Automated prediction of isthmus areas in scar-related atrial tachycardias using artificial intelligence.

INTRODUCTION: Ablation of scar-related reentrant atrial tachycardia (SRRAT) involves identification and ablation of a critical isthmus. A graph convolutional network (GCN) is a machine learning structure that is well-suited to analyze the irregularly-structured data obtained in mapping procedures and may be used to identify potential isthmuses. METHODS: Electroanatomic maps from 29 SRRATs were collected, and custom electrogram features assessing key tissue and wavefront properties were calculated for each point. Isthmuses were labeled off-line. Training data was used to determine the optimal GCN parameters and train the final model. Putative isthmus points were predicted in the training and test populations and grouped into proposed isthmus areas based on density and distance thresholds. The primary outcome was the distance between the centroids of the true and closest proposed isthmus areas. RESULTS: A total of 193 821 points were collected. Thirty isthmuses were detected in 29 tachycardias among 25 patients (median age 65.0, 5 women). The median (IQR) distance between true and the closest proposed isthmus area centroids was 8.2 (3.5, 14.4) mm in the training and 7.3 (2.8, 16.1) mm in the test group. The mean overlap in areas, measured by the Dice coefficient, was 11.5 ± 3.2% in the training group and 13.9 ± 4.6% in the test group. CONCLUSION: A GCN can be trained to identify isthmus areas in SRRATs and may help identify critical ablation targets.

Equilibrium Tension and Compression Mechanical Properties of the Human Uterus.

A successful pregnancy relies on the proper cellular, biochemical, and mechanical functions of the uterus. A comprehensive understanding of uterine mechanical properties during pregnancy is key to understanding different gynecological and obstetric disorders such as preterm birth, placenta accreta, leiomyoma, and endometriosis. This study sought to characterize the macro-scale equilibrium material behaviors of the human uterus in non-pregnancy and late pregnancy under both compressive and tensile loading. Fifty human uterine specimens from 16 patients (8 nonpregnant [NP] and 8 pregnant [PG]) were tested using spherical indentation and uniaxial tension coupled with digital image correlation (DIC). A three-level incremental load-hold protocol was applied to both tests. A microstructurally-inspired material model considering fiber architecture was applied to this dataset. Inverse finite element analysis (IFEA) was then performed to generate a single set of mechanical parameters to describe compressive and tensile behaviors. The freeze-thaw effect on uterine macro mechanical properties was also evaluated. PG tissue exhibits decreased overall stiffness and increased fiber network extensibility compared to NP uterine tissue. Under indentation, ground substance compressibility was similar between NP and PG uterine tissue. In tension, the fiber network of the PG uterus was found to be more extensible and dispersed than in nonpregnancy. Lastly, a single freeze-thaw cycle did not systematically alter the macro-scale material behavior of the human uterus.

Robust, high-density lesion mapping in the left atrium with near-infrared spectroscopy.

Significance: Radiofrequency ablation (RFA) procedures for atrial fibrillation frequently fail to prevent recurrence, partially due to limitations in assessing extent of ablation. Optical spectroscopy shows promise in assessing RFA lesion formation but has not been validated in conditions resembling those in vivo. Aim: Catheter-based near-infrared spectroscopy (NIRS) was applied to porcine hearts to demonstrate that spectrally derived optical indices remain accurate in blood and at oblique incidence angles. Approach: Porcine left atria were ablated and mapped using a custom-fabricated NIRS catheter. Each atrium was mapped first in phosphate-buffered saline (PBS) then in porcine blood. Results: NIRS measurements showed little angle dependence up to 60 deg. A trained random forest model predicted lesions with a sensitivity of 81.7%, a specificity of 86.1%, and a receiver operating characteristic curve area of 0.921. Predicted lesion maps achieved a mean structural similarity index of 0.749 and a mean normalized inner product of 0.867 when comparing maps obtained in PBS and blood. Conclusions: Catheter-based NIRS can precisely detect RFA lesions on left atria submerged in blood. Optical parameters are reliable in blood and without perpendicular contact, confirming their ability to provide useful feedback during in vivo RFA procedures.

Compressed sensing on displacement signals measured with optical coherence tomography.

