Optical coherence tomography imaging of cardiac substrates

A Drosophila heart optical coherence microscopy dataset for automatic video segmentation

The heart of the fruit fly, Drosophila melanogaster, is a particularly suitable model for cardiac studies. Optical coherence microscopy (OCM) captures in vivo cross-sectional videos of the beating Drosophila heart for cardiac function quantification. To analyze those large-size multi-frame OCM recordings, human labelling has been employed, leading to low efficiency and poor reproducibility. Here, we introduce a robust and accurate automated Drosophila heart segmentation algorithm, called FlyNet 2.0+, which utilizes a long short-term memory (LSTM) convolutional neural network to leverage time series information in the videos, ensuring consistent, high-quality segmentation. We present a dataset of 213 Drosophila heart videos, equivalent to 604,000 cross-sectional images, containing all developmental stages and a wide range of beating patterns, including faster and slower than normal beating, arrhythmic beating, and periods of heart stop to capture these heart dynamics. Each video contains a corresponding ground truth mask. We expect this unique large dataset of the beating Drosophila heart in vivo will enable new deep learning approaches to efficiently characterize heart function to advance cardiac research.

Non-destructive Characterization of Skeletal Muscle Extracellular Matrix Morphology by Combining Optical Coherence Tomography (OCT) Imaging with Tissue Clearing

Histology is an essential step to visualize and analyze the microstructure of any biological tissue; however, histological processing is often irreversible, and histological samples are unable to be imaged or tested further. In this work, a novel non-destructive protocol is proposed for morphological analysis of skeletal muscles, combining Optical Coherence Tomography (OCT) imaging with Tissue Clearing. Imaging combining OCT and Propylene Glycol (PG) as a tissue-clearing agent, was performed on rat tail and extensor digitorum longus (EDL) muscle. The results show that the extracellular matrix morphology of skeletal muscles, including muscular fibers and the whole microstructure architecture were clearly identified. PG improved OCT imaging as measured by image quality metric Contrast Per Pixel CPP (increases by 3.9%), Naturalness Image Quality Evaluator NIQE (decreases by 23%), and Volume of Interest VOI size (higher for CPP and lower for NIQE values). The tendon microstructure was observed with less precision, as collagen fibers could not be clearly detected. The reversibility of the optical effects of the PG on the immersed tissue (in a Phosphate-Buffered Saline solution) was studied comparing native and rehydrated OCT image acquisition from a single EDL sample. Optical properties and microstructure visibility (CPP and NIQE) have been recovered to 99% of the native sample values. Moreover, clearing process caused shrinkage of the tissue recovered to 86% of the original width. Future work will aim to employ the proposed experimental protocol to identify the local mechanical properties of biological tissues.

Don't go breakin' my heart: cardioprotective alterations to the mechanical and structural properties of reperfused myocardium during post-infarction inflammation

Myocardial infarctions (MIs) kickstart an intense inflammatory response resulting in extracellular matrix (ECM) degradation, wall thinning, and chamber dilation that leaves the heart susceptible to rupture. Reperfusion therapy is one of the most effective strategies for limiting adverse effects of MIs, but is a challenge to administer in a timely manner. Late reperfusion therapy (LRT; 3 + hours post-MI) does not limit infarct size, but does reduce incidences of post-MI rupture and improves long-term patient outcomes. Foundational studies employing LRT in the mid-twentieth century revealed beneficial reductions in infarct expansion, aneurysm formation, and left ventricle dysfunction. The mechanism by which LRT acts, however, is undefined. Structural analyses, relying largely on one-dimensional estimates of ECM composition, have found few differences in collagen content between LRT and permanently occluded animal models when using homogeneous samples from infarct cores. Uniaxial testing, on the other hand, revealed slight reductions in stiffness early in inflammation, followed soon after by an enhanced resistance to failure for cases of LRT. The use of one-dimensional estimates of ECM organization and gross mechanical function have resulted in a poor understanding of the infarct's spatially variable mechanical and structural anisotropy. To resolve these gaps in literature, future work employing full-field mechanical, structural, and cellular analyses is needed to better define the spatiotemporal post-MI alterations occurring during the inflammatory phase of healing and how they are impacted following reperfusion therapy. In turn, these studies may reveal how LRT affects the likelihood of rupture and inspire novel approaches to guide scar formation.

