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

Simulation of Uterus Active Contraction and Fetus Delivery in ls-dyna

Vaginal childbirth is the final phase of pregnancy when one or more fetuses pass through the birth canal from the uterus, and it is a biomechanical process. The uterine active contraction, causing the pushing force on the fetus, plays a vital role in regulating the fetus delivery process. In this project, the active contraction behaviors of muscle tissue were first modeled and investigated. After that, a finite element method (FEM) model to simulate the uterine cyclic active contraction and delivery of a fetus was developed in ls-dyna. The active contraction was driven through contractile fibers modeled as one-dimensional truss elements, with the Hill material model governing their response. Fibers were assembled in the longitudinal, circumferential, and normal (transverse) directions to correspond to tissue microstructure, and they were divided into seven regions to represent the strong anisotropy of the fiber distribution and activity within the uterus. The passive portion of the uterine tissue was modeled with a Neo Hookean hyperelastic material model. Three active contraction cycles were modeled. The cyclic uterine active contraction behaviors were analyzed. Finally, the fetus delivery through the uterus was simulated. The model of the uterine active contraction presented in this paper modeled the contractile fibers in three-dimensions, considered the anisotropy of the fiber distribution, provided the uterine cyclic active contraction and propagation of the contraction waves, performed a large deformation, and caused the pushing effect on the fetus. This model will be combined with a model of pelvic structures so that a complete system simulating the second stage of the delivery process of a fetus can be established.

NerveTracker: a Python-based software toolkit for visualizing and tracking groups of nerve fibers in serial block-face microscopy with ultraviolet surface excitation images

Significance

Information about the spatial organization of fibers within a nerve is crucial to our understanding of nerve anatomy and its response to neuromodulation therapies. A serial block-face microscopy method [three-dimensional microscopy with ultraviolet surface excitation (3D-MUSE)] has been developed to image nerves over extended depths ex vivo. To routinely visualize and track nerve fibers in these datasets, a dedicated and customizable software tool is required.

Aim

Our objective was to develop custom software that includes image processing and visualization methods to perform microscopic tractography along the length of a peripheral nerve sample.

Approach

We modified common computer vision algorithms (optic flow and structure tensor) to track groups of peripheral nerve fibers along the length of the nerve. Interactive streamline visualization and manual editing tools are provided. Optionally, deep learning segmentation of fascicles (fiber bundles) can be applied to constrain the tracts from inadvertently crossing into the epineurium. As an example, we performed tractography on vagus and tibial nerve datasets and assessed accuracy by comparing the resulting nerve tracts with segmentations of fascicles as they split and merge with each other in the nerve sample stack.

Results

We found that a normalized Dice overlap (Dice norm ) metric had a mean value above 0.75 across several millimeters along the nerve. We also found that the tractograms were robust to changes in certain image properties (e.g., downsampling in-plane and out-of-plane), which resulted in only a 2% to 9% change to the meanDice norm values. In a vagus nerve sample, tractography allowed us to readily identify that subsets of fibers from four distinct fascicles merge into a single fascicle as we move ∼ 5    mm along the nerve's length.

Conclusions

Overall, we demonstrated the feasibility of performing automated microscopic tractography on 3D-MUSE datasets of peripheral nerves. The software should be applicable to other imaging approaches. The code is available at https://github.com/ckolluru/NerveTracker.

Simulation of the Childbirth Process in LS-DYNA

Childbirth or labor, as the final phase of a pregnancy, is a biomechanical process that delivers the fetus from the uterus. It mainly involves two important biological structures in the mother, the uterus-generating the pushing force on the fetus-and the pelvis (bony pelvis and pelvic floor muscles)-resisting the movement of the fetus. The existing computational models developed in this field that simulate the childbirth process have focused on either the uterine expulsion force or the resistive structures of the pelvis, not both. An FEM model including both structures as a system was developed in this paper to simulate the fetus delivery process in LS-DYNA. Uterine active contraction was driven by contractile fiber elements using the Hill material model. The passive portion of the uterus and pelvic floor muscles were modeled with Neo Hookean and Mooney-Rivlin materials, respectively. The bony pelvis was modeled as a rigid body. The fetus was divided into three components: the head, neck, and body. Three uterine active contraction cycles were modeled. The model system was validated based on multiple outputs from the model, including the stress distribution within the uterus, the maximum Von Mises and principal stress on the pelvic floor muscles, the duration of the second stage of the labor, and the movement of the fetus. The developed model system can be applied to investigate the effects of pathomechanics related to labor, such as pelvic floor disorders and brachial plexus injury.

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.