Optical coherence tomography (OCT) is capable of angstrom-scale vibrometry of particular interest to researchers of auditory mechanics. We develop a method for compressed sensing vibrometry using OCT that significantly reduces acquisition time for dense motion maps. Our method, based on total generalized variation with uniform subsampling, can reduce the number of samples needed to measure motion maps by a factor of ten with less than 5% normalized mean square error when tested on a diverse set of in vivo measurements from the gerbil cochlea. This opens up the possibility for more complex in vivo experiments for cochlear mechanics.

Compressed sensing of human breast optical coherence 3-D image volume data using predictive coding.

There are clinical needs for optical coherence tomography (OCT) of large areas within a short period of time, such as imaging resected breast tissue for the evaluation of cancer. We report on the use of denoising predictive coding (DN-PC), a novel compressed sensing (CS) algorithm for reconstruction of OCT volumes of human normal breast and breast cancer tissue. The DN-PC algorithm has been rewritten to allow for computational parallelization and efficient memory transfer, resulting in a net reduction of computation time by a factor of 20. We compress image volumes at decreasing A-line sampling rates to evaluate a relation between reconstruction behavior and image features of breast tissue.

30 Years of Optical Coherence Tomography: introduction to the feature issue.

The guest editors introduce a feature issue commemorating the 30th anniversary of Optical Coherence Tomography.

Cardiac Adipose Tissue Segmentation via Image-Level Annotations.

Automatically identifying the structural substrates underlying cardiac abnormalities can potentially provide real-time guidance for interventional procedures. With the knowledge of cardiac tissue substrates, the treatment of complex arrhythmias such as atrial fibrillation and ventricular tachycardia can be further optimized by detecting arrhythmia substrates to target for treatment (i.e., adipose) and identifying critical structures to avoid. Optical coherence tomography (OCT) is a real-time imaging modality that aids in addressing this need. Existing approaches for cardiac image analysis mainly rely on fully supervised learning techniques, which suffer from the drawback of workload on labor-intensive annotation process of pixel-wise labeling. To lessen the need for pixel-wise labeling, we develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates. In particular, we integrate class activation mapping with superpixel segmentation to solve the sparse tissue seed challenge raised in cardiac tissue segmentation. Our study bridges the gap between the demand on automatic tissue analysis and the lack of high-quality pixel-wise annotations. To the best of our knowledge, this is the first study that attempts to address cardiac tissue segmentation on OCT images via weakly supervised learning techniques. Within an in-vitro human cardiac OCT dataset, we demonstrate that our weakly supervised approach on image-level annotations achieves comparable performance as fully supervised methods trained on pixel-wise annotations.

Automated analysis framework for in vivo cardiac ablation therapy monitoring with optical coherence tomography.

Radiofrequency ablation (RFA) is a minimally invasive procedure that is commonly used for the treatment of atrial fibrillation. However, it is associated with a significant risk of arrhythmia recurrence and complications owing to the lack of direct visualization of cardiac substrates and real-time feedback on ablation lesion transmurality. Within this manuscript, we present an automated deep learning framework for in vivo intracardiac optical coherence tomography (OCT) analysis of swine left atria. Our model can accurately identify cardiac substrates, monitor catheter-tissue contact stability, and assess lesion transmurality on both OCT intensity and polarization-sensitive OCT data. To the best of our knowledge, we have developed the first automatic framework for in vivo cardiac OCT analysis, which holds promise for real-time monitoring and guidance of cardiac RFA therapy..

Classifying breast cancer in ultrahigh-resolution optical coherence tomography images using convolutional neural networks.

Optical coherence tomography (OCT) is being investigated in breast cancer diagnostics as a real-time histology evaluation tool. We present a customized deep convolutional neural network (CNN) for classification of breast tissues in OCT B-scans. Images of human breast samples from mastectomies and breast reductions were acquired using a custom ultrahigh-resolution OCT system with 2.72 µm axial resolution and 5.52 µm lateral resolution. The network achieved 96.7% accuracy, 92% sensitivity, and 99.7% specificity on a dataset of 23 patients. The usage of deep learning will be important for the practical integration of OCT into clinical practice.

Optical coherence tomography holds promise to transform the diagnostic anatomic pathology gross evaluation process.

