System and method for improving optical coherence tomography (OCT) image resolution using K-linearization (KL) and dispersion correction (DC)

Abstract

Aspects of the present disclosure relate to systems, methods, and algorithms for performing wavenumber linearization and dispersion correction in optical systems that do not require hardware modifications, empirical tuning, precise mirror alignment, and can be performed in real time at low computational cost. A one-time calibration process can generate spectra or calibration standards, including wavenumber linearization standards, dispersion correction, and spectrally flattened spectra, that can be used to correct optical coherence tomograms in real time.

Description

[Technical field]

[0001] The present disclosure relates generally to the field of vasculature imaging and data collection systems and methods, and more particularly to a method for improving the resolution of images acquired in an optical coherence tomography system.

[0002] [CROSS REFERENCE TO RELATED APPLICATIONS] This application claims the benefit of the filing date of U.S. Provisional Patent Application No. 63 / 146,904, filed February 8, 2021, the disclosure of which is incorporated herein by reference. [Background technology]

[0003] Optical coherence tomography (OCT) is an imaging technique that uses light to capture cross-sectional images of tissues on a micron scale. OCT can be used both in situ and in systems external to the sample organism.

[0004] More specifically, the axial resolution of OCT images can be degraded with chromatic dispersion and the broadening of sidelobe artifacts due to nonlinear wavenumber sampling in Fourier domain optical coherence tomography systems. Many OCT systems sample the interference pattern at non-uniform wavenumber (k) intervals. This can introduce "chirp" or noise into the signal that can depend on the path length difference of the OCT signal.

[0005] Control of both nonlinear wavenumber sampling (also known as k-linearization) and chromatic dispersion through wavenumber linearization are important steps in maintaining resolution in OCT images.

[0006] Chromatic dispersion is inherent to optical systems and can be managed by careful control of the optical fibers and other components in the system. Wavenumber linearization poses a more difficult technical challenge and is performed by applying numerical methods such as numerical rescaling methods based on the fringe signals from mirrors in the OCT system, or by using an external sampling clock (k-clock) as used in the current OPTIS integrated system commercially available from Abbott Vascular. Although an external k-clock can attempt to linearize the nonlinear sampling interval of the wavenumber, the accuracy of wavenumber linearization is limited in the system due to improper adjustment of the k-clock delay and chromatic dispersion. Nonlinear sampling due to errors in the k-delay leads to reduced axial resolution and imaging artifacts such as broadened side lobes. Even with precise adjustment of the k-delay, slight system dispersion can directly reduce the resolution. In a dispersion-free system, the k-clock delay can be precisely adjusted by placing a mirror in the sample arm and minimizing the linewidth. However, in a dispersion-present system, the combination of k-linearization and chromatic dispersion results in a k-delay adjustment that can only achieve optimal resolution at certain depths. Furthermore, even if the swept source OCT engine is optically optimized, electronics such as bandpass filters and cables can introduce unexpected wavenumber delays into the external sampling clock.

[0007] An algorithm is proposed to digitally compensate for the inappropriate k-delay by calculating the “true” k-linearized spectrum, which needs to be separated from the system dispersion. The fringe data is then resampled by interpolating the “true” k-spectrum to linearize the sampling in k-space. The system dispersion is then compensated for by multiplying the interference signal with a counter-dispersive phase before the Fast Fourier transform (FFT). In general, calculating the system dispersion phase is an iterative process that optimizes the second and third order dispersion coefficients based on the sharpness of the OCT mirror signal. However, the dispersion order varies from one OCT system to another. The iterative process may have limited feasibility in commercial OCT systems because it requires a priori knowledge of the dispersion order.

[0008] Algorithms have been proposed to generate a "true" remapping table (wavenumber spectrum) to compensate for k-delay, but such algorithms have hardware requirements that require measurements of system dispersion, making the remapping table difficult or impractical to implement, especially in commercial products. Other methods proposed to compensate for k-delay bias and system dispersion are computationally intensive, require tens of minutes of processing time, and require precise alignment of the imaged mirror position, making them impossible to implement. Recently, an algorithm has been proposed to derive and compensate for the system dispersion after generating the remapping table in a system with dispersion. This method is simple and effective, and does not require any changes to the hardware setup, but the accuracy of the calculated remapping table is insufficient to be used in practical cardiovascular applications, as the reported useful imaging depth after correction is only 1 mm (in air). To achieve optimal imaging performance throughout the imaging depth, one of the main focuses of this disclosure is to propose a polynomial optimization process to improve the accuracy of the "true" remapping table, showing improvements in SNR and resolution at deeper depths. To date, the algorithms described above have been effective in compensating for k-nonlinearity and system dispersion in a controlled laboratory environment, but they suffer from a variety of challenges that prevent them from being feasible in commercial OCT systems. Summary of the Invention

[0009] In part, this disclosure relates to systems and methods that simultaneously correct for the problems of nonlinear sampling and chromatic dispersion without requiring additional hardware modifications, a priori knowledge of the dispersion order, precise mirror alignment, and computational costs associated with other previously proposed systems and methods.

[0010] In part, this disclosure relates to systems and methods for obtaining calibration data for an OCT system at various calibration depths, and a calibration process that optimizes the spectrum using wavenumber linearization or k-linearization (KL), dispersion correction (DC), and spectral flattening (SF).

[0011] Aspects of the disclosed technology include algorithms that compensate for k-delay bias and system dispersion without additional hardware and a priori knowledge of the system dispersion. Additionally, aspects of the disclosed technology do not require mirror positions to be precisely aligned to the same position by using the proposed phase shifting process.

[0012] Aspects of the disclosed technology allow for an increase in the useful imaging depth of spectral domain OCT and swept-wavelength OCT systems, compared to the 14% and 20% limits of currently published methods. Aspects of the disclosed technology include a polynomial optimization process by using three or more mirror sampling positions at different depths, with at least two depths in the positive imaging plane, to optimize the k-linearized spectrum and maintain improved resolution and SNR throughout depths of 80% or more of the Nyquist depth. As one example, the disclosed technology allows imaging depths of more than 6 mm with high accuracy, improving OCT image resolution and SNR in applications such as detecting calcium plaques around coronary vessels (requiring depths of up to 6 mm).

[0013] Aspects of the disclosed technology do not require oversampling or any upscaling and can be implemented in the order of seconds, enabling real-time product use. Moreover, they are computationally efficient, allowing the proposed algorithms to be implemented on live samples being imaged using OCT techniques.

[0014] Aspects of the disclosed technology eliminate the need for an external k-clock to compensate for nonlinear sampling in k-space. As one example, the proposed method improves the system signal-to-noise (SNR) ratio by 3 to 5 decibels (dB) by using a 1 GHz internal sampling rate of the analog-to-digital (A / D) card that is faster than the k-clock frequency. The faster internal sampling rate of the analog-to-digital card incoherently averages out the system noise, but the signals add coherently, resulting in more sampling points that can improve image contrast after Fourier transformation.

[0015] Aspects of the disclosed technology enable use within existing commercial intravascular OCT products through software or method modifications, improving axial resolution by compensating for k-delay bias, system dispersion and spectral flattening, providing extended high-resolution depth by using an optimized KLDC spectrum, and low computational cost.

[0016] Aspects of the disclosed technology allow for the elimination of the k-clock and the use of internal sampling provided by an analog-to-digital (A / D) card, which improves system SNR and imaging depth. This is especially ideal for future high speed OCT systems, which require faster sampling rates to reach signals from greater depths.

[0017] Aspects of the disclosed technology include a method of processing optical signals to improve the resolution of an optically acquired image. The method can include calibrating an optical system, including (i) acquiring, by one or more processors, at least a first mirror measurement and a second mirror measurement, the first mirror measurement and the second mirror measurement being collected from either side of a zero-delay line, each mirror measurement being an interferogram or a signal or system impulse response, (ii) acquiring, by the one or more processors, an amplitude and a phase of the first mirror measurement and the second mirror measurement, (iii) resampling at least one of the first mirror measurement and the second mirror measurement to be at least one of (a) linear wavenumber (k) space or (b) linear wavelength to generate a resampled mirror measurement, and (iv) calculating an optimized fit of the resampled mirror measurement using a function. In some examples, two mirror measurements are used, while in other examples, additional mirror measurements may be used. For example, the calibration can be based on three, four, tens, hundreds, or any number of mirror measurements.

