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.

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.

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

BACKGROUND: 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. METHODS AND RESULTS: 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. CONCLUSION: 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.

Chip-based frequency comb sources for optical coherence tomography.

Optical coherence tomography (OCT) is a powerful interferometric imaging technique widely used in medical fields such as ophthalmology, cardiology and dermatology. Superluminescent diodes (SLDs) are widely used as light sources in OCT. Recently integrated chip-based frequency combs have been demonstrated in numerous platforms and the possibility of using these broadband chip-scale combs for OCT has been raised extensively over the past few years. However, the use of these chip-based frequency combs as light sources for OCT requires bandwidth and power compatibility with current OCT systems and have not been shown to date. Here we generate frequency combs based on chip-scale lithographically-defined microresonators and demonstrate its capability as a novel light source for OCT. The combs are designed with a small spectral line spacing of 0.21 nm which ensure imaging range comparable to commercial system and operated at non-phase locked regime which provide conversion efficiency of 30%. The comb source is shown to be compatible with a standard commercial spectral domain (SD) OCT system and enables imaging of human tissue with image quality comparable to the one achieved with tabletop commercial sources. The comb source also provides a path towards fully integrated OCT systems.

Compressed sensing-enabled phase-sensitive swept-source optical coherence tomography.

Here we present a novel phase-sensitive swept-source optical coherence tomography (PhS-SS-OCT) system. The simultaneously recorded calibration signal, which is commonly used in SS-OCT to stabilize the phase, is randomly sub-sampled during the acquisition, and it is later reconstructed based on the Compressed Sensing (CS) theory. We first mathematically investigated the method, and verified it through computer simulations. We then conducted a vibrational frequency test and a flow velocity measurement in phantoms to demonstrate the system's capability of handling phase-sensitive tasks. The proposed scheme shows excellent phase stability with greatly discounted data bandwidth compared with conventional procedures. We further showcased the usefulness of the system in biological samples by detecting the blood flow in ex vivo swine left marginal artery. The proposed system is compatible with most of the existing SS-OCT systems and could be a preferred solution for future high-speed phase-sensitive applications.

High-speed collagen fiber modeling and orientation quantification for optical coherence tomography imaging.

Quantifying collagen fiber architecture has clinical and scientific relevance across a variety of tissue types and adds functionality to otherwise largely qualitative imaging modalities. Optical coherence tomography (OCT) is uniquely suited for this task due to its ability to capture the collagen microstructure over larger fields of view than traditional microscopy. Existing image processing techniques for quantifying fiber architecture, while accurate and effective, are very slow for processing large datasets and tend to lack structural specificity. We describe here a computationally efficient method for quantifying and visualizing collagen fiber organization. The algorithm is demonstrated on swine atria, bovine anterior cruciate ligament, and human cervical tissue samples. Additionally, we show an improved performance for images with crimped fiber textures and low signal to noise when compared to similar methods.

Real-time assessment of catheter contact and orientation using an integrated optical coherence tomography cardiac ablation catheter.

The efficacy of catheter ablation treatment for atrial fibrillation is directly impacted by the quality of lesion formation. Two parameters that are critical for maximizing energy delivery are sustained catheter contact and orientation. Currently, these parameters must be inferred indirectly through tactile feedback or measurements of bioelectrical impedance and tip force. In this work, we propose a method for discerning contact and orientation based on direct endomyocardial imaging mediated by optical coherence tomography (OCT)-integrated ablation catheters. A two-stage classifier is developed to deduce contact parameters from M-mode images. Experimental validation within swine left-atrial specimens demonstrate accuracies of 99.96% and 92.88% for contact and orientation stages, respectively. These results highlight the potential of OCT M-mode imaging for guiding catheter placement during radiofrequency ablation interventions.

Anisotropic Material Characterization of Human Cervix Tissue Based on Indentation and Inverse Finite Element Analysis.

The cervix is essential to a healthy pregnancy as it must bear the increasing load caused by the growing fetus. Preterm birth is suspected to be caused by the premature softening and mechanical failure of the cervix. The objective of this paper is to measure the anisotropic mechanical properties of human cervical tissue using indentation and video extensometry. The human cervix is a layered structure, where its thick stromal core contains preferentially aligned collagen fibers embedded in a soft ground substance. The fiber composite nature of the tissue provides resistance to the complex three-dimensional loading environment of pregnancy. In this work, we detail an indentation mechanical test to obtain the force and deformation response during loading which closely matches in vivo conditions. We postulate a constitutive material model to describe the equilibrium material behavior to ramp-hold indentation, and we use an inverse finite element method based on genetic algorithm (GA) optimization to determine best-fit material parameters. We report the material properties of human cervical slices taken at different anatomical locations from women of different obstetric backgrounds. In this cohort of patients, the anterior internal os (the area where the cervix meets the uterus) of the cervix is stiffer than the anterior external os (the area closest to the vagina). The anatomic anterior and posterior quadrants of cervical tissue are more anisotropic than the left and right quadrants. There is no significant difference in material properties between samples of different parities (number of pregnancies reaching viable gestation age).