Three-dimensional analysis of load-dependent changes in the orientation of dermal collagen fibers in human skin: A pilot study

The availability of quantitative structural data on the orientation of collagen fibers is of crucial importance for understanding the behavior of connective tissues. These fibers can be visualized using a variety of imaging techniques, including second harmonic generation (SHG) microscopy. However, characterization of the collagen network requires the accurate extraction of parameters from imaging data. To this end, several automated processes have been developed to identify the preferred orientation of collagen fibers. Common methods include fast Fourier transforms and curvelet transforms, but these tools are mostly used to infer a single preferred orientation. The purpose of this pilot study was to develop an easy procedure for comprehensively comparing multiple human skin samples with the goal of analyzing load-dependent changes via SHG microscopy. We created a 3D model based upon 2D image stacks that provide fiber orientation data perpendicular and parallel to the plane of the epidermis. The SHG images were analyzed by CurveAlign to obtain angle histogram plots containing information about the multiple fiber orientations in each single image. Subsequently, contour plots of the angle histogram intensities were created to provide a useful visual plotting method to clearly show the anomalies in the angle histograms in all samples. Our results provided additional details on how the collagen network carries a load. In fact, analysis of SHG images indicated that increased stretch was accompanied by an increase in the alignment of fibers in the loading direction. Moreover, these images demonstrated that more than one type of preferred orientation is present. In particular, the 3D network of fibers appears to have two preferred orientations in the planes both perpendicular and parallel to the plane of the epidermis.

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.

A Method for the Reconstruction of Myocardial Fiber Structure in Diffusivity Adaptive Imaging Based on Particle Filter

In order to explore the cause of characteristic change and pathological variation of myocardial fiber structure, the posterior probability distribution of fiber direction was described. To solve the problems of low computational efficiency and slow convergence of traditional particle filter, an adaptive particle filter myocardial fiber reconstruction algorithm based on diffusion anisotropy is proposed. This algorithm dynamically adjusts the number of particles and the disturbance intensity at the prediction stage according to the diffusion anisotropy values at different body elements. While ensuring the quality of state estimation, the computational complexity of the algorithm is reduced and the operating efficiency of the system is significantly improved. The experimental results show that the proposed method has strong anti-noise ability. While improving the accuracy of fiber reconstruction, the computational cost of the system decreases by 50%, which significantly improves the efficiency of the system. The proposed algorithm is good over traditional PF and STL approaches.

Membrane curvature and connective fiber alignment in guinea pig round window membrane

The round window membrane (RWM) covers an opening between the perilymph fluid-filled inner ear space and the air-filled middle ear space. As the only non-osseous barrier between these two spaces, the RWM is an ideal candidate for aspiration of perilymph for diagnostics purposes and delivery of medication for treatment of inner ear disorders. Routine access across the RWM requires the development of new surgical tools whose design can only be optimized with a thorough understanding of the RWM's structure and properties. The RWM possesses a layer of collagen and elastic fibers so characterization of the distribution and orientation of these fibers is essential. Confocal and two-photon microscopy were conducted on intact RWMs in a guinea pig model to characterize the distribution of collagen and elastic fibers. The fibers were imaged via second-harmonic-generation, autofluorescence, and Rhodamine B staining. Quantitative analyses of both fiber orientation and geometrical properties of the RWM uncovered a significant correlation between mean fiber orientations and directions of zero curvature in some portions of the RWM, with an even more significant correlation between the mean fiber orientations and linear distance along the RWM in a direction approximately parallel to the cochlear axis. The measured mean fiber directions and dispersions can be incorporated into a generalized structure tensor for use in the development of continuum anisotropic mechanical constitutive models that in turn will enable optimization of surgical tools to access the cochlea. STATEMENT OF SIGNIFICANCE: The Round Window Membrane (RWM) is the only non-osseous barrier separating the middle and inner ear spaces, and thus is an ideal portal for medical access to the cochlea. An understanding of RWM structure and mechanical response is necessary to optimize the design of surgical tools for this purpose. The RWM geometry and the connective fiber orientation and dispersion are measured via confocal and 2-photon microscopy. A region of the RWM geometry is characterized as a hyperbolic paraboloid and another region as a tapered parabolic cylinder. Predominant fiber directions correlate well with directions of zero curvature in the hyperbolic paraboloid region. Overall fiber directions correlate well with position along a line approximately parallel to the central axis of the cochlea's spiral.

Deep Learning in Biomedical Optics

This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.

Computational multi-directional optical coherence tomography for visualizing the microstructural directionality of the tissue

We demonstrate computational multi-directional optical coherence tomography (OCT) to assess the directional property of tissue microstructure. This method is the combination of phase-sensitive volumetric OCT imaging and post-signal processing. The latter comprises of two steps. The first step is an intensity-directional analysis, which determines the dominant en face fiber orientations. The second step is the phase-directional imaging, which reveals the sub-resolution depth-orientation of the microstructure. The feasibility of the method was tested by assessing muscle and tendon samples. Stripe patterns with several sizes were visualized in the phase-directional images. In order to interpret these images, the muscle and tendon structures were numerically modeled, and the phase-directional images were generated from the numerical model. The similarity of the experimental and numerical results suggested that the stripe patterns correspond to the muscle fiber bundle and its crimping.

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.

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%.