Multi-shaping technique reduces sidelobe magnitude in optical coherence tomography

Optical coherence tomography otoscope for imaging of tympanic membrane and middle ear pathology

Significance

Pathologies within the tympanic membrane (TM) and middle ear (ME) can lead to hearing loss. Imaging tools available in the hearing clinic for diagnosis and management are limited to visual inspection using the classic otoscope. The otoscopic view is limited to the surface of the TM, especially in diseased ears where the TM is opaque. An integrated optical coherence tomography (OCT) otoscope can provide images of the interior of the TM and ME space as well as an otoscope image. This enables the clinicians to correlate the standard otoscopic view with OCT and then use the new information to improve the diagnostic accuracy and management.

Aim

We aim to develop an OCT otoscope that can easily be used in the hearing clinic and demonstrate the system in the hearing clinic, identifying relevant image features of various pathologies not apparent in the standard otoscopic view.

Approach

We developed a portable OCT otoscope device featuring an improved field of view and form-factor that can be operated solely by the clinician using an integrated foot pedal to control image acquisition. The device was used to image patients at a hearing clinic.

Results

The field of view of the imaging system was improved to a 7.4 mm diameter, with lateral and axial resolutions of 38    μ m and 33.4    μ m , respectively. We developed algorithms to resample the images in Cartesian coordinates after collection in spherical polar coordinates and correct the image aberration. We imaged over 100 patients in the hearing clinic at USC Keck Hospital. Here, we identify some of the pathological features evident in the OCT images and highlight cases in which the OCT image provided clinically relevant information that was not available from traditional otoscopic imaging.

Conclusions

The developed OCT otoscope can readily fit into the hearing clinic workflow and provide new relevant information for diagnosing and managing TM and ME disease.

Dispersion mismatch correction for evident chromatic anomaly in low coherence interferometry

The applications of ultrafast optics to biomedical microscopy have expanded rapidly in recent years, including interferometric techniques like optical coherence tomography and microscopy (OCT/OCM). The advances of ultra-high resolution OCT and the inclusion of OCT/OCM in multimodal systems combined with multiphoton microscopy have marked a transition from using pseudo-continuous broadband sources, such as superluminescent diodes, to ultrafast supercontinuum optical sources. We report anomalies in the dispersion profiles of low-coherence ultrafast pulses through long and non-identical arms of a Michelson interferometer that are well beyond group delay or third-order dispersions. This chromatic anomaly worsens the observed axial resolution and causes fringe artifacts in the reconstructed tomograms in OCT/OCM using traditional algorithms. We present DISpersion COmpensation Techniques for Evident Chromatic Anomalies (DISCOTECA) as a universal solution to address the problem of chromatic dispersion mismatch in interferometry, especially with ultrafast sources. First, we demonstrate the origin of these artifacts through the self-phase modulation of ultrafast pulses due to focusing elements in the beam path. Next, we present three solution paradigms for DISCOTECA: optical, optoelectronic, and computational, along with quantitative comparisons to traditional methods to highlight the improvements to the dynamic range and axial profile. We explain the piecewise reconstruction of the phase mismatch between the arms of the spectral-domain interferometer using a modified short-term Fourier transform algorithm inspired by spectroscopic OCT. Finally, we present a decision-making guide for evaluating the utility of DISCOTECA in interferometry and for the artifact-free reconstruction of OCT images using an ultrafast supercontinuum source for biomedical applications.

Multi-window approach enables two-fold improvement in OCT axial resolution with strong side-lobe suppression and improved phase sensitivity

A common processing approach for optical coherence tomography (OCT) uses a window function (e.g., Hann or rectangular window) for spectral shaping prior to calculating the Fourier transform. Here we build on a multi-window approach [Opt. Express8, 5267 (2017)10.1364/BOE.8.005267] that enables improved resolution while still suppressing side-lobe intensity. The shape of the window function defines the trade-off between main-lobe width (resolution) and side-lobe intensity. We have extended the approach to include the interferometric phase for phase-sensitive applications like vibrometry and Doppler OCT. Using the Hann window as a reference, we show that 11 Taylor windows are sufficient to achieve 50% improvement in axial resolution, -31 dB side-lobe intensity, and 20% improvement in phase sensitivity with low computational cost.

Multi-shaping sparse-continuous reconstruction for an optical coherence tomography sidelobe suppression

Optical coherence tomography (OCT) images are commonly affected by sidelobe artifacts due to spectral non-uniformity and spectral leakage. Conventional frequency domain spectral shaping methods widen the mainlobe and compromise axial resolution. While image-domain deconvolution techniques can address the trade-off between axial resolution and artifact suppression, their reconstruction quality relies on accurate measurement or estimation of system point spread function (PSF). Inaccurate PSF estimation leads to loss of details in the reconstructed images. In this Letter, we introduce multi-shaping sparse-continuous reconstruction (MSSCR) for an OCT image, a novel, to the best of our knowledge, framework that combines spectral multi-shaping and iterative image reconstruction with sparse-continuous priors. The MSSCR achieves sidelobe suppression without requiring any PSF measurement or estimation and effectively preserving the axial resolution. The experimental results demonstrate that the MSSCR achieves sidelobe suppression of more than 8 dB. We believe that the MSSCR holds potential for addressing sidelobe artifacts in OCT.