[0018] Aspects of the disclosed technology may include any combination of the following, including a method, which may include calibrating an optical system, including (i) acquiring, by one or more processors, at least a first mirror measurement and a second mirror measurement, where the first mirror measurement and the second mirror measurement are collected from either side of a zero delay line, each mirror measurement being an interferogram or a signal or a system impulse response, (ii) acquiring, by the one or more processors, an amplitude and a phase of the first mirror measurement and the second mirror measurement, (iii) resampling at least one of the first mirror measurement and the second mirror measurement to be at least one of (a) linear wavenumber (k) space, or (b) linear wavelength to generate a resampled mirror measurement, and (iv) calculating an optimized fitting of the resampled mirror measurement using a function. The function used to resample the data may be one of a polynomial function, a cubic or other spline fitting function, a radial basis function, or a piece-wise function. K-linearization may be performed on the raw interferogram of the mirror measurements, with or without a k-clock, to generate a resampled interferogram, where the resampled mirror measurements are in k-space. Optimization of the K-linearization function may be based on a polynomial fitting order of order 1 to n, where n is a fixed integer. The polynomial fitting order that minimizes the sum of (i) the area of ​​the point spread function and / or (ii) the full-width half max (FWHM) resolution for each of the mirror measurements may be found as part of the method. The optimized polynomial may be determined based on two or more mirror measurements.A dispersion spectrum or reference may be calculated for at least one mirror measurement, and compensation may be calculated. Dispersion compensation, dispersion coefficients, or dispersion references may be calculated using two mirror positions, one on each side of the "zero delay line." Spectral flattening may be performed for at least one mirror measurement by using an envelope calculated from a particular mirror measurement. The spectral envelope calculated during spectral flattening may be stored. Each mirror measurement of the multiple mirror measurements may be a system impulse response. Calibration standards for (i) k-linearization (KL), (ii) dispersion compensation (DC), and (iii) spectral flattening (SF) may be stored. An OCT signal or interference signal from a specimen may be received. A linear k-interpolation may be generated to k-linearize the received OCT signal or interference signal by using the KL calibration standard, after which new fringes may be generated or the interferogram may be resampled. Dispersion compensation may be performed on the k-linearized OCT signal or interference signal using the DC calibration standard. The envelope of the linearized and dispersion corrected OCT signal or interference signal can be removed. The OCT signal or interference signal can be converted into an OCT image for display on a display.

[0019] Aspects of the disclosed technology include a method. The method can include generating an OCT image from an OCT signal, the method includes acquiring an OCT signal corresponding to an analyte, wavelength (k) linearizing the OCT signal to generate a k-linearized (KL) OCT signal, dispersion correcting (DC) the KL OCT signal to generate a KL DC OCT signal, spectrally flattening (sf) the KLDC OCT signal to generate a final KLDCsf OCT signal, and performing post-processing on the final OCT signal to generate an OCT image. The KL OCT signal, the KL DC OCT signal, and the final KLDCsf OCT signal are based on a calibration standard generated in a calibration stage. The OCT signal can be an interferogram. The calibration standard can be generated in a calibration stage using at least a first mirror measurement and a second mirror measurement, the first mirror measurement and the second mirror measurement being collected from both sides of a zero delay line, and each mirror measurement being an interferogram or a signal or a system impulse response.

[0020] Aspects of the disclosed technology include a system for displaying a set of images. The system may include a memory that stores image data and time-varying data corresponding to a subject, and one or more processors in communication with the memory. The one or more processors may be operable to: obtain a first mirror measurement and a second mirror measurement, the first mirror measurement and the second mirror measurement being collected from both sides of a zero delay line, each mirror measurement being an interferogram or a signal or a system impulse response; calculate, by the one or more processors, an amplitude and a phase of the first mirror measurement and the second mirror measurement; resample at least one of the first mirror measurement and the second mirror measurement to be at least one of (i) a linear wavenumber (k) space and (ii) a linear wavelength to generate a resampled mirror measurement; and calculate an optimized fitting of the resampled mirror measurement using a function. The system may be configured to perform the steps of any of the methods described above.

[0021] Aspects of the disclosed technology include a system for performing optical coherence tomography on a sample. The system may include a light source, a reference mirror with a variable position, a display, a memory that stores image data corresponding to the sample, and one or more processors in communication with the memory. The one or more processors may be operable to acquire at least one calibration parameter each time the system is activated, acquire a signal from the sample, apply the at least one calibration parameter to the acquired signal, the calibration parameter being at least one of a wavelength linearization, a dispersion correction, or a spectral flattening parameter, and generate an image from the corrected acquired signal. The system may further include an optical switch and a calibration mirror configured to generate the calibration signal. The system may be configured to perform the steps of any of the methods described above.

[0022] It should be understood that the different aspects and embodiments disclosed herein can be integrated together in whole or in part, as needed. Thus, each example disclosed herein can be incorporated into each of the aspects to various degrees as needed for a given implementation. Furthermore, various software-based tools for addressing medical imaging problems and other related issues and problems and some of the above can be used for, without limitation, medical applications and other applications for displaying stent and vessel related information and two-dimensional and three-dimensional views thereof. Other features and advantages of the disclosed examples will become apparent from the following description and accompanying drawings.

[0023] Although the present disclosure relates to different aspects and examples and other features listed and illustrated herein, it is understood that each of the above disclosed herein can be integrated together in whole or in part, as appropriate. Thus, each of the examples disclosed herein can be incorporated into each of the aspects to various degrees, as appropriate for a given implementation. Furthermore, the various methods and techniques described herein can be used with various imaging modalities.

[0024] Other features and advantages of the disclosed examples will become apparent from the following description and the accompanying drawings. [Brief description of the drawings]

[0025] [Figure 1A] FIG. 1 is a schematic diagram of an imaging and data collection system according to an aspect of the present disclosure. [Figure 1B] 1 illustrates an exemplary positioning of an example reference mirror according to aspects of the present disclosure. [Figure 1C] FIG. 1 is a schematic diagram of an imaging and data collection system according to an aspect of the present disclosure. [Figure 1D] FIG. 2 is a block diagram of an exemplary calibration mirror, according to an aspect of the present disclosure. [Diagram 2] FIG. 1 illustrates an exemplary computing device for imaging and data collection, according to aspects of the present disclosure. [Diagram 3] FIG. 1 is a flow diagram of a method according to an aspect of the present disclosure. [Figure 4A] 1A-1C show aspects of acquired interferograms according to aspects of the present disclosure. [Figure 4B] FIG. 4B illustrates the nonlinear effect of system dispersion based on the curves of the k spectrum calibrated by the KL process and the ideal linearized k spectrum. FIG. 4B also illustrates the effective correction to the uncalibrated k spectrum by applying the KL process. The dashed line is the calculated optical phase after the k linearization process. [Figure 4C]FIG. 13 illustrates intensity behavior at various imaging depths with and without k-spectral optimization, according to an embodiment of the present disclosure. [Figure 4D] FIG. 13 illustrates aspects of raw dispersion phase and polynomial fit of dispersion phase, according to aspects of the present disclosure. [Figure 4E] 1 illustrates KLDC corrected fringes and an envelope calculated for the corrected fringes, according to an embodiment of the present disclosure. [Diagram 5] FIG. 1 is a flow diagram of a method according to an aspect of the present disclosure. [Figure 6A] FIG. 2 illustrates a raw optical signal and a k-linearized optical signal according to an embodiment of the present disclosure. [Figure 6B] FIG. 13 is a close-up view of an interferogram and corresponding intensity resulting from a mirror reflection in the sample arm, according to an aspect of the present disclosure. [Figure 6C] FIG. 1 illustrates raw fringes as well as before and after spectral flattening in accordance with an aspect of the present disclosure. [Figure 6D] 13A-13C illustrate various aspects of full width at half maximum (FWHM) plotted against various depths for k-clock data, KL data, KLDC data, and KLDCsf data, in accordance with aspects of the present disclosure. [Figure 7] FIG. 1 illustrates a method for acquiring an intensity image according to aspects of the present disclosure. [Figure 8] FIG. 1 illustrates an example of an improvement in point spread function (PSF) width and resolution chart according to an embodiment of the present disclosure. [Figure 9] 13A-13C illustrate aspects of PSFs with strong variance at various imaging depths with and without KLDCsf technique, in accordance with aspects of the present disclosure. [Figure 10] 13A-13C illustrate aspects of k-delay tuning and FWHM and signal-to-noise ratio without strong dispersion using k-clock data, KLDCsf data, and KLDCsf data with optimized k-spectrum, according to aspects of the present disclosure. [Figure 11]FIG. 1 illustrates aspects of a-line intensity using KLDCsf with and without optimized k-spectrum, according to aspects of the present disclosure. An "A-line" or "axial line" corresponds to one wavelength sweep of the laser, which corresponds to a one-dimensional line scan. [Figure 12] 1A-1C illustrate aspects of data sampled linearly in time (wavelength) without a k-clock and processed using the KLDCsf method, according to aspects of the present disclosure. [Figure 13] 13 illustrates an embodiment of FWHM using data resampled using k-clock and A / D card internal sampling with KLDCsf technique at various imaging depths according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] In part, the disclosure relates to a calibration process that can calculate k-linearized (KL), variance, and spectral flattening spectra. The calibration process can provide a set of calibration spectra. With a mirror in the sample arm, OCT signals including raw fringe data can be acquired separately from the positive and negative planes by adjusting the reference arm path distance. The calibration spectra can be calculated based on the phase retrieved by using a Hilbert transform process.

