Spatial coordinate corrected motion tracking for optical coherence elastography

Wave-based optical coherence elastography: The 10-year perspective

After 10 years of progress and innovation, optical coherence elastography (OCE) based on the propagation of mechanical waves has become one of the major and the most studied OCE branches, producing a fundamental impact in the quantitative and nondestructive biomechanical characterization of tissues. Preceding previous progress made in ultrasound and magnetic resonance elastography; wave-based OCE has pushed to the limit the advance of three major pillars: (1) implementation of novel wave excitation methods in tissues, (2) understanding new types of mechanical waves in complex boundary conditions by proposing advance analytical and numerical models, and (3) the development of novel estimators capable of retrieving quantitative 2D/3D biomechanical information of tissues. This remarkable progress promoted a major advance in answering basic science questions and the improvement of medical disease diagnosis and treatment monitoring in several types of tissues leading, ultimately, to the first attempts of clinical trials and translational research aiming to have wave-based OCE working in clinical environments. This paper summarizes the fundamental up-to-date principles and categories of wave-based OCE, revises the timeline and the state-of-the-art techniques and applications lying in those categories, and concludes with a discussion on the current challenges and future directions, including clinical translation research.

Enhanced medical diagnosis for dOCTors: a perspective of optical coherence tomography

SIGNIFICANCE

After three decades, more than 75,000 publications, tens of companies being involved in its commercialization, and a global market perspective of about USD 1.5 billion in 2023, optical coherence tomography (OCT) has become one of the fastest successfully translated imaging techniques with substantial clinical and economic impacts and acceptance.

AIM

Our perspective focuses on disruptive forward-looking innovations and key technologies to further boost OCT performance and therefore enable significantly enhanced medical diagnosis.

APPROACH

A comprehensive review of state-of-the-art accomplishments in OCT has been performed.

RESULTS

The most disruptive future OCT innovations include imaging resolution and speed (single-beam raster scanning versus parallelization) improvement, new implementations for dual modality or even multimodality systems, and using endogenous or exogenous contrast in these hybrid OCT systems targeting molecular and metabolic imaging. Aside from OCT angiography, no other functional or contrast enhancing OCT extension has accomplished comparable clinical and commercial impacts. Some more recently developed extensions, e.g., optical coherence elastography, dynamic contrast OCT, optoretinography, and artificial intelligence enhanced OCT are also considered with high potential for the future. In addition, OCT miniaturization for portable, compact, handheld, and/or cost-effective capsule-based OCT applications, home-OCT, and self-OCT systems based on micro-optic assemblies or photonic integrated circuits will revolutionize new applications and availability in the near future. Finally, clinical translation of OCT including medical device regulatory challenges will continue to be absolutely essential.

CONCLUSIONS

With its exquisite non-invasive, micrometer resolution depth sectioning capability, OCT has especially revolutionized ophthalmic diagnosis and hence is the fastest adopted imaging technology in the history of ophthalmology. Nonetheless, OCT has not been completely exploited and has substantial growth potential-in academics as well as in industry. This applies not only to the ophthalmic application field, but also especially to the original motivation of OCT to enable optical biopsy, i.e., the in situ imaging of tissue microstructure with a resolution approaching that of histology but without the need for tissue excision.

Optical flow optical coherence tomography for determining accurate velocity fields

Determining micron-scale fluid flow velocities using optical coherence tomography (OCT) is important in both biomedical research and clinical diagnosis. Numerous methods have been explored to quantify the flow information, which can be divided into either phase-based or amplitude-based methods. However, phase-based methods, such as Doppler methods, are less sensitive to transverse velocity components and suffer from wrapped phase and phase instability problems for axial velocity components. On the other hand, amplitude-based methods, such as speckle variance OCT, correlation mapping OCT and split-spectrum amplitude-decorrelation angiography, focus more on segmenting flow areas than quantifying flow velocities. In this paper, we propose optical flow OCT (OFOCT) to quantify accurate velocity fields. The equivalence between optical flow and real velocity fields is validated in OCT imaging. The sensitivity fall-off of a Fourier-domain OCT (FDOCT) system is considered in the modified optical flow continuity constraint. Spatial-temporal smoothness constraints are used to make the optical flow problem well-posed and reduce noises in the velocity fields. An iteration solution to the optical flow problem is implemented in a graphics processing unit (GPU) for real-time processing. The accuracy of the velocity fields is verified through phantom flow experiments by using a diluted milk powder solution as a scattering medium. Velocity fields are then used to detect flow turbulence and reconstruct flow trajectory. The results show that OFOCT is accurate in determining velocity fields and applicable to research concerning fluid dynamics.

Single fiber OCT imager for breast tissue classification based on deep learning

We investigated a deep learning strategy to analyze optical coherence tomography image for accurate tissue characterization based on a single fiber OCT probe. We obtained OCT data from human breast tissue specimens. Using OCT data obtained from adipose breast tissue (normal tissue) and diseased tissue as confirmed in histology, we trained and validated a convolutional neural network (CNN) for accurate breast tissue classification. We demonstrated tumor margin identification based CNN classification of tissue at different spatial locations. We further demonstrated CNN tissue classification in OCT imaging based on a manually scanned single fiber probe. Our results demonstrated that OCT imaging capability integrated into a low-cost, disposable single fiber probe, along with sophisticated deep learning algorithms for tissue classification, allows minimally invasive tissue characterization, and can be used for cancer diagnosis or surgical margin assessment.

Spatial coordinate corrected motion tracking for optical coherence elastography

We develop a spatial coordinate corrected (SCC) motion tracking method for optical coherence elastography. SCC motion tracking refers the instantaneous velocity field extracted from optical coherence tomography (OCT) data to the laboratory coordinate system and accurately reconstructs the displacement field established during the mechanical excitation (compression) process. We acquired image data from compression OCE experiments on human breast tissue specimens, and reconstructed the displacement field through Doppler analysis of OCT data. Our results suggested that SCC tracking enables accurate reconstruction of displacement field, and enables effective identification mechanical heterogeneity that can be used as a biomarker for cancer diagnosis and tumor margin assessment.