This study’s goal is to apply Denoising Predictive Coding (DN-PC), a novel compressed sensing (CS) algorithm for reconstruction of optical coherence tomography (OCT) images of breast tissue. We have assembled an image bank of representative OCT samples of normal breast and breast cancer tissue. We apply DN-PC for compression at decreasing a-line sampling rates to evaluate a relation between reconstruction behavior and image features of breast tissue as well as compare to classic downsampling and interpolation-based methods.
This study aims to explore Denoising Predictive Coding (DN-PC), a new compressed sensing (CS) algorithm for reconstruction of optical coherence tomography (OCT) image volumes. In preliminary work, the algorithm has yielded reconstructions of OCT images of various biological samples, with accuracy and computation time superior to other CS methods. Here we have assembled an image bank of representative OCT images of normal breast such as adipose and stroma, and pathology such as breast cancer. We apply DN-PC for compression at decreasing a-line sampling rates to evaluate a relation between reconstruction behavior and breast tissue structure.
KEYWORDS: Tissue optics, Optical spectroscopy, Spectroscopy, Near infrared spectroscopy, Heart, 3D metrology, Tissues, Reflectance spectroscopy, Monte Carlo methods, In vitro testing
Epicardial catheter ablation has been increasingly recognized as an important adjunct in treating ventricular arrhythmias unamenable by endocardial ablation alone. The presence of epicardial adipose tissue (EAT) is a primary cause for ineffective ablation energy delivery and electrogram misinterpretation. To address this need, we propose a catheter-based near-infrared spectroscopic technique for mapping EAT and lesion deposition, and validate it within excised human donor hearts. We introduce a new parameter, the adipose contrast index (ACI), for rapid lipid assessment. Strong correspondence was observed between values derived from interpolated 3-dimensional ACI maps and histologically-determined EAT layer thickness (Pearson’s, R = 0.903).
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