Abstract:
A system and method for resampling interference datasets of samples in segments, in a swept-source based Optical Coherence Tomography (OCT) system. The resampling is preferably performed within a Field Programmable Gate Array (FPGA) of the OCT system, the FPGA preferably having Fourier-transform based signal processing capabilities such as Fast Fourier Transform (FFT) cores. Resampling the interference datasets in segments provides a flexible approach to resampling that makes efficient use of system resources such as FFT cores. By resampling the interference datasets in segments, the system adjusts to an increased number of resampling points as the imaging depth upon the sample increases. The OCT system then combines the segments using overlapping values of the resampling points between adjacent resampling regions of the resampled segments, and performs Fourier Transform based post-processing on the combined segments to obtain axial profiles of the sample at desired imaging depths.

Description:
BACKGROUND OF THE INVENTION 
     Optical coherence tomography (“OCT”) is a type of optical coherence analysis that is becoming increasingly popular in research and clinical settings. OCT provides high-resolution imaging of sub-surface features of a sample. This is useful for the in vivo analysis of biological tissues, for example. 
     The original OCT imaging technique was time-domain OCT (TD-OCT), which used a movable reference mirror in a Michelson interferometer arrangement. A modern optical coherence analysis technique is termed Fourier domain OCT (“FD-OCT,”) of which there are generally two types: Spectral Domain OCT and Swept Source OCT. 
     In both techniques, optical waves that have been reflected from an object or sample are combined with reference waves to produce OCT interference signals. A computer produces axial scans (A-lines) or combines many A-lines into two-dimensional cross sections or three-dimensional volume renderings of the sample by using information on how the waves are changed upon reflection by reference to the reference waves. 
     Spectral Domain OCT and Swept Source OCT systems differ in the type of optical sources and detectors that they use. Spectral Domain OCT systems typically resolve the spectral components of an interference signal by spatial separation. Spectral Domain OCT systems utilize a broadband optical source and a spectrally resolving detector system to determine the different spectral components in each axial scan of the sample. As a result, the detector system is typically complex, as it must detect the wavelengths of all optical signals in the spectral scan band simultaneously, and then convert them to a corresponding interference dataset. This affects the speed and performance of Spectral Domain OCT systems. In contrast, Swept Source OCT systems encode spectral components in time, not by spatial separation. Swept Source OCT systems typically utilize a single tunable laser source that is swept in wavelength over a scan range or band. The interference signal is detected by a non-spectrally resolving detector system. 
     Swept Source OCT systems often utilize a sampling clock, or k-clock, that is used in the sampling (including resampling) of the interference signals. Basically, the k-clock is used to correct for non-linearities in the frequency sweeping of the swept source. Some Swept Source OCT systems use a hardware-based k-clock to directly trigger the Analog-to-Digital (“A/D”) converter of a Data Acquisition (“DAQ”) system that samples the interference signals. Other Swept Source OCT systems sample the k-clock signals in the same manner as the interference signals, creating a k-clock dataset of all sampled k-clock signals and an interference dataset of all sampled interference signals. Then, the k-clock dataset is used to resample the interference dataset in software. This is also known as a software-based k-clock. The resampling provides data that are evenly spaced in the optical frequency domain, or k-space. This provides maximal SNR and axial imaging resolution for subsequent Fourier transform-based signal processing upon the acquired interference signal spectra or interference dataset. 
     Fourier transform-based signal processing upon the interference signals provides the “A-line” information, or axial scan depth within the sample at each frequency of the reflected light in the resampled interference dataset. The spatial domain signals that include the “A-line” information, in turn, are compiled from many scans to generate a tomographic image or volume data set. 
     Because of the potentially high processing overhead that resampling of interference datasets and Fourier transform-based signal processing can incur, manufacturers of FD-OCT systems are increasingly turning to special-purpose processing units such as Field-Programmable Gate Arrays (“FPGA”), and General-Purpose Graphical Processing Units (“GPGPU,” or “GPU”). For more information, see “Scalable, High Performance Fourier Domain Optical Coherence Tomography: Why FPGAs and Not GPGPUs,” Jian Li, Marinko V. Sarunic, Lesley Shannon, School of Engineering Science, Simon Fraser University, Burnaby BC, Canada. Proceedings of the 2011 IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM &#39;11, 2011. 
