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.

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

Automatic quantification and visualization of 3-D collagen fiber architecture using Optical Coherence Tomography (OCT) has previously relied on polarization information and/or prior knowledge of tissue-specific fiber architecture. This study explores image processing, enhancement, segmentation, and detection algorithms to map 3-D collagen fiber architecture from OCT images alone. 3-D fiber mapping, histogram analysis, and 3-D tractography revealed fiber groupings and macro-organization previously unseen in uterine tissue samples.

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.

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.

Human Corneal Fibroblast Pattern Evolution and Matrix Synthesis on Mechanically Biased Substrates.

In a fibroblast colony model of corneal stromal development, we asked how physiological tension influences the patterning dynamics of fibroblasts and the orientation of deposited extracellular matrix (ECM). Using long-term live-cell microscopy, enabled by an optically accessible mechanobioreactor, a primary human corneal fibroblast colony was cultured on three types of substrates: a mechanically biased, loaded, dense, disorganized collagen substrate (LDDCS), a glass coverslip, and an unloaded, dense, disorganized collagen substrate (UDDCS). On LDDCS, fibroblast orientation and migration along a preferred angle developed early, cell orientation was correlated over long distances, and the colony pattern was stable.

The rate of detection of intravascular injection in cervical transforaminal epidural steroid injections with and without digital subtraction angiography.

Objective: To determine whether digital subtraction angiography (DSA) combined with real-time fluoroscopic imaging improves the detection rate of intravascular injection during cervical transforaminal epidural steroid injections (CTFESIs).

Design: Retrospective analysis.

Setting: Outpatient surgery center.