging these areas were marked with surgical ink and trimmed for histopathology evaluation. Results showed that different tissue types had distinct characteristic appearances on OCT imaging. Adipose tissue exhibited a relatively low scattering and a honey‐comb texture pattern. Skeletal muscle and sarcoma tissue were both dense and highly scattering. While sarcoma tissue was highly scattering, it did not have organized recognizable structure in contrast to muscle which showed clear fibre alignment patterns. In this investigation, we showed different tissue types had different and characteristic scattering and image texture appearances on OCT, which closely correlate with low‐power histology images. Given the differentiation between tissue types the results support that OCT could be used to identify positive surgical margins immediately following resection of STS. Further research is needed to assess the diagnostic accuracy of this method for surgical margin assessment.

    

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3. 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)

   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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4. Cardiac endocardial left atrial substrate and lesion depth mapping using near-infrared spectroscopy

   https://doi.org/10.1364/BOE.451547  

   Park, Soo Young ; Yang, Haiqiu ; Marboe, Charles ; Ziv, Ohad ; Laurita, Kenneth ; Rollins, Andrew ; Saluja, Deepak ; Hendon, Christine P. ( March 2022 , Biomedical Optics Express)

   Atrial fibrillation (AF) is a rapid irregular electrical activity in the upper chamber and the most common sustained cardiac arrhythmia. Many patients require radiofrequency ablation (RFA) therapy to restore sinus rhythm. Pulmonary vein isolation requires distinguishing normal atrial wall from the pulmonary vein tissue, and atrial substrate ablation requires differentiating scar tissue, fibrosis, and adipose tissue. However, current anatomical mapping methods for strategically locating ablation sites by identifying structural substrates in real-time are limited. An intraoperative tool that accurately provides detailed structural information and classifies endocardial substrates could help improve RF guidance during RF ablation therapy. In this work, we propose a 7F NIRS integrated ablation catheter and demonstrate endocardial mapping onex vivoswine (n = 12) and human (n = 5) left atrium (LA). First, pulmonary vein (PV) sleeve, fibrosis and ablation lesions were identified with NIRS-derived contrast indices. Based on these key spectral features, classification algorithms identified endocardial substrates with high accuracy (<11% error). Then, a predictive model for lesion depth was evaluated on classified lesions. Model predictions correlated well with histological measurements of lesion dimensions (R = 0.984). Classified endocardial substrates and lesion depth were represented in 2D spatial maps. These results suggest NIRS integrated mapping catheters can serve as a complementary tool to the current electroanatomical mapping system to improve treatment efficacy.

    

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5. Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

   https://doi.org/10.1002/jbio.201960135  

   Dong, Zhao ; Liu, Guoyan ; Ni, Guangming ; Jerwick, Jason ; Duan, Lian ; Zhou, Chao ( February 2020 , Journal of Biophotonics)

   Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to denoise OCT images. A speckle‐modulating OCT (SM‐OCT) was built to generate low speckle images to be used as the ground truth. In total, 210 000 SM‐OCT images were used for training and validating the neural network model, which we call SM‐GAN. The performance of the SM‐GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM‐GAN model was compared to traditional OCT denoising methods and other state‐of‐the‐art deep learning based denoise networks. We conclude that the SM‐GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions.

    

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