     SUMMARY OF THE INVENTION 
     Some current systems and methods for software resampling of OCT signals, and techniques for processing the resampled signals into the “A-line” axial profiles of the sample lack computational and computer resource efficiency. The processing burden of the computer systems that perform the resampling increases as the number of sample points generated per scan of the sample increases. 
     The samples of the interference signals generated from OCT systems are typically not equally (linearly) spaced in k-space. OCT systems must first resample the interference datasets to be linearized such that they are located at equally spaced optical frequency increments. This is because Fourier Transform based algorithms that generate the A-lines from the interference signals require data samples that are linearly spaced in k-space. 
     The algorithms preferably include Fast Fourier Transforms (FFT) that typically require large amounts of memory and processing power, especially when real-time processing is desired. Selection of FFTs typically involves a cost tradeoff between core size/number of FFT points and the time required to perform the transform, also known as the transform time. The transform time increases with increasing interference dataset width. As a result, operators typically purchase different versions of the OCT equipment in response to their signal processing needs and dataset width, which increases cost. 
     OCT systems typically resample their interference datasets using linear phase information extracted from the k-clock dataset. Because the linear phase information is evenly spaced in k-space, it can be utilized as a resampling clock. Typically, OCT systems extract the linear phase information from the k-clock dataset using a Hilbert Transform. 
     Hilbert Transforms utilize multiple FFT-based computations. As a result, the Hilbert transform-based process of extracting phase information from the k-clock dataset typically introduces two additional FFT computations or stages. When combined with the processing associated with the final FFT stage for creating the A-lines from the linearized interference dataset, current OCT systems and methods have poor real-time performance. Though current resampling systems and methods typically utilize GPUs and/or FPGAs to execute computationally intensive resampling algorithms such as the FFT, the current OCT systems and resampling methods typically do not scale with increasing interference signal dataset width. 
     The present invention can be used to overcome these limitations by providing the ability to divide an interference dataset into an arbitrary number of smaller datasets, or segments. The number of segments is typically selected in conjunction with the FFT/processing core size and transform time to achieve optimum efficiency for the scans of the sample. The invention resamples the segments, and combines or stitches the resampled segments into a reconstructed or linearized interference data set for further processing. Finally, using Fourier Transform based processing of the linearized interference dataset, axial profiles of the sample at different imaging depths can be obtained. 
     In addition, the invention can also entail dividing the k-clock dataset into segments and processing the segments separately in a fashion similar to the division and processing of the interference datasets. This improves computational efficiency as compared to current OCT systems and methods. By dividing the k-clock dataset into segments, the computationally-intensive, Hilbert Transform-based phase extraction process is performed on the segments. This reduces the processing overhead and memory utilization as compared to performing the Hilbert Transform upon the original, entire k-clock dataset. 
     The invention combines or stitches adjacent k-clock segments together according to sample points in common between the segments. This creates a phase function for resampling the interference dataset that is equivalent to the phase function extracted from the original k-clock dataset using current systems and methods. The creation of the phase function provided by the present invention can be performed in a more resource and time-efficient manner than current systems and methods, however. 
     This system can provide several advantages. By breaking the data sets into smaller pieces, the resampling can be performed more efficiently. The smaller data sets enable usage of reduced core size Fourier Transform computing components as compared to the core size or number of sample points required for processing of full data sets using current methods. This can save on component cost and can improve processing speed. The smaller data sets also reduce throughput bottlenecks in the different stages of the resampling and processing of the interference signals as compared to current methods. Moreover, the present invention can also be utilized to limit signal sensitivity “roll off” in the A-lines generated from the linearized interference dataset. 
     Sensitivity of the interference signals, and therefore the quality of images created from the interference signals, decreases or “rolls off” with increasing spectral bandwidth. The spectral bandwidth is also known as the imaging depth range. 
     Solutions for minimizing roll-off in the A-lines generated from the interference dataset include expanding the number of samples in the k-clock dataset prior to extracting its linear phase information for resampling of the interference dataset. This is typically performed by multiplying the number of points or samples in the k-clock dataset by a depth factor. The depth factor can be integral or non-integral. In examples, the multiplication includes expanding the number of samples via band-limited interpolation. 