Toward cardiac tissue characterization using machine learning and light-scattering spectroscopy

SIGNIFICANCE

The non-destructive characterization of cardiac tissue composition provides essential information for both planning and evaluating the effectiveness of surgical interventions such as ablative procedures. Although several methods of tissue characterization, such as optical coherence tomography and fiber-optic confocal microscopy, show promise, many barriers exist that reduce effectiveness or prevent adoption, such as time delays in analysis, prohibitive costs, and limited scope of application. Developing a rapid, low-cost non-destructive means of characterizing cardiac tissue could improve planning, implementation, and evaluation of cardiac surgical procedures.

AIM

To determine whether a new light-scattering spectroscopy (LSS) system that analyzes spectra via neural networks is capable of predicting the nuclear densities (NDs) of ventricular tissues.

APPROACH

We developed an LSS system with a fiber-optics probe and applied it for measurements on cardiac tissues from an ovine model. We quantified the ND in the cardiac tissues using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks (CNNs) to assess the feasibility of characterizing the ND of tissue via LSS.

RESULTS

Spectral clustering revealed distinct groups of spectra correlated to ranges of ND. CNNs classified three groups of spectra with low, medium, or high ND with an accuracy of 95.00  ±  11.77  %   (mean and standard deviation). Our analyses revealed the sensitivity of the classification accuracy to wavelength range and subsampling of spectra.

CONCLUSIONS

LSS and machine learning are capable of assessing ND in cardiac tissues. We suggest that the approach is useful for the diagnosis of cardiac diseases associated with changes of ND, such as hypertrophy and fibrosis.

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%.

An optomechanogram for assessment of the structural and mechanical properties of tissues

The structural and mechanical properties of tissue and the interplay between them play a critical role in tissue function. We introduce the optomechanogram, a combined quantitative and qualitative visualization of spatially co-registered measurements of the microstructural and micromechanical properties of any tissue. Our approach relies on the co-registration of two independent platforms, second-harmonic generation (SHG) microscopy for quantitative assessment of 3D collagen-fiber microstructural organization, and nanoindentation (NI) for local micromechanical properties. We experimentally validate our method by applying to uterine cervix tissue, which exhibits structural and mechanical complexity. We find statistically significant agreement between the micromechanical and microstructural data, and confirm that the distinct tissue regions are distinguishable using either the SHG or NI measurements. Our method could potentially be used for research in pregnancy maintenance, mechanobiological studies of tissues and their constitutive modeling and more generally for the optomechanical metrology of materials.

Ultra-high-resolution SD-OCM imaging with a compact polarization-aligned 840 nm broadband combined-SLED source

We analyze the influence of intrinsic polarization alignment on image quality and axial resolution employing a broadband 840 nm light source with an optical bandwidth of 160 nm and an output power of 12 mW tailored for spectral-domain optical coherence microscopy (SD-OCM) applications. Three superluminescent diodes (SLEDs) are integrated into a 14-pin butterfly module using a free-space micro-optical bench architecture, maintaining a constant polarization state across the full spectral output. We demonstrate superior imaging performance in comparison to traditionally coupled-SLED broadband light sources in a teleost model organism in-vivo.

High-resolution 3D tractography of fibrous tissue based on polarization-sensitive optical coherence tomography

Fibrous tissues play important roles in many parts of the body. Their highly organized directional structure is essential in achieving their normal biomechanical and physiological functions. Disruption of the typical fiber organization in these tissues is often linked to pathological changes and disease progression. Tractography is a specialized imaging method that can reveal the detailed fiber architecture. Here, we review recent developments in high-resolution optical tractography using Jones matrix polarization-sensitive optical coherence tomography. We also illustrate the use of this new tractography technology for visualizing depth-resolved, three-dimensional fibrous structures and quantifying tissue damages in several major fibrous tissues.

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.