[0027] In part, this disclosure relates to utilizing an acquired calibration spectrum to correct or correct an interferogram prior to Fast Fourier Transform (FFT) processing. The calibration spectrum can be used to linearize future fringe data acquired by the system in the wavenumber domain for an improved interferogram. In some examples, the wavenumber sampling can be linearized by interpolation using the calibration spectrum (k-linearization). Variance can be corrected by applying a Hilbert transform and multiplying with the variance spectrum of the k-linearized interferogram.

[0028] In part, the present disclosure may further relate to suppressing side lobe artifacts due to non-uniform laser intensity during a spectral sweep by flattening the spectral envelope of the laser source to optimize the laser bandwidth.

[0029] In part, aspects of the present disclosure provide systems and methods that separately correct for k-spectral nonlinearity as well as dispersion and asymmetric laser sweep intensity by using a spectrum calibrated with a signal processing step. The present disclosure allows optimized optical resolution to be maintained throughout the imaging depth. Furthermore, the algorithms, methods, and systems described in the present disclosure can also function with or without a k-clock, which provides the option to improve the system SNR by using an internal sampling rate of the digitizer that is typically faster than the maximum frequency of the k-clock.

[0030] In some aspects of the present disclosure, the OCT imaging engine includes an internal reference reflector or calibration mirror that automatically receives information corresponding to the quality of the OCT imaging engine. The reference reflector may be internal to the sample arm of the OCT imaging engine and may be used to receive optical signals that perform various diagnostic processes to quantify the performance of the OCT imaging engine. For example, the OCT imaging engine may self-assess its performance by calculating system performance data that may include one or more of the point spread function (PSF), full width at half maximum (FWHM), noise level, signal to noise ratio, and dynamic range of the system. The reference reflector may be coupled to the rest of the OCT imaging engine through an optical switch, allowing the OCT imaging engine to switch between imaging mode and self-test mode. The OCT imaging engine may use the reference reflector internal to the system to sample the optical signal to generate a calibration spectrum. Because the reference reflector is internal, the OCT imaging engine can automatically perform self-testing and calibration without requiring user input or an external device to be first connected to the system.

[0031] As used in this disclosure and understood by those skilled in the art, a system impulse response or impulse response is a response to a short input signal that is an impulse. In an optical system, the system impulse response can be a response obtained from an impulse of light on a sample. In some examples, the system impulse response can be a reflection obtained from a single mirror in the sample arm (as described below). In some examples, the system impulse response is obtained from a wavelength swept laser.

[0032] As used in this disclosure, an interferogram or interference pattern may be a pattern formed by wave interference, such as by interference of light waves from a reference arm and a sample arm of an OCT system. An interferogram may also be considered as a time varying signal that can be converted using an analog-to-digital converter.

[0033] (Example System) OCT is a catheter based imaging modality that uses light to peer into the coronary artery walls and generate images for study. Utilizing coherent light, interferometry, and micro-optics, OCT can provide video-rate in vivo tomography within diseased blood vessels with micrometer-level resolution. Using a fiber optic probe to see subsurface structures at high resolution makes OCT particularly useful for minimally invasive imaging of internal tissues and organs. This level of detail possible with OCT allows users to diagnose and monitor the progression of coronary artery disease.

[0034] OCT imaging of parts of a patient's body provides a useful diagnostic tool for physicians and others. For example, imaging of coronary arteries with intravascular OCT can reveal the location of narrowing or stenosis. This information helps cardiologists choose between invasive coronary artery bypass surgery and less invasive catheter-based procedures such as angioplasty or stent delivery. Although a popular option, stent delivery has its own associated risks.

[0035] FIG 1A illustrates an exemplary OCT system 100 according to aspects of the disclosure. In some examples, the OCT system 100 is or incorporates an optical interferometer. One skilled in the art will appreciate that while one configuration of an OCT system is shown in FIG 1, variations of the system and different implementations, such as an in vivo OCT system, are within the scope of the disclosure. The direction of the light or electrical signals is indicated by the arrows in FIG 1.

[0036] The light source 110 can be a source of low coherence light capable of capturing sub-micrometer level resolution. The light source can generate light in and beyond the visible wavelength range. In some instances, an ultra-broad output of wavelengths of light is desirable. In some instances, a laser can be used as the light source. In yet other instances, a light emitting diode can be used as the light source. In some instances, the light generated by the light source 110 can be sent through a collimating lens.

[0037] Light from the light source 110 can be sent to a beam splitter 120. The beam splitter 120 can be an optical device that splits the light beam from the light source 110 into two or more beams. The light split by the beam splitter can travel to a reference mirror 130 and to a sample 140. An embodiment of the reference mirror 130 is further described with respect to FIG. 1B. The sample 140 can be an organic or other sample on which OCT can be performed. In some examples, the sample 140 can be examined internally, as in an in vivo OCT scan. Light can be reflected from both the reference mirror 130 and the sample 140 across the beam splitter and through an optical path that directs the light to a photodiode 150. The photodiode 150 can be a semiconductor or other device that converts light into an electrical current and allows for the detection of light. When photons or waves of light are incident on the photodiode, a current or another electrical signal is generated in the photodiode. The photodiode 150 may include multiple optical filters, lenses, or other components to focus the light and increase the signal-to-noise ratio. The signal generated in the photodiode 150 may be converted from analog to digital and processed by the digital signal processor 160. The digital signal processor may be a dedicated microprocessor or integrated chip with architecture and / or software optimized for the operational needs of digital signal processing. In some examples, the digital signal processor 160 may enable the information generated in the photodiode 150 to be processed and sampled by the k-clock signal at 191 and converted into an image for display on the display 170. As described further below, the digital signal processor may perform one or more of the steps described below by optimizing the image generated from studying or observing the sample 140. The display 170 may display an image related to the sample 140. As a non-limiting example, the display 170 may be a monitor, an OLED screen, an LCD screen, a television, an electroluminescent display, or a quantum dot display.Other specialized screens or displays that provide specialized contrast ratios or facilitate the display of OCT information can also be used as display 170 .

[0038] The k-clock 190 is also shown in FIG. 1A. If the arrival time of the signal from the k-clock output to the A / D card is not synchronized with the signal from the main interferometer to the A / D card, the interferogram cannot be sampled accurately. Synchronization problems can be caused by mismatches between the length of the optical fiber and the electronic connections after the photodiode 191 and the photodiode 150, such as the length of a bandpass filter or electrical cable. In some instances, only a few percent, e.g., 2%-3%, of the light may be transmitted to the k-clock. The k-clock 190 can consist of any combination of 90 degree phase shifters, zero-crossing detection units, XOR or OR gates, or similar elements.

[0039] The beam splitter 180 can also split the light to the k-clock 190 and the beam splitter 120. In some examples, a small amount of light can be transmitted to the k-clock through the beam splitter. A photodiode 191, similar to the photodiode 150, can be connected to the k-clock and can analyze the light incident on the photodiode 191. The photodiode 191 can be connected to a digital signal processor 160. Because a portion of the light is transmitted through the k-clock, it can be analyzed separately from the light incident on or obtained from the sample 140.

[0040] Those skilled in the art will appreciate that although various optical components are referenced in FIG. 1, equivalent components can be used or substituted with the system 100 described above. In some examples, optical fibers and related optical components such as optical couplers can be used in place of or in conjunction with the components described herein. For example, those skilled in the art will appreciate that rather than using collimation lenses and beam splitters, optical couplers can be used to achieve the same or equivalent setup as described with respect to FIG. 1A. The light can take a path determined by the optical fiber wires. The use of optical fibers and optical couplers can provide a more robust and simpler optical setup for use in commercial OCT applications. As one example, the beam splitter 180 and the beam splitter 120 can consist of optical fiber couplers that split one input fiber into two output fibers and split the input light into two paths.

[0041] In some examples, the various components may be linked, controlled, and communicated through a suitable computing system, such as computing system 200, further described below with reference to FIG.