SIGNIFICANCE: Real-time histology can close a variety of gaps in tissue diagnostics. Currently, gross pathology analysis of excised tissue is dependent upon visual inspection and palpation to identify regions of interest for histopathological processing. Such analysis is limited by the variable correlation between macroscopic and microscopic findings. The current standard of care is costly, burdensome, and inefficient. AIM: We are the first to address this gap by introducing optical coherence tomography (OCT) to be integrated in real-time during the pathology grossing process. APPROACH: This is achieved by our high-resolution, ultrahigh-speed, large field-of-view OCT device designed for this clinical application. RESULTS: We demonstrate the feasibility of imaging tissue sections from multiple human organs (breast, prostate, lung, and pancreas) in a clinical gross pathology setting without interrupting standard workflows. CONCLUSIONS: OCT-based real-time histology evaluation holds promise for addressing a gap that has been present for >100 years.

Monitoring of irrigated lesion formation with single fiber based multispectral system using machine learning.

In radiofrequency ablation (RFA) treatment of cardiac arrhythmias, intraprocedural assessment of treatment efficacy relies on indirect measures of adequate tissue destruction. Direct sensing of diffuse reflectance spectral changes at the ablation site using optically integrated RFA catheters has been shown to enable accurate prediction of lesion dimensions, ex vivo. Challenges of optical guidance can be due to obtaining reliable measurements under various catheter-tissue contact orientations. In this work, addressed this limitation by assessing the feasibility of monitoring lesion progression using single-fiber reflectance spectroscopy (SFRS). A total of 110 endocardial lesions of various sizes were generated in freshly excised swine right ventricular tissue using a custom-built, irrigated SFRS-RFA catheter. Models were developed for assessing catheter-tissue contact, the presence of nontransmural or transmural lesions and lesion depth percentage. These results support the use of SFRS-based catheters for irrigated lesion assessment and motivate further exploration of using multi-SFRS catheters for omnidirectionality.

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

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 on ex vivo swine (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.

Polarization-sensitive optical coherence tomography monitoring of percutaneous radiofrequency ablation in left atrium of living swine.

Radiofrequency ablation (RFA) is commonly used to treat atrial fibrillation (AF). However, the outcome is often compromised due to the lack of direct real-time feedback to assess lesion transmurality. In this work, we evaluated the ability of polarization-sensitive optical coherence tomography (PSOCT) to measure cardiac wall thickness and assess RF lesion transmurality during left atrium (LA) RFA procedures. Quantitative transmural lesion criteria using PSOCT images were determined ex vivo using an integrated PSOCT-RFA catheter and fresh swine hearts. LA wall thickness of living swine was measured with PSOCT and validated with a micrometer after harvesting the heart. A total of 38 point lesions were created in the LA of 5 living swine with the integrated PSOCT-RFA catheter using standard clinical RFA procedures. For all lesions with analyzable PSOCT images, lesion transmurality was assessed with a sensitivity of 89% (17 of 19 tested positive) and a specificity of 100% (5 of 5 tested negative) using the quantitative transmural criteria. This is the first report of using PSOCT to assess LA RFA lesion transmurality in vivo. The results indicate that PSOCT may potentially provide direct real-time feedback for LA wall thickness and lesion transmurality.

Quantification of irrigated lesion morphology using near-infrared spectroscopy.

There are currently limited means by which lesion formation can be confirmed during radiofrequency ablation procedures. The purpose of this study was to evaluate the use of NIRS-integrated RFA catheters for monitoring irrigated lesion progression, ex vivo and in vivo. Open-irrigated NIRS-ablation catheters with optical fibers were fabricated to sample tissue diffuse reflectance. Spectra from 44 irrigated lesions and 44 non-lesion sites from ex vivo swine hearts (n = 15) were used to train and evaluate a predictive model for lesion dimensions based on key spectral features. Additional studies were performed in diluted blood to assess NIRS signatures of catheter-tissue contact status. Finally, the potential of NIRS-RFA catheters for guiding lesion delivery was evaluated in a set of in vivo pilot studies conducted in healthy pigs (n = 4). Model predictions for lesion depth (R = 0.968), width (R = 0.971), and depth percentage (R = 0.924) correlated well with measured lesion dimensions. In vivo deployment in preliminary trials showed robust translational consistency of contact discrimination (P < 0.0001) and lesion depth parameters (< 3% error). NIRS empowered catheters are well suited for monitoring myocardial response to RF ablation and may provide useful intraprocedural feedback for optimizing treatment efficacy alongside current practices.

