Catheterized Fiber-Optics Confocal Microscopy of the Beating Heart In Situ

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.

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.

Towards Automated Quantification of Atrial Fibrosis in Images from Catheterized Fiber-Optics Confocal Microscopy Using Convolutional Neural Networks

Clinical approaches for quantification of atrial fibrosis are currently based on digital image processing of magnetic resonance images. Here, we introduce and evaluate a comprehensive framework based on convolutional neural networks for quantifying atrial fibrosis from images acquired with catheterized fiber-optics confocal microscopy (FCM). FCM images in three regions of the atria were acquired in the beating heart in situ in an established transgenic animal model of atrial fibrosis. Fibrosis in the imaged regions was histologically assessed in excised tissue. FCM images and their corresponding histologically-assessed fibrosis levels were used for training of a convolutional neural network. We evaluated the utility and performance of the convolutional neural networks by varying parameters including image dimension and training batch size. In general, we observed that the root-mean square error (RMSE) of the predicted fibrosis was decreased with increasing image dimension. We achieved a RMSE of 2.6% and a Pearson correlation coefficient of 0.953 when applying a network trained on images with a dimension of 400 × 400 pixels and a batch size of 128 to our test image set. The findings indicate feasibility of our approach for fibrosis quantification from images acquired with catheterized FCM using convolutional neural networks. We suggest that the developed framework will facilitate translation of catheterized FCM into a clinical approach that complements current approaches for quantification of atrial fibrosis.