[0042] FIG. 1B illustrates an embodiment of a reference mirror 130. The reference mirror 130 may be a mirror or other reflective surface with high reflectivity and optical properties that allow for the reflection of photons. The reference mirror 130 may be included in a reference arm of the OCT system 100. By scanning the mirror in the reference arm with light from the sample 140, an interference pattern may be generated from which an OCT image may be reconstructed. As shown in FIG. 1B, once the mirror is placed on the sample 140, the reference mirror 130 may be moved to various positions on either side of a "zero delay line." Various positions, such as positions P1, P2, and P3, may move the image of the mirror to different depth pixels. Position -P1 and position P1 may or may not be equidistant to the zero delay line. Although only -P1 and P1-P3 are shown for simplicity, there may be a finite number of positions in any order. For example, there may be an additional position P4 that is at a greater positive pixel depth than position P3. In one example, the pixel depth can range from +1024 pixels to -1024 pixels. The pixel depth can depend on the k-clock total sample or internal sampling rate of the A / D card and half the total number of pixels used before the FFT according to the zero-padded data length before the FFT. The imaging depth from pixel depth 0 to pixel depth 1024 is expressed as {(center wavelength) 2 The optical density is determined by the formula { / (2*laser bandwidth)}*(0.5*total number of sample points from an OCT fringe).

[0043] In some examples, the position of the reference mirror 130 can change the interference pattern generated from the light returning from the sample 140. In other examples, the position of the reference mirror 130 can be used to generate information related to the performance of the optical system, and can be used in the methods described herein. In some examples, the information generated from positions P1 and P2 can be used to calculate a spectral wavelength linearization and a spectral flattening spectrum, as further described with reference to FIG.

[0044] FIG. 1C may show an OCT system with a mirror integrated into the sample arm (system 199). FIG. 1C shows that system 199 may be similar to system 100, but may further include an additional calibration mirror 193 and an optical switch 192. The optical switch 192 may be optically coupled between the beam splitter 120 on one end and the sample 140 and calibration mirror 193 on the other end. The signal from the calibration mirror 193, together with the variable position reference mirror 130, allows the system to perform the KLDCsf method in an automated or semi-automated process. In some examples, a "self-diagnosis" procedure may be performed each time the system is started or each time a new optical joint is included or changed in any part of the system.

[0045] FIG. 1D is a block diagram of the calibration mirror 193 of FIG. 1C according to some examples. The calibration mirror 193 may include an optical connector 205C that connects the calibration mirror 193 to the switch 192. Alternatively, the calibration mirror 193 may be fusion spliced ​​to connect to the switch 192. The calibration mirror 193 may include an attenuator 210C. The attenuator 210C may have a predetermined attenuation, for example, to attenuate the reflected signal so that the magnitude of the reflection is similar to the reflection from the sample 140. The calibration mirror 193 may include a flat surface 215C that provides a single point reflection. The length of the optical fiber may have the same optical path length as the sample 140 so that the light reflected from 215C interferes with the reference arm. The single point reflection may be referred to as a point spread function (PSF) and may be processed by the DSP 160 or other components of the OCT imaging engine to obtain information that quantifies the OCT imaging engine.

[0046] According to some examples, the OCT imaging engine receives mirror measurements using a reference mirror and a calibration mirror. The mirror measurements may include a time-varying amplitude of an interferogram. The received measurements may be used by the system to derive the phase of the optical signal as a function of the sampling index.

[0047] FIG. 2 illustrates an exemplary computing system 200. The computing system 200 may include hardware, software, and other modules as further described herein. The following description is intended to provide an overview of device hardware and other operating components suitable for implementing the methods of the present disclosure described herein that may be part of the computing system 200. This description is not intended to limit the applicable environment or the scope of the present disclosure. Similarly, the hardware and other operating components may be suitable as part of the apparatus described above. The present disclosure may be implemented in other system configurations, including personal computers, multiprocessor systems, microprocessor-based or programmable electronic devices, network PCs, minicomputers, mainframe computers, and the like. The present disclosure may also be practiced in distributed computing environments, where tasks are performed by remote processing devices linked through a communications network, such as different rooms in an OCT or catheterization laboratory.

[0048] Some portions of the detailed descriptions are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations may be used by those skilled in the art of computers and software. In one example, an algorithm is herein generally conceived to be a self-consistent sequence of operations leading to a desired result. The operations performed as method steps or otherwise described herein are operations requiring physical manipulation of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, transformed, compared, and otherwise manipulated.

[0049] Unless otherwise specified, and as will be apparent from the discussion that follows, it will be appreciated that throughout the description, discussions utilizing terms such as "processing" or "computing," "overlaying," or "searching" or "detecting" or "measuring" or "calculating" or "comparing," "generating" or "determining" or "displaying," or other sets of terms relating to Boolean logic or operations, refer to actions and processes of a computer system or electronic device that manipulate and convert data represented as physical (electronic) quantities in registers and memories of the computer system or electronic device into other data that is similarly represented as physical quantities in electronic memory or registers or other such information storage, transmission, or display devices.

[0050] The disclosure also relates in some instances to apparatus for performing the operations herein, which may be specially constructed for the required purposes, or which may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer.

[0051] The exemplary system of the present disclosure may be embodied in many different forms, including but in no way limited to computer program logic for use with a processor (e.g., a microprocessor, microcontroller, digital signal processor, or general purpose computer), programmable logic for use with a programmable logic device (e.g., a field programmable gate array (FPGA) or other programmable logic device), discrete components, integrated circuits (e.g., application specific integrated circuits (ASICs)), or any other means including any combination thereof. In one example, some or all of the processing of data collected using an OCT probe and processor-based system is implemented as a set of computer program instructions, which are converted into a computer-executable form and themselves stored on a computer-readable medium and executed by a microprocessor under the control of an operating system. Thus, the query responses and input data are converted into processor understandable instructions suitable for generating imaging data, detecting lumen boundaries, detecting stent struts, comparing measured perpendicular distances to set thresholds, image comparison, signal processing, lumen detection, stent detection, and comparison of detected stents, as well as otherwise implementing other features and examples described above.

[0052] Computer program logic implementing all or part of the functionality described herein above may be embodied in a variety of forms, including but in no way limited to source code form, computer executable form, and various intermediate forms (e.g., forms generated by an assembler, compiler, linker, or locator). Source code may include a series of computer program instructions implemented in any of a variety of programming languages ​​(e.g., object code, assembly language, or high-level languages ​​such as Fortran, C, C++, JAVA, or HTML) for use with a variety of operating systems or operating environments. Source code may define and use various data structures and communication messages. Source code may be in a computer executable form (e.g., via an interpreter) or source code may be converted into a computer executable form (e.g., via a translator, assembler, or compiler).

[0053] The computer program may be fixed in any form (e.g., source code form, computer executable form, or intermediate form) permanently or temporarily on a tangible storage medium such as a semiconductor memory device (e.g., RAM, ROM, PROM, EEPROM, or flash programmable RAM), a magnetic memory device (e.g., diskette or fixed disk), an optical memory device (e.g., CD-ROM), a PC card (e.g., PCMCIA card), or other memory device. The computer program may be fixed in any form on a signal that is transmittable to a computer using any of a variety of communication technologies, including, but in no way limited to, analog, digital, optical, wireless (e.g., Bluetooth), networking, and internetworking technologies. The computer program may be distributed in any form on a removable storage medium (e.g., shrink wrapped software) with accompanying printed or electronic documentation, preloaded on a computer system (e.g., system ROM or fixed disk), or distributed from a server or electronic bulletin board over a communication system (e.g., the Internet or World Wide Web).

[0054] Hardware logic (including programmable logic used in conjunction with a programmable logic device) implementing all or a portion of the functionality described herein above may be designed using conventional manual methods, or may be electronically designed, captured, simulated, or documented using a variety of tools, such as computer-aided design (CAD), hardware description languages ​​(e.g., VHDL or AHDL), or PLD programming languages ​​(e.g., PALASM, ABEL, or CUPL).

[0055] The programmable logic may be fixed, either permanently or temporarily, in a tangible storage medium, such as a semiconductor memory device (e.g., RAM, ROM, PROM, EEPROM, or flash programmable RAM), a magnetic memory device (e.g., a diskette or fixed disk), an optical memory device (e.g., a CD-ROM), or other memory device. The programmable logic may be fixed in signals that can be transmitted to a computer using any of a variety of communication technologies, including, but in no way limited to, analog, digital, optical, wireless (e.g., Bluetooth), networking, and internetworking technologies. The programmable logic may be distributed as a removable storage medium (e.g., off-the-shelf software) with accompanying printed or electronic documentation, may be preloaded into a computer system (e.g., a system ROM or fixed disk), or may be distributed over a communication system (e.g., the Internet or World Wide Web) from a server or electronic bulletin board.

[0056] Various examples of suitable processing modules are discussed in more detail below. As used herein, a module refers to software, hardware, or firmware suitable for performing a particular data processing or data transmission task. In some examples, a module refers to instructions or software routines, programs, or other memory resident applications suitable for receiving, converting, routing, and processing various types of data, such as OCT scan data, and other information of interest.

[0057] The computers and computer systems described herein may include operatively associated computer-readable media, such as memory for storing software applications used in acquiring, processing, storing, and / or communicating data. It may be appreciated that such memory may be internal, external, remote, or local with respect to the computer or computer system with which it is operatively associated.