Millimeter-scale chip-based supercontinuum generation for optical coherence tomography.

Supercontinuum sources for optical coherence tomography (OCT) have raised great interest as they provide broad bandwidth to enable high resolution and high power to improve imaging sensitivity. Commercial fiber-based supercontinuum systems require high pump powers to generate broad bandwidth and customized optical filters to shape/attenuate the spectra. They also have limited sensitivity and depth performance. We introduce a supercontinuum platform based on a 1-mm2 Si3N4 photonic chip for OCT. We directly pump and efficiently generate supercontinuum near 1300 nm without any postfiltering. With a 25-pJ pump pulse, we generate a broadband spectrum with a flat 3-dB bandwidth of 105 nm. Integrating the chip into a spectral domain OCT system, we achieve 105-dB sensitivity and 1.81-mm 6-dB sensitivity roll-off with 300-µW optical power on sample. We image breast tissue to demonstrate strong imaging performance. Our chip will pave the way toward portable OCT and incorporating integrated photonics into optical imaging technologies.

3-D compressed sensing optical coherence tomography using predictive coding.

We present a compressed sensing (CS) algorithm and sampling strategy for reconstructing 3-D Optical Coherence Tomography (OCT) image volumes from as little as 10% of the original data. Reconstruction using the proposed method, Denoising Predictive Coding (DN-PC), is demonstrated for five clinically relevant tissue types including human heart, retina, uterus, breast, and bovine ligament. DN-PC reconstructs the difference between adjacent b-scans in a volume and iteratively applies Gaussian filtering to improve image sparsity. An a-line sampling strategy was developed that can be easily implemented in existing Spectral-Domain OCT systems and reduce scan time by up to 90%.

Anisotropic Mechanical Properties of the Human Uterus Measured by Spherical Indentation.

The mechanical function of the uterus is critical for a successful pregnancy. During gestation, uterine tissue grows and stretches to many times its size to accommodate the growing fetus, and it is hypothesized the magnitude of uterine tissue stretch triggers the onset of contractions. To establish rigorous mechanical testing protocols for the human uterus in hopes of predicting tissue stretch during pregnancy, this study measures the anisotropic mechanical properties of the human uterus using optical coherence tomography (OCT), instrumented spherical indentation, and video extensometry. In this work, we perform spherical indentation and digital image correlation to obtain the tissue's force and deformation response to a ramp-hold loading regimen. We translate previously reported fiber architecture, measured via optical coherence tomography, into a constitutive fiber composite material model to describe the equilibrium material behavior during indentation. We use an inverse finite element method integrated with a genetic algorithm (GA) to fit the material model to our experimental data. We report the mechanical properties of human uterine specimens taken across different anatomical locations and layers from one non-pregnant (NP) and one pregnant (PG) patient; both patients had pathological uterine tissue. Compared to NP uterine tissue, PG tissue has a more dispersed fiber distribution and equivalent stiffness material parameters. In both PG and NP uterine tissue, the mechanical properties differ significantly between anatomical locations.

Introduction to the Biophotonics Congress 2020 feature issue.

The guest editors introduce a feature issue containing papers based on research presented at the OSA Biophotonics Congress (the former BIOMED) 20-23 April 2020, in the first all virtual, web conference format undertaken by OSA.

Three-dimensional collagen fiber mapping and tractography of human uterine tissue using OCT.

Automatic quantification and visualization of 3-D collagen fiber architecture using Optical Coherence Tomography (OCT) has previously relied on polarization information and/or prior knowledge of tissue-specific fiber architecture. This study explores image processing, enhancement, segmentation, and detection algorithms to map 3-D collagen fiber architecture from OCT images alone. 3-D fiber mapping, histogram analysis, and 3-D tractography revealed fiber groupings and macro-organization previously unseen in uterine tissue samples. We applied our method on centimeter-scale mosaic OCT volumes of uterine tissue blocks from pregnant and non-pregnant specimens revealing a complex, patient-specific network of fibrous collagen and myocyte bundles.