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregards frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.

Self super-resolution of optical coherence tomography images based on deep learning

As a medical imaging modality, many researches have been devoted to improving the resolution of optical coherence tomography (OCT). We developed a deep-learning based OCT self super-resolution (OCT-SSR) pipeline to improve the axial resolution of OCT images based on the high-resolution and low-resolution spectral data collected by the OCT system. In this pipeline, the enhanced super-resolution asymmetric generative adversarial networks were built to improve the network outputs without increasing the complexity. The feasibility and effectiveness of the approach were demonstrated by experimental results on the images of the biological samples collected by the home-made spectral-domain OCT and swept-source OCT systems. More importantly, we found the sidelobes in the original images can be obviously suppressed while improving the resolution based on the OCT-SSR method, which can help to reduce pseudo-signal in OCT imaging when non-Gaussian spectra light source is used. We believe that the OCT-SSR method has broad prospects in breaking the limitation of the source bandwidth on the axial resolution of the OCT system.

Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe

Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, high-speed, high-sensitive volumetric biological imaging in vivo. However, systemic features inherent in the basic operating principle of OCT limit its imaging performance such as spatial resolution and signal-to-noise ratio. Here, we propose a deep learning-based OCT image enhancement framework that exploits raw interference fringes to achieve further enhancement from currently obtainable optimized images. The proposed framework for enhancing spatial resolution and reducing speckle noise in OCT images consists of two separate models: an A-scan-based network (NetA) and a B-scan-based network (NetB). NetA utilizes spectrograms obtained via short-time Fourier transform of raw interference fringes to enhance axial resolution of A-scans. NetB was introduced to enhance lateral resolution and reduce speckle noise in B-scan images. The individually trained networks were applied sequentially. We demonstrate the versatility and capability of the proposed framework by visually and quantitatively validating its robust performance. Comparative studies suggest that deep learning utilizing interference fringes can outperform the existing methods. Furthermore, we demonstrate the advantages of the proposed method by comparing our outcomes with multi-B-scan averaged images and contrast-adjusted images. We expect that the proposed framework will be a versatile technology that can improve functionality of OCT.

Optical coherence microscopy with a split-spectrum image reconstruction method for temporal-dynamics contrast-based imaging of intracellular motility

Biomedical researchers use optical coherence microscopy (OCM) for its high resolution in real-time label-free tomographic imaging. However, OCM lacks bioactivity-related functional contrast. We developed an OCM system that can measure changes in intracellular motility (indicating cellular process states) via pixel-wise calculations of intensity fluctuations from metabolic activity of intracellular components. To reduce image noise, the source spectrum is split into five using Gaussian windows with 50% of the full bandwidth. The technique verified that F-actin fiber inhibition by Y-27632 reduces intracellular motility. This finding could be used to search for other intracellular-motility-associated therapeutic strategies for cardiovascular diseases.

Convolutional dictionary learning for blind deconvolution of optical coherence tomography images

In this study, we demonstrate a sparsity-regularized, complex, blind deconvolution method for removing sidelobe artefacts and stochastic noise from optical coherence tomography (OCT) images. Our method estimates the complex scattering amplitude of tissue on a line-by-line basis by estimating and deconvolving the complex, one-dimensional axial point spread function (PSF) from measured OCT A-line data. We also present a strategy for employing a sparsity weighting mask to mitigate the loss of speckle brightness within tissue-containing regions caused by the sparse deconvolution. Qualitative and quantitative analyses show that this approach suppresses sidelobe artefacts and background noise better than traditional spectral reshaping techniques, with negligible loss of tissue structure. The technique is particularly useful for emerging OCT applications where OCT images contain strong specular reflections at air-tissue boundaries that create large sidelobe artefacts.

400 MHz ultrafast optical coherence tomography

An ultrafast time-stretched swept source with a sweep rate of 400 MHz is demonstrated based on the buffering of a 100 MHz femtosecond laser pulse train. To the best of our knowledge, this is the highest sweep rate of swept sources for optical coherence tomography (OCT) that has been reported. With a 10 dB sweep range of ∼100nm, an axial resolution of 19 µm is obtained in the OCT. OCT imaging of high-speed rotating disks is demonstrated. A composite complex apodization method is proposed and demonstrated to enhance the signal to noise ratio in the OCT imaging.

Effect of spectral leakage on the image formation of Fourier-domain optical coherence tomography

We report on the investigation of spectral leakage's impact on the reconstruction of Fourier-domain optical coherence tomography (FD-OCT). We discuss the shift-variant nature introduced by the spectral leakage and develop a novel spatial-domain FD-OCT image formation model. A proof-of-concept phantom experiment is conducted to validate our model. Compared with previous models, the proposed framework could better describe the image formation process, especially when the fineness of the axial structure approaches the theoretical resolution limit.