[0058] Memory may also include any means for storing software or other instructions, including, for example, without limitation, a hard disk, an optical disk, a floppy disk, a DVD (digital versatile disk), a CD (compact disk), a memory stick, flash memory, a ROM (read only memory), a RAM (random access memory), a DRAM (dynamic random access memory), a PROM (programmable ROM), an EEPROM (extended erasable programmable read only memory), and / or other similar computer readable medium.

[0059] Generally, the computer-readable memory medium applied in connection with the examples of the present disclosure described herein can include any memory medium capable of storing instructions executed by a programmable device. Where applicable, the method steps described herein can be embodied or executed as instructions stored on one or more computer-readable memory media. These instructions can be software embodied in various programming languages ​​such as C++, C, Java, and / or various other types of software programming languages ​​that can be applied to create instructions according to the examples of the present disclosure.

[0060] A storage medium may be non-transitory or may include a non-transitory device. Thus, a non-transitory storage medium or a non-transitory device may include a device that is tangible, meaning that the device has a concrete physical form, although it may change its physical state. Thus, for example, non-transitory refers to the device remaining tangible even with this change in state.

[0061] (Method of Example) 3 illustrates a method 300 for obtaining one or more calibration spectra. The calibration spectra can be used to calibrate an OCT signal derived from a sample, such as sample 140, to improve resolution, signal-to-noise ratio, and suppress image side-lobe artifacts.

[0062] The following notations may be used as used with reference to the method 300. The following equations may be derived and understood with reference to the notations. Those skilled in the art will understand that equivalent and similar steps, symbols, and notations may be used. k: wave number. j: The imaginary unit in the complex domain. KL: k-linearization. DC: Dispersion correction. SF: Spectral flattening. n: Sampling index according to the A / D card used. I: Strength I P1 (n): Intensity at position P1, where n is the sampling index variable by the A / D card. z x : Depth position x relative to the zero retardation plane. Px: imaging position x on the normal plane, for example x=1, 2, 3... -Px: imaging position x on the negative plane. Φ x (n): Optical phase acquired at the x position on the positive plane. Φ -x (n): Optical phase obtained at the x position on the negative plane. Φ disp :Optical dispersion phase. Φ KL (n): Dispersion-free k spectrum after k linearization process. X d (n): Phase compensation term used to compensate for asymmetry about the zero delay line. z d : PSF peak position after FFT, d is Px and −Px. s0: kCorrected fringes after linearization. s1: corrected fringes after KL and DC processes. s2: Corrected fringes after KL, DC and spectral flattening processes. s3: Corrected fringes after KL, DC and spectral flattening processes, without using analytical form to reduce computational cost.

[0063] In block 305, mirror measurements can be acquired. Mirror measurements are signals generated from the OCT system 100 when the mirror is placed on the sample 140. The resulting measurements are time-varying amplitudes from the interferogram acquired by the A / D card. In this block, OCT signals, which may include raw fringe data, can be acquired from a mirror or reference reflector in the sample arm of the OCT system, separately from both the negative and positive imaging planes. A background spectrum can also be acquired by removing the sample. In some examples, raw fringes on both sides of the mirror arm can be acquired at a pixel depth of 250 pixels to 300 pixels, which corresponds to 25% to 30% of the Nyquist depth in a 1024 pixel system. In other examples, any two arbitrary positions can be selected for the pixel depth.

[0064] In some examples, measurements can be taken at positions P1 to P3, as shown in FIG. 1B. Information is derived from these measurements, as will be further described. In some examples, measurements from positions P1 and −P1 are taken at sampling index n, Φ P1 (n) and Φ -P1 The phase as a function of (n) may be used to extract the phase as a function of (n) and, accordingly, used to calculate the spectrum KL and the variance spectrum. The fringes acquired at P1 may be used to estimate the spectrally flattened spectrum. In other examples, the measurements taken at P1-P3 may be used to optimize the k spectrum based on the best polynomial fitting order found from the algorithm. In some examples, the locations of P1, P2, and P3 may be selected based on the desired or estimated depth of the sample to optimize the polynomial fitting over the entire depth range.

[0065] Aspects of the acquired signal or signals are shown with reference to Figure 4A. In some examples, the complex signal and the phase of that signal can be calculated by a Hilbert transform from measurements acquired at positions P1 to P3.

[0066] The k-linear phase also averages the fringes to obtain the clean optical phase Φ from positions P1 and P2. P1 (n) and Φ -P1 (n). Using the interferograms P1 to P3, the interpolation performance of the KL spectrum is optimized, and the influence of noise from deeper depths in the OCT signal is reduced or avoided.

[0067] In other words, I P1 (n) ∝ cos{k(n)z P1 +Φ disp (n)} and I -P1 (n) ∝ cos{k(n)z -P1 -Φ disp (n)}, the instantaneous optical phase Φ P1 (n) and Φ -P1 (n) is calculated by the Hilbert transform, P1 (n)=k(n)z P1 +Φ disp (n) and Φ -P1 (n)=k(n)z -P1 -Φ disp (n) can be obtained. In this block, the background can be removed from the signal obtained in block 305.

[0068] In block 310, a dispersion-free k-spectrum (k-spectrum) Φ KL (n) is Φ KL (n)=0.5*{Φ P1 (n)+Φ -P1The KL-corrected fringes s0(n) can be calculated based on the relationship between s0(n) and s0(n). The aspects of this block are further explained with reference to FIG. 4B(1), where the solid line represents the dispersion-free k spectrum and the dashed line represents the ideal linearized k spectrum. The difference between the curves represents the nonlinearity of the k clock, which needs to be corrected in the next section. Using both curves, the raw fringes on the wavenumber phase domain are directly interpolated to obtain the KL-corrected fringes s0(n). In addition, to test the algorithm with an uncalibrated OPTIS system, instead of using the k clock, the OCT signal was acquired at the 500 MHz internal sampling rate of the A / D card. FIG. 4B(2) shows the effective calibration to correct the nonlinear k spectrum to a linearized k spectrum after the KL process. Thus, the calibrated result is shown in FIG. 12. Conventional techniques perform the interpolation in the time domain, but performing the interpolation in the time domain requires additional polynomial fitting to project the k domain phase back to the time domain (N=1 to 2048), and the accuracy is highly sensitive to the polynomial fitting order. To save computational cost from additional polynomial fitting that remaps the interpolation from the k-domain to the time domain, an embodiment of the method can be selected to perform the k-spectral interpolation directly in the wavenumber domain.

[0069] In some examples, referring to FIG. 4C, the system noise across the fringes and the increased sampling error close to the Nyquist depth can easily cause imaging side lobe artifacts and further reduce the system resolution at deeper depths (i.e., the deeper depth of the signal has fewer sampling points per fringe cycle). Therefore, the dispersion-free k spectrum needs to be optimized by a polynomial fitting function, and the polynomial fitting order can be optimized. The result is shown in FIG. 4C with the enlarged window diagrams 455 and 465, which shows an improved PSF without side lobe artifacts.

[0070] In block 315, any or all of the following steps may be used as part of a polynomial optimization process to determine the best polynomial order for the KL spectrum. ·KL spectrum Φ KL (n) can be obtained using polynomial fitting either at P1 or at the other mirror (P2, P3, etc.) positions. For signals acquired at other locations such as P2 and P3, KL resampling with various polynomial fitting orders can be applied. In some examples, polynomials of orders 0 to 50 can be used, with 0 representing the raw k spectrum with no fitting applied. Based on the PSF profile after k-spectral interpolation by each fitting order, the full width at half maximum (FWHM) (referred to as a1 in this block) and the point spread function (PSF) total area (referred to as a2 in this block) are calculated. The a1 and a2 parameters can be calculated at any polynomial order for each depth location P1, P2, and P3. The parameters a1 and a2 are then averaged along depth and then normalized to 0 to 1 separately. ·The polynomial degree with the smallest (a1+a2) can be found. The polynomial order that gives rise to the sharpest intensity peak in the preceding step can be saved as the optimized fitting order of the saved k-spectrum and used throughout the KL process at all other depths and for all other OCT signals in the real-time imaging stage. See Fig. 4C and the bottom row of Fig. 11 for an exemplary improvement of imaging artifacts.

[0071] In block 320, the dispersion spectrum can be calculated. First, the raw fringes are interpolated using the k spectra from block 310. The interferograms with the mirrors at positions P1 and -P1 are calculated according to the following relationship I P1 (n) ∝ cos{k(n)z P1 +Φ disp (n)} and I -P1 (n) ∝ cos{k(n)z-P1 -Φ disp If the mirror positions are not equidistant to the zero delay line, the dispersion phase Φ disp can be achieved as follows: Φ disp (n)=0.5*{(Φ P1 (n)+n*X p1 )-(Φ -P1 (n)+n*X (-P1) )}, and X d =-z d +0.5*{z (P1) +z (-P1)}

[0072] For each calibration pair of mirrors, the term X d is used to compensate for the phase difference of the calibration mirrors due to their unequal positions relative to the zero delay line. disp (n) can be removed by fitting a polynomial function, as shown in FIG. 4D, or can be simply filtered by a low-pass filter. disp The (k) function can be stored for use as the inverse dispersion phase subtracted using the analytical form of the KL corrected fringe s0(n). See FIG. 6B for example results of the fringe data and OCT intensity shape after applying the dispersion correction.