Tissue-Specific Optical Mapping Models of Swine Atria Informed by Optical Coherence Tomography.

Computational models and experimental optical mapping of cardiac electrophysiology serve as powerful tools to investigate the underlying mechanisms of arrhythmias. Modeling can also aid the interpretation of optical mapping signals, which may have different characteristics with respect to the underlying electrophysiological signals they represent. However, despite the prevalence of atrial arrhythmias such as atrial fibrillation, models of optical electrical mapping incorporating realistic structure of the atria are lacking. Therefore, we developed image-based models of atrial tissue using structural information extracted from optical coherence tomography (OCT), which can provide volumetric tissue characteristics in high resolution. OCT volumetric data of four swine atrial tissue samples were used to develop models incorporating tissue geometry, tissue-specific myofiber orientation, and ablation lesion regions. We demonstrated the use of these models through electrophysiology and photon scattering simulations. Changes in transmural electrical conduction were observed with the inclusion of OCT-derived, depth-resolved fiber orientation. Additionally, the amplitude of optical mapping signals were not found to correspond with lesion transmurality because of lesion geometry and electrical propagation occurring beyond excitation light penetration. This work established a framework for the development of tissue-specific models of atrial tissue derived from OCT imaging data, which can be useful in future investigations of electrophysiology and optical mapping signals with respect to realistic atrial tissue structure.

Frequency-constrained robust principal component analysis: a sparse representation approach to segmentation of dynamic features in optical coherence tomography imaging.

Sparse representation theory is an exciting area of research with recent applications in medical imaging and detection, segmentation, and quantitative analysis of biological processes. We present a variant on the robust-principal component analysis (RPCA) algorithm, called frequency constrained RPCA (FC-RPCA), for selectively segmenting dynamic phenomena that exhibit spectra within a user-defined range of frequencies. The algorithm lacks subjective parameter tuning and demonstrates robust segmentation in datasets containing multiple motion sources and high amplitude noise. When tested on 17 ex-vivo, time lapse optical coherence tomography (OCT) B-scans of human ciliated epithelium, segmentation accuracies ranged between 91-99% and consistently out-performed traditional RPCA.

Phase-noise analysis of swept-source optical coherence tomography systems.

We propose a new model to characterize the phase noise in swept-source optical coherence tomography (SS-OCT). The new model explicitly incorporates scanning variability, timing jitter, and sample location in addition to intensity noise (shot noise). The model was analyzed and validated by using both Monte Carlo methods and experiments. We suggest that the proposed model can be used as a guideline for future SS-OCT experimental designs.

Visualization and tissue classification of human breast cancer images using ultrahigh-resolution OCT.

BACKGROUND AND OBJECTIVE: Breast cancer is one of the most common cancers, and recognized as the third leading cause of mortality in women. Optical coherence tomography (OCT) enables three dimensional visualization of biological tissue with micrometer level resolution at high speed, and can play an important role in early diagnosis and treatment guidance of breast cancer. In particular, ultra-high resolution (UHR) OCT provides images with better histological correlation. This paper compared UHR OCT performance with standard OCT in breast cancer imaging qualitatively and quantitatively. Automatic tissue classification algorithms were used to automatically detect invasive ductal carcinoma in ex vivo human breast tissue. STUDY DESIGN/MATERIALS AND METHODS: Human breast tissues, including non-neoplastic/normal tissues from breast reduction and tumor samples from mastectomy specimens, were excised from patients at Columbia University Medical Center. The tissue specimens were imaged by two spectral domain OCT systems at different wavelengths: a home-built ultra-high resolution (UHR) OCT system at 800 nm (measured as 2.72 µm axial and 5.52 µm lateral) and a commercial OCT system at 1,300 nm with standard resolution (measured as 6.5 µm axial and 15 µm lateral), and their imaging performances were analyzed qualitatively. Using regional features derived from OCT images produced by the two systems, we developed an automated classification algorithm based on relevance vector machine (RVM) to differentiate hollow-structured adipose tissue against solid tissue. We further developed B-scan based features for RVM to classify invasive ductal carcinoma (IDC) against normal fibrous stroma tissue among OCT datasets produced by the two systems. For adipose classification, 32 UHR OCT B-scans from 9 normal specimens, and 28 standard OCT B-scans from 6 normal and 4 IDC specimens were employed. For IDC classification, 152 UHR OCT B-scans from 6 normal and 13 IDC specimens, and 104 standard OCT B-scans from 5 normal and 8 IDC specimens were employed. RESULTS: We have demonstrated that UHR OCT images can produce images with better feature delineation compared with images produced by 1,300 nm OCT system. UHR OCT images of a variety of tissue types found in human breast tissue were presented. With a limited number of datasets, we showed that both OCT systems can achieve a good accuracy in identifying adipose tissue. Classification in UHR OCT images achieved higher sensitivity (94%) and specificity (93%) of adipose tissue than the sensitivity (91%) and specificity (76%) in 1,300 nm OCT images. In IDC classification, similarly, we achieved better results with UHR OCT images, featured an overall accuracy of 84%, sensitivity of 89% and specificity of 71% in this preliminary study. CONCLUSION: In this study, we provided UHR OCT images of different normal and malignant breast tissue types, and qualitatively and quantitatively studied the texture and optical features from OCT images of human breast tissue at different resolutions. We developed an automated approach to differentiate adipose tissue, fibrous stroma, and IDC within human breast tissues. Our work may open the door toward automatic intraoperative OCT evaluation of early-stage breast cancer. Lasers Surg. Med. 49:258-269, 2017. © 2017 Wiley Periodicals, Inc.

