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We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling-TNode to generate training data. We show that, while TNode processing is computationally demanding, it serves as a convenient, accessible gold-standard source for training data; our cGAN replicates efficient suppression of speckle while preserving tissue structures with dimensions approaching the system resolution of non-local means despeckling while being two orders of magnitude faster than TNode. We demonstrate fast, effective, and high-quality despeckling of the proposed network in different tissue types acquired with three different OCT systems compared to existing deep learning methods. The open-source nature of our work facilitates re-training and deployment in any OCT system with an all-software implementation, working around the challenge of generating high-quality, speckle-free training data.

Intravascular polarimetry with catheter-based polarization-sensitive optical coherence tomography (PS-OCT) complements the high-resolution structural tomograms of OCT with morphological contrast available through polarimetry. Its clinical translation has been complicated by the need for modification of conventional OCT hardware to enable polarimetric measurements. Here, we present a signal processing method to reconstruct polarization properties of tissue from measurements with a single input polarization state, bypassing the need for modulation or multiplexing of input states. Our method relies on a polarization symmetry intrinsic to round-trip measurements and uses the residual spectral variation of the polarization states incident on the tissue to avoid measurement ambiguities. We demonstrate depth-resolved birefringence and optic axis orientation maps reconstructed from in-vivo data of human coronary arteries. We validate our method through comparison with conventional dual-input state measurements and find a mean cumulative retardance error of 13.2deg without observable bias. The 95% limit of agreement between depth-resolved birefringence is 2.80 x 10^(-4), which is less than the agreement between two repeat pullbacks of conventional PS-OCT (3.14 x 10^(-4)), indicating that the two methods can be used interchangeably. The hardware simplification arising from using a single input state may be decisive in realizing the potential of polarimetric measurements for assessing coronary atherosclerosis in clinical practice.

Imaging through optical multimode fibers (MMFs) has the potential to enable hair-thin endoscopes that reduce the invasiveness of imaging deep inside tissues and organs. Current approaches predominantly require active wavefront shaping and fluorescent labeling, which limits their use to preclinical applications and frustrates imaging speed. Here we present a computational approach to reconstruct depth-gated confocal images using a raster-scanned, focused input illumination. We demonstrate the compatibility of this approach with quantitative phase, dark-field, and polarimetric imaging. Computational imaging through MMF opens a new pathway for minimally invasive imaging in medical diagnosis and biological investigations., Comment: 13 pages, 5 figures, journal

Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses and provide critical vessel parameters to optimize percutaneous intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) simultaneously provides high-resolution cross-sectional images of vascular structures while also revealing preponderant tissue components such as collagen and smooth muscle and thereby enhances plaque characterization. Automated interpretation of these features promises to facilitate the objective clinical investigation of the natural history and significance of coronary atheromas. Here, we propose a convolutional neural network model, optimized using a new multi-term loss function, to classify the lumen, intima, and media layers in addition to the guidewire and plaque shadows. We demonstrate that our multi-class classification model outperforms state-of-the-art methods in detecting the coronary anatomical layers. Furthermore, the proposed model segments two classes of common imaging artifacts and detects the anatomical layers within the thickened vessel wall regions that were excluded from analysis by other studies. The source code and the trained model are publicly available at https://github.com/mhaft/OCTseg

Reciprocity is a fundamental principle of wave physics and directly relates to the symmetry in the transmission through a system when interchanging the input and output. The coherent transmission matrix (TM) is a convenient method to characterize wave transmission through general media. Here we demonstrate the optical reciprocal nature of complex media by exploring their TM properties. We measured phase-corrected TMs of forward and round-trip propagation through a looped 1m-long step-index optical multimode fiber (MMF) to experimentally verify a transpose relationship between forward and backward transmission. This symmetry impedes straightforward MMF calibration from proximal measurements of the round-trip TM. Furthermore, we show how focusing through the MMF with digital optical phase conjugation is compromised by system loss, since time reversibility relies on power conservation. These insights may inform development of new imaging techniques through complex media and coherent control of waves in photonic systems., Comment: 11 pages, 6 figures, under review

In Fourier-domain optical coherence tomography (OCT), the finite bandwidth of the acquisition electronics constrains the depth range and speed of the system. Circular-ranging (CR) OCT methods use optical-domain compression to surpass this limit. However, the CR-OCT system architectures of prior reports were limited by poor stability and were confined to the 1.55 um wavelength range. In this work, we describe a novel CR-OCT architecture that is free from these limitations. To ensure stable operation, temperature sensitive optical modules within the system were replaced; the kilometer-length fiber spools used in the stretched-pulse mode-locked (SPML) laser was eliminated in favor of a single 10 meter, continuously chirped fiber Bragg grating, and the interferometer's passive optical quadrature demodulation circuit was replaced by an active technique using a lithium niobate phase modulator. For improved imaging penetration in biological tissues, the system operating wavelength was shifted to a center wavelength of 1.29 um by leveraging the wavelength flexibility intrinsic to CFBG-based dispersive fibers. These improvements were achieved while maintaining a broad (100 nm) optical bandwidth, a long 4 cm imaging range, and a high 7.6 MHz A-line rate. By enhancing stability, simplifying overall system design, and operating at 1.3 um, this CR-OCT architecture will allow a broader exploration of CR-OCT in both medical and non-medical applications., Comment: 11 pages, 8 figures

We observed that the polarization state of light after round-trip propagation through a birefringent medium frequently aligns with the employed input polarization state "mirrored" by the horizontal plane of the Poincare sphere. In this letter we explore the predisposition for this mirror state and demonstrate how it constrains the evolution of polarization states as a function of the round-trip depth into weakly scattering birefringent samples, as measured with polarization-sensitive optical coherence tomography (PS-OCT). The constraint enables measurements of depth-resolved sample birefringence with PS-OCT using only a single input polarization state, which offers a critical simplification compared to the use of multiple input states.

Fourier domain mode-locking (FDML) has been a popular laser design for high speed optical frequency domain imaging (OFDI) but achieving long coherence lengths, and therefore imaging range, has been challenging. The narrow instantaneous linewidth of a frequency comb (FC) FDML laser could provide an attractive platform for high speed as well as long range OFDI. Unfortunately, aliasing artifacts arising from signals beyond the principle measurement depth of the free spectral range have prohibited the use of a FC FDML for imaging so far. To make the enhanced coherence length of FC FDML laser available, methods to manage such artifacts are required. Recently, coherent circular ranging has been demonstrated that uses frequency combs for imaging in much reduced RF bandwidths. Here, we revisit circular ranging as a tool of making the long coherence length of an FDML frequency comb laser as well as its use for tissue imaging accessible. Using an acousto-optic frequency shifter (AOFS), we describe an active method to mitigate signal aliasing that is both stable and wavelength independent. We show that an FC FDML laser offers an order of magnitude improved coherence length compared to traditional FDML laser designs without requiring precise dispersion engineering. We discuss design parameters of a frequency stepping laser resonator as well as aliasing from a frequency comb and AOFS in OFDI with numerical simulations. The use of circular ranging additionally reduced acquisition bandwidths 15-fold compared with traditional OFDI methods. The FC FDML/AOFS design offers a convenient platform for long range and high speed imaging as well as exploring signal and image processing methods in circular ranging., Comment: 8 pages, 9 figures