[0073] In block 325, a spectrally flattened (SF) spectrum is calculated. SF(n) can be calculated by the envelope value of the KLDC corrected fringe s1(n). In some examples, the spectral flattening can further increase the axial resolution performance by increasing the available bandwidth, as shown in FIG. 6C. In one example, the SF window can be calculated as a calibration step and applied to future data using a window function (i.e., Kaiser-Bessel window) to form a demodulation window, e.g., s2(n)=s1(n)×(kaiser window) / SF(n). In one example, the envelope used can be derived from a signal acquired from any of the reference mirror positions from block 305.

[0074] In one example, the first step in the spectral flattening process is to calculate the fringe envelope by taking the absolute number of KLDC corrected fringes after the Hilbert transform has been applied. This step can occur when the mirror is placed in the sample arm. Once the spectral envelope is calculated, it can be fitted by a polynomial function (see FIG. 4E for an example).

[0075] In block 330, the various spectra calculated in steps 310 through 325 may be saved. In this block, the various spectra may be stored or moved to various portions of the computing device 200.

[0076] 4A illustrates various aspects of the acquired input signal. Graph 410 illustrates an input signal acquired from a positive imaging plane. Graph 420 illustrates an input signal acquired from a negative imaging plane. Graphs 410 and 420 can also be described as an example of a k-clock sampled interferogram (raw fringes) from an OCT system. While graphs 410 and 420 represent empirically acquired data, the intensity of the fringe signals in the graphs can be described in terms of wavenumber "k" and variance, according to the relationship I P1 (n) ∝ cos{k(n)z P1 +Φ disp (n)} and I -P1 (n) ∝ cos{k(n)z -P1 -Φ disp Dispersion can be measured in radians or any other angular scale and represents the optical phase that determines the amplitude modulation of a sinusoidal signal.

[0077] Referring to FIG. 4B, a graph 430 (FIG. 4B(1)) and a graph 440 (FIG. 4B(2)) are shown. FIG. 4B(1) is a graph 430 showing a dispersion-free k spectrum compared to an ideal linearized k spectrum. FIG. 4B(2) shows a graph 440 showing the effective correction to the uncalibrated k spectrum after the KL process. In FIG. 4B, the dashed line may be the calculated optical phase after the k-linearization process.

[0078] 4C shows graphs 450 and 460. Graph 450 shows the intensity without k-spectral optimization. Graph 455 highlights one peak indicating uneven side lobes. Graph 460 shows the same signal with k-spectral optimization. Graph 465 highlights the same peak as 455 indicating the side lobes are suppressed, and the graphs are symmetrical after k-spectral optimization.

[0079] Figure 4D shows the polynomial fitting Φ disp (n) shows a graph 470 showing a graph of the stored raw variance data.

[0080] 4E shows a graph 480 illustrating example data of a KLDC corrected fringe and its envelope calculated using polynomial fitting, which can be saved for later use.

[0081] 5 illustrates a method 500 for performing real-time OCT imaging performed by a graphics processing unit (GPU) or a field programmable gate array (FPGA) according to an embodiment of the present disclosure. Method 500 may also be performed in part or in whole by other computing devices, such as device 200. Method 500 may be performed in real time during operation of the OCT system using data acquired above.

[0082] In block 505, one or more spectra can be loaded or acquired. For example, one or more spectra calculated with reference to method 300, such as the k-spectrum, the dispersion spectrum, and the spectrally flattened spectrum, can be loaded from memory. In this block, the background data for each fringe must be removed by a high pass filter or the data must be acquired by blocking the sample arm.

[0083] In block 510, k-linearization (KL) correction can be performed on the collected raw fringe data. In this block, the fringe data can be interpolated by a stored k-spectrum with an optimized polynomial fitting order. In some examples, block 510 can use the k-spectrum data stored with reference to method 300 and as shown with reference to FIG. 4B. In this step, the loaded k-spectrum can be used to correct the acquired raw fringe data by performing a cubic spline interpolation in wavenumber space (k-space) on the collected raw fringe data. Performing a cubic spline interpolation in wavenumber space allows the raw fringes to be scaled from a nonlinear wavenumber domain to a linear wavenumber domain before performing the FFT process. Spline interpolation is a form of piecewise polynomial interpolation that avoids the problem of over-fitting that causes spike errors. In some examples, an FPGA or GPU can be used to efficiently perform the cubic spline interpolation in the wavenumber domain, allowing the KL correction to be performed in real time, such as during operation of the OCT system. The raw fringe data and the KLDC corrected fringe data are shown with respect to Figure 6B. The spectrum can also be loaded into the non-uniform FFT without the numerical interpolation process.

[0084] In block 515, a dispersion correction (DC) is calculated Φ with reference to method 300, and more specifically with reference to block 320. disp Through the use of the (n) function, a dispersion compensation may be performed on the KL corrected fringe data generated in block 510, where dispersion compensation is performed to cancel the dispersion phase and correct the acquired OCT signal. A more robust correction can be performed compared to the raw dispersion data, since a polynomial fitting function or a low pass filter is used.

[0085] In this block, the fringe data can be Hilbert transformed into analytical form, -jΦ, where j is the imaginary unit. dispAn exponential function of (n) can be used to compensate for the dispersion phase.

[0086] In this block, new fringe functions can be calculated by taking the real part of the corrected fringe data multiplied by the complex-valued phase. s1(n)=Real{analytical form of s0(n)×exp[-jΦ disp (n)]}

[0087] The real function above takes the "real" part of the fringes that have been k-linearized and converted to a complex-valued analytical form.

[0088] In block 520, a spectral flattening can be performed on the results obtained in block 515. In this block, the spectral envelope can be removed. Despite both KL and DC correction, the ramping envelope of the fringes shown in FIG. 4E can still limit the axial resolution performance due to the constrained bandwidth. In this block, the real-valued envelope calculated in block 320 can be used. The spectral envelope from the calibration is fitted to a window function (e.g., a Kaiser window) and used as a demodulation window to flatten the KLDC corrected fringes.

[0089] In some examples, the following steps may be performed at block 520. s2(n)=s1(n)×(kaiser window(n)) / SF(n)

[0090] The above spectral envelope SF(n) is defined as the envelope of s1(n).

[0091] 4B shows a graph 430 illustrating the generated k-spectrum and the ideal k-spectrum. Graph 620 is a zoomed-in portion of the original fringes versus the KL corrected fringes according to an embodiment of method 300. The fitted spectrum can be stored and used for real-time imaging as described in method 500. Graph 620 shows the original data and the corrected data after a step of k-linearization correction has been performed.

[0092] 6A shows a graph 620 of an interferogram as a function of sampling index, showing the original fringe signal and the KL corrected fringe signal.

[0093] 6B shows a graph 630 with the difference of the KL corrected fringe signals before and after applying dispersion correction. A simplified form for accelerating the real-time imaging process can be written as follows: s3(z)=abs{s0(n)×exp[-jΦ disp (n)]×(kaiser window(n)) / SF(n)}

[0094] The absolute function abs mentioned above takes the "magnitude or modulus" portion of the k linearized fringes multiplied by the dispersion phase.

[0095] Graph 640 shows the narrower PSF vs. k-clock linearization after KLDC processing.

[0096] 6C shows an example of the difference in bandwidth before and after the application of spectral flattening in a graph 650. Note that the bottom plot has a wider FWHM bandwidth and a more symmetric structure after spectral flattening.

[0097] FIG. 6D shows an example graph 660 showing FWHM at different imaging depths using different methods. Data was acquired from an OPTIS OCT system without an optimized k-delay and strong system dispersion. Graph 660 shows the improvement in FWHM across all imaging depths between (i) signals using only k-clock data, (ii) signals using only KL technique, (iii) signals using KL and DC techniques, and (iv) signals using KL, DC, and SF techniques. Smaller FWHM indicates improved axial resolution in OCT images.

[0098] FIG. 7 illustrates a method 700 for generating OCT real-time imaging incorporating aspects of KLDC according to an embodiment of the present disclosure.

[0099] In block 705, fringe data may be acquired from a sensor of an OCT system. As one example, the system described with reference to Figure 1 may be used. As another example, the OPTIS system manufactured by Abbott Corporation may be used.

[0100] At block 710, KL, DC, and spectral flattening may be performed. At this step, one or more steps related to method 500 may be performed.