Ex vivo visualization of human ciliated epithelium and quantitative analysis of induced flow dynamics by using optical coherence tomography.

BACKGROUND AND OBJECTIVE: Cilia-driven mucociliary clearance is an important self-defense mechanism of great clinical importance in pulmonary research. Conventional light microscopy possesses the capability to visualize individual cilia and its beating pattern but lacks the throughput to assess the global ciliary activities and flow dynamics. Optical coherence tomography (OCT), which provides depth-resolved cross-sectional images, was recently introduced to this area. MATERIALS AND METHODS: Fourteen de-identified human tracheobronchial tissues are directly imaged by two OCT systems: one system centered at 1,300 nm with 6.5 µm axial resolution and 15 µm lateral resolution, and the other centered at 800 nm with 2.72 µm axial resolution and 5.52 µm lateral resolution. Speckle variance images are obtained in both cross-sectional and volumetric modes. After imaging, sample blocks are sliced along the registered OCT imaging plane and processed with hematoxylin and eosin (H&E) stain for comparison. Quantitative flow analysis is performed by tracking the path-lines of microspheres in a fixed cross-section. Both the flow rate and flow direction are characterized. RESULTS: The speckle variance images successfully segment the ciliated epithelial tissue from its cilia-denuded counterpart, and the results are validated by corresponding H&E stained sections. A further temporal frequency analysis is performed to extract the ciliary beat frequency (CBF) at cilia cites. By adding polyester microspheres as contrast agents, we demonstrate ex vivo imaging of the flow induced by cilia activities of human tracheobronchial samples. CONCLUSION: This manuscript presents an ex vivo study on human tracheobronchial ciliated epithelium and its induced mucous flow by using OCT. Within OCT images, intact ciliated epithelium is effectively distinguished from cilia-denuded counterpart, which serves as a negative control, by examining the speckle variance images. The cilia beat frequency is extracted by temporal frequency analysis. The flow rate, flow direction, and particle throughput are obtained through particle tracking. The availability of these quantitative parameters provides us with a powerful tool that will be useful for studying the physiology, pathophysiology and the effectiveness of therapies on epithelial cilia function, as well as serve as a diagnostic tool for diseases associated with ciliary dysmotility. Lasers Surg. Med. 49:270-279, 2017. © 2017 Wiley Periodicals, Inc.

Signal competition in optical coherence tomography and its relevance for cochlear vibrometry.

The usual technique for measuring vibration within the cochlear partition is heterodyne interferometry. Recently, spectral domain phase microscopy (SDPM) was introduced and offers improvements over standard heterodyne interferometry. In particular, it has a penetration depth of several mm due to working in the infrared range, has narrow and steep optical sectioning due to using a wideband light source, and is able to measure from several cochlear layers simultaneously. However, SDPM is susceptible to systematic error due to "phase leakage," in which the signal from one layer competes with the signal from other layers. Here, phase leakage is explored in vibration measurements in the cochlea and a model structure. The similarity between phase leakage and signal competition in heterodyne interferometry is demonstrated both experimentally and theoretically. Due to phase leakage, erroneous vibration amplitudes can be reported in regions of low reflectivity that are near structures of high reflectivity. When vibration amplitudes are greater than ∼0.1 of the light source wavelength, phase leakage can cause reported vibration waveforms to be distorted. To aid in the screening of phase leakage in experimental results, the error is plotted and discussed as a function of the important parameters of signal strength and vibration amplitude.

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.

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.

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.

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..