[0101] In block 715, zero padding and windowing may be performed. In windowing, a portion of the signal is kept within a selected interval. Mathematically, windowing is equivalent to applying a window function with a value of zero outside the selected interval. Zero padding is a process that can add zeros to the end of the signal to extend the length of the signal. This process may also allow the acquired signal to be more efficiently processed by the Fast Fourier Transform. Zero padding can increase the sample points, allowing the reconstructed signal to approach the theoretical limit of the optical resolution.

[0102] A Fast Fourier Transform (FFT) may be performed on the acquired data at block 720. The FFT may be an algorithm that calculates the Discrete Fourier Transform or its inverse.

[0103] The data is converted to a logarithmic (log) scale at block 725. In some examples, the FFT data may be normalized before converting to the logarithmic scale.

[0104] In block 730, an intensity image may be generated. The generated intensity image may be displayed on a monitor. The image data may be based on the logarithmic scale values ​​obtained in block 725.

[0105] Figure 8 shows an example OPTIS system with k-delay tuning set by standard manufacturing procedures. The plot shows that the KLDCsf (KLDC and Spectral Flattening) process corrects imaging side lobes and improves system resolution in a commercially available system that meets the specifications.

[0106] FIG. 9 shows the KLDCsf performance for an OPTIS system with k-delay tuning within specifications. The PSF profile shows a sharper beam waist and the system FWHM shows excellent system resolution across the entire imaging depth. FIG. 9 shows intensity plots 910, 920, and 930 at various imaging depths and the intensity of a signal acquired using the KLDCsf technique and the intensity of the same signal without the KLDCsf technique.

[0107] 9 also shows a plot 940, which shows imaging depth on the x-axis and FWMH in micrometers on the y-axis. As shown in plot 940, when using the KLDCsf technique, the FWMH is consistently narrower across the entire imaging depth compared to a system using a k-clock.

[0108] FIG. 10 shows plots 1010 and 1020 relating to the performance of KLDCsf on an OPTIS system without k-delay tuning and strong dispersion (not within specifications). Plots 1010 and 1020 also demonstrate the best resolution and SNR by implementing KLDCsf with an optimized k-spectrum. Plot 1010 shows imaging depth on the x-axis and FWHM in micrometers on the y-axis. Plot 1010 shows the smaller FWHM with the KLDCsf process and the slight improvement in FWHM when the KLDCsf process is applied to data resampled using the k-clock. Plot 1020 shows the signal-to-noise ratio (SNR) on the y-axis and imaging depth on the x-axis, showing the higher signal-to-noise ratio using the KLDCsf technique compared to signals acquired using the k-clock.

[0109] FIG. 11 shows a graph 1110 of a single line scan (A-line) imaged from a Kapton tape roll by an OPTIS system operating within specifications. The KLDCsf was applied to the raw data and to the data linearized in k-space using the k-clock. The graph 1110 shows pixel depth on the x-axis and intensity in decibels on the y-axis. As can be seen from the unlabeled arrows showing the peaks, the intensity peaks obtained using the KLDCsf with the optimized k-spectrum are higher than the intensity peaks from using only the KLDCsf at deeper depths (above 80% of the Nyquist imaging depth), resulting in better SNR and system resolution at depth. No obvious differences were observed before 80% of the Nyquist depth.

[0110] Figure 12 shows the performance of KLDCsf applied to the mirror signal of the OPTIS system without the use of k-clock. As can be seen from graph 1210, KLDCsf processing of the signal results in a sharper and more symmetrical peak at the sample depth, but the mirror signal cannot be reconstructed using data that is not linearized in k-space.

[0111] 13 shows a plot 1310 showing the resolution of a standard OPTIS system using its internal k-clock, and the same system without the k-clock connected, where the KLDCsf process is used. As can be seen from 1310, the KLDCsf without the k-clock improves the resolution of the original k-clock configuration.

[0112] The aspects, embodiments, features, and examples of the present disclosure are considered in all respects to be illustrative and are not intended to be limiting of the disclosure, the scope of which is defined solely by the claims. Other examples, modifications, and uses will be apparent to those skilled in the art without departing from the spirit and scope of the disclosure as claimed.

[0113] The use of headings and paragraphs in this application is not meant to limit the disclosure; each paragraph may apply to any aspect, example, or feature of the disclosure.

[0114] Throughout this application, when a composition is described as having, including, or comprising particular components, or a process is described as having, including, or comprising particular process steps, it is intended that the composition of the present teachings consists essentially of or consists of the recited components, and that the process of the present teachings consists essentially of or consists of the recited process steps.

[0115] When an element or component is referred to in this application as being included in and / or selected from a list of enumerated elements or components, it should be understood that the element or component can be any one of the enumerated elements or components, or can be selected from a group consisting of two or more of the enumerated elements or components. Furthermore, it should be understood that the elements and / or features of the compositions, devices, or methods described herein, whether expressly or implicitly stated herein, can be combined in various ways without departing from the spirit and scope of the present teachings.

[0116] Use of the terms "include," "includes," "including," "have," "has," or "having" should generally be understood to be open-ended and non-limiting, unless otherwise specified.

[0117] The use of the singular herein includes the plural (and vice versa) unless otherwise specified. Additionally, the singular forms "a," "an," and "the" include the plural unless the context clearly dictates otherwise. Additionally, when the term "about" or "substantially" is used before a quantitative value, the present teachings also include the specific quantitative value itself unless otherwise specified. The term "about" or "substantially" as used herein refers to variations in quantity that may occur, for example, through real-world measuring or handling procedures, through accidental errors in these procedures, through differences / faults in the manufacture of materials such as composite tapes, through defects, as well as variations that would be recognized as equivalents by those skilled in the art, unless such variations encompass known values ​​implemented by the prior art. Typically, the term "about" or "substantially" means greater or smaller than the stated value or range of values ​​by 1 / 10, e.g., ±10%, of the stated value.

[0118] It should be understood that the order of steps or order for performing certain actions is immaterial so long as the present teachings remain operable. Moreover, two or more steps or actions may be conducted simultaneously.

[0119] The use of headings and paragraphs in this application is not meant to limit the disclosure. Each paragraph may apply to any aspect, example, or feature of the disclosure. Only claims using the term "means for" are intended to be interpreted under 35 U.S.C. 112, paragraph 6. In the absence of a "means for" recitation in a claim, such a claim should not be interpreted under 35 U.S.C. 112. No limitations from this specification are intended to be read into any claim unless such limitations are expressly included in the claim.

[0120] Where values ​​or ranges of values ​​are given, each value and the endpoints of the given ranges and values ​​therebetween can be increased or decreased by 20% while still remaining within the teachings of the present disclosure, unless any different range is specifically stated.

[0121] When a range or list of values ​​is provided, each intervening value between the upper and lower limits of that range or list of values ​​is individually contemplated and encompassed within the disclosure as if each value were specifically recited herein. Additionally, smaller ranges between and including the upper and lower limits of a given range are contemplated and encompassed within the disclosure. A list of exemplary values ​​or ranges does not exclude other values ​​or ranges between and including the upper and lower limits of a given range.

[0122] It is understood that the figures and descriptions of the present disclosure have been simplified to show elements that are relevant for a clear understanding of the present disclosure, while omitting other elements for clarity. Those skilled in the art will recognize, however, that these and other elements may be desirable. However, because such elements are well known in the art, and because they do not facilitate a better understanding of the present disclosure, descriptions of such elements are not provided herein. It should be understood that the figures are presented for illustrative purposes, and not as structural diagrams. The omitted details and modifications or alternative examples are within the knowledge of those skilled in the art.

[0123] It can be recognized that in certain aspects of the present disclosure, multiple components can be substituted for single components, and multiple components can be substituted for single components, to provide an element or structure or to perform a given function or functions. Except to the extent that such substitutions cannot be used to practice a particular embodiment of the present disclosure, such substitutions are deemed to be within the scope of the present disclosure.

[0124] The examples presented herein are intended to illustrate possible and specific embodiments of the present disclosure. It can be appreciated that the examples are intended primarily to illustrate the present disclosure for those skilled in the art. There may be variations to these diagrams or the operations described herein without departing from the spirit of the present disclosure. For example, in certain cases, method steps or operations may be performed or executed in a different order, or operations may be added, deleted, or modified.

[0125] Aspects of the disclosed technology may include the following features in any combination.

[0126] A method for processing optical signals to improve resolution of an optically acquired image includes calibrating an optical system, the calibration including: one or more processors acquiring at least a first mirror measurement and a second mirror measurement, the first mirror measurement and the second mirror measurement being collected from both sides of a zero delay line, each mirror measurement being an interferogram or a signal or system impulse response; one or more processors acquiring amplitude and phase of the first mirror measurement and the second mirror measurement; resampling at least one of the first mirror measurement and the second mirror measurement to be in (i) linear wavenumber (k) space, (ii) linear wavelength, and / or (iii) linear time to generate a resampled mirror measurement; and using a function to calculate an optimized fit of the resampled mirror measurement. Any number of mirror measurements can be used for the calibration. For example, a few, tens, hundreds, or more mirror measurements can be acquired and used for the calibration.

[0127] The function used for fitting can be one of a polynomial function, a cubic spline fitting function, a radial basis function, or a piecewise function. The method may further include performing k-linearization on a raw interferogram of mirror measurements, the mirror measurements being obtained with or without a k-clock, and generating a resampled interferogram, the resampled mirror measurements being in k-space.

[0128] The k-linearization may be performed using an interpolation function, which is one of a cubic spline interpolation, a cubic interpolation, or a linear interpolation in k-space.

[0129] The method may further include performing spectral flattening on at least one mirror measurement by using an envelope calculated from the particular mirror measurement.

[0130] Each mirror measurement may be a system impulse response.

[0131] The method may further include calculating a dispersion compensation for the at least two mirror measurements and optionally compensating for the dispersion.The method may further include storing the spectral envelope calculated during the spectral flattening.

[0132] According to some examples, the optimized polynomial is determined based on at least two mirror measurements. In some examples, three or more mirror measurements may be used. The optimization of the optimized polynomial may be based on a polynomial fitting order 1 to n, where n is a fixed integer. The method may further include determining, for each of the mirror measurements, a polynomial fitting order that minimizes (i) the area of ​​the point spread function, (ii) the full width at half maximum (FWHM) resolution, or a combination of (i) and (ii).

[0133] (i) The method may further include storing calibration standards related to k-linearization (KL), (ii) dispersion correction (DC), and (iii) spectral flattening (SF).

[0134] The method may further include receiving an OCT signal or interference signal from the specimen. The method may further include generating new fringes or resampling the interferogram after linear k-interpolation to k-linearize the received OCT signal or interference signal by using a KL calibration standard. The method may further include performing dispersion correction on the k-linearized OCT signal or interference signal using a DC calibration standard. The method may further include removing the envelope of the k-linearized and dispersion corrected OCT signal or interference signal. The method may further include converting the OCT signal or interference signal into an OCT image for display on a display.

[0135] A method for generating an OCT image from an OCT signal may include the steps of acquiring an OCT signal corresponding to a specimen and loading a calibration spectrum, wavenumber linearizing (KL) the OCT signal to generate a KL OCT signal, dispersion correcting (DC) the KL OCT signal to generate a KL DC OCT signal, spectrally flattening (sf) the KLDC OCT signal to generate a final KLDCsf OCT signal, and performing post-processing on the final OCT signal to generate an OCT image, wherein the calibration spectrum used to generate the KL OCT signal, the KL DC OCT signal, and the final KLDCsf OCT signal is based on a calibration standard generated in a calibration stage.

[0136] The OCT signal can be, for example, an interferogram.

[0137] The calibration standard can be generated in a calibration stage using at least a first mirror measurement and a second mirror measurement collected from opposite sides of a zero delay line, each mirror measurement being an interferogram or a signal or a system impulse response. According to some examples, additional mirror measurements can be used.

[0138] A system for displaying a set of images of a subject comprises a memory that stores image data and time-varying data corresponding to the subject, and one or more processors in communication with the memory, wherein the one or more processors are operable to: acquire at least a first mirror measurement and a second mirror measurement, the first mirror measurement and the second mirror measurement being collected from both sides of a zero delay line, each mirror measurement being an interferogram or a signal or a system impulse response; calculate amplitudes and phases of the first mirror measurement and the second mirror measurement; resample at least one of the first mirror measurement and the second mirror measurement to be in at least one of (i) linear wavenumber (k) space, (ii) linear wavelength, and (iii) linear time to generate resampled mirror measurements; and calculate an optimized fitting of the resampled mirror measurements using a function.

[0139] The system or computer readable medium may be configured to implement any combination of the features described above.

[0140] A system for performing optical coherence tomography on a sample may include a light source, a reference mirror having a variable position, a display, a memory that stores image data corresponding to the sample, and one or more processors in communication with the memory, the one or more processors operable to: acquire at least one calibration parameter from the mirror measurements each time the system is started; acquire a signal from the sample; apply the at least one calibration parameter to the acquired signal, where the calibration parameter is at least one of a wavenumber linearization, a dispersion correction, or a spectral flattening parameter; and generate an image from the corrected acquired signal.

[0141] The system may further comprise an optical switch and a calibration mirror configured to generate a calibration signal.

[0142] The computer readable medium can include instructions executable by one or more processors to perform a method including obtaining a first mirror measurement and a second mirror measurement, the first mirror measurement and the second mirror measurement collected from either side of a zero delay line, each mirror measurement being an interferogram or a signal or a system impulse response, calculating an amplitude and phase of the first mirror measurement and the second mirror measurement, resampling at least one of the first mirror measurement and the second mirror measurement to be in at least one of (i) linear wavenumber (k) space, (ii) linear wavelength, and (iii) linear time to generate a resampled mirror measurement, and calculating an optimized fitting of the resampled mirror measurement using a function.

[0143] The computer readable medium can store instructions executable by one or more processors to perform a method that includes acquiring at least one calibration parameter each time the system is launched, acquiring a signal from a sample, applying the at least one calibration parameter to the acquired signal, where the calibration parameter is at least one of a wavenumber linearization, a dispersion correction, or a spectral flattening parameter, and generating an image from the corrected acquired signal.

[0144] A method of processing optical signals to improve resolution of an optically acquired image may include calibrating an optical system, the calibration comprising the steps of: one or more processors acquiring a first mirror measurement and a second mirror measurement, the first mirror measurement and the second mirror measurement collected from either side of a zero delay line, each mirror measurement being an interferogram or a signal or a system impulse response; the one or more processors acquiring amplitude and phase of the first mirror measurement and the second mirror measurement; calculating dispersion compensation for at least one mirror measurement, optionally compensating for dispersion; and calculating an optimized fit to the mirror measurements using a function.

Claims

1. 1. A method of processing an optical signal, comprising calibrating an optical system, the method comprising: The calibration may include: obtaining, by one or more processors, at least a first mirror measurement and a second mirror measurement, the first mirror measurement and the second mirror measurement collected from opposite sides of a zero delay line; the one or more processors obtaining amplitude and phase of the first mirror measurement and the second mirror measurement; resampling at least one of the first mirror measurement and the second mirror measurement into at least one of: (i) linear wavenumber (k) space, (ii) linear wavelength, and (iii) linear time to generate a resampled mirror measurement; calculating an optimized fit of the resampled mirror measurements using a function; The method of claim 1,

2. The method of claim 1 , wherein the function is one of a polynomial function, a cubic spline fitting function, a radial basis function, or a piecewise function.

3. 2. The method of claim 1, further comprising performing k-linearization on the mirror measurements, the mirror measurements being acquired with or without a k-clock, and generating a resampled interferogram, the resampled mirror measurements being in k-space.

4. 4. The method of claim 3, wherein the k-linearization is performed using an interpolation function, the interpolation function being one of a cubic spline interpolation, a cubic interpolation, or a linear interpolation in k-space.

5. The method of claim 1 , further comprising the step of performing spectral flattening on at least one mirror measurement by using an envelope calculated from the particular mirror measurement.

6. The method of claim 1 , wherein each mirror measurement is one of an interferogram, a signal, or a system impulse response.

7. The method of claim 1 , further comprising the step of calculating dispersion compensation for the at least two mirror measurements.

8. The method of claim 5 further comprising the step of storing the spectral envelope calculated during the spectral flattening.

9. 2. The method of claim 1, further comprising determining an order of polynomial fitting that minimizes the sum of at least one of: (i) the area of ​​the point spread function; or (ii) the full width at half maximum (FWHM) resolution.

10. The method of claim 1 , further comprising storing calibration standards related to (i) k-linearization (KL), (ii) dispersion correction (DC), and (iii) spectral flattening (SF).

11. The method of claim 1 , further comprising receiving an OCT signal or an interference signal from the sample.

12. 12. The method of claim 11, further comprising generating new fringes or resampling an interferogram after linear k-interpolation to k-linearize the received OCT or interference signal by using the KL calibration standard.

13. 13. The method of claim 12, further comprising: performing dispersion compensation on the k-linearized OCT or interference signal using the DC calibration standard; and removing an envelope of the k-linearized and dispersion compensated OCT or interference signal.

14. The method of claim 11 , further comprising converting the OCT or interference signal into an OCT image for display on a display.

15. a memory for storing image data and time-varying data corresponding to a subject; One or more processors operable to perform the method of any one of claims 1 to 14 and in communication with said memory; The system comprising:

Images