     Operators of the OCT system select the depth factor experimentally. Multiplying the k-clock dataset by the depth factor increases the number of samples in the k-clock dataset and expands the “grid” of sample points in the k-clock dataset. Resampling the interference dataset using phase information extracted from the expanded k-clock dataset maximizes the effective coherence length of the samples in the resampled interference dataset, also known as the linearized interference dataset. Maximizing the effective coherence length of the samples at depths near the Nyquist frequency during resampling correspondingly minimizes roll-off in A-lines generated from the linearized interference dataset. 
     In general, according to one aspect, the invention features a method for processing interference signals in an optical coherence tomography system. The method comprises generating k-clock signals in response to frequency sweeping of a swept optical signal, generating interference signals from the swept optical signal, sampling the k-clock signals and the interference signals to generate a k-clock dataset and an interference dataset, creating a clock phase function from the k-clock dataset, dividing the interference dataset into segments of sample points, resampling the segments of the sample points using the clock phase function, and combining the segments of resampled points into a linearized interference dataset. 
     The method typically performs resampling of the segments of the sample points using a Field Programmable Gate Array and creates the clock phase function by performing a Hilbert Transform upon the k-clock dataset. The method can divide the k-clock data set into phase segments, and creates the clock phase function by performing a Hilbert Transform upon the phase segments and by combining the transformed phase segments. 
     The method performs a Fourier transform upon the linearized interference dataset to obtain axial profiles of the sample, and combines the segments of resampled points by aligning adjacent segments of the resampled points in time. 
     The method selects resampling regions of the interference dataset for resampling the segments of the sample points, and selects an overlap region between adjacent resampling regions. The overlap region includes one or more resampled points in common between the adjacent resampling regions. 
     The method preferably aligns the adjacent segments of the resampled points according to the overlap regions for combining the segments of the resampled points into the linearized interference dataset. 
     In other aspects, the method performs the resampling of the segments of the sample points by upsampling the segments of the sample points to create transformed segments, performs linear interpolation of the transformed segments to create the segments of the resampled points, and performs an Inverse Fourier Transform upon the segments of the resampled points. The upsampling of the segments of the sample points is typically accomplished by performing a band-limited Fourier Transform upon the segments of the sampling points. The linear interpolation of the transformed segments is typically accomplished by zero-padding the transformed segments. 
     The method also performs a Fourier transform upon the linearized interference dataset to obtain a point spread function (PSF) of the sample. 
     In yet another aspect, the method performs the resampling of the segments of the sample points by selecting a resampling region of the interference dataset for each segment of the sample points, wherein each resampling region includes the sample points for each segment, and wherein each resampling region includes additional sample points within an overlap region of the interference dataset. Then, the method performs upsampling of the resampling regions to create transformed resampling regions, and performs linear interpolation of the transformed resampling regions. Finally, the method performs an Inverse Fourier Transform upon the transformed resampling regions to create linearized resampling regions. 
     The upsampling of the resampling regions is accomplished by performing a band-limited Fourier Transform upon the resampling regions to create the transformed resampling regions. The linear interpolation of the transformed resampling regions is accomplished by zero-padding the transformed resampling regions. The method combines the segments of resampled points into the linearized interference dataset by preferably aligning their linearized resampling regions according to the sample points in the overlap regions. 
     In general, according to another aspect, the invention features a system for processing interference signals in an optical coherence tomography system. The system comprises a k-clock module that generates k-clock signals in response to frequency sweeping of a swept optical signal, an interferometer that generates interference signals from the swept optical signal, a data acquisition system that samples the k-clock signals and the interference signals to generate a k-clock dataset and an interference dataset, and a rendering system. This rendering system creates a clock phase function from the k-clock dataset, divides the interference dataset into segments of sample points, resamples the segments of the sample points using the clock phase function, and combines the segments of resampled points into a linearized interference dataset. 
     The rendering system can also divide the interference dataset into the segments of the sample points in response to imaging depth of the interference signals upon the sample. In addition, the rendering system includes computer memory utilized during the resampling of the segments of the sample points, and divides the interference dataset into the segments of the sample points in response to depth of the computer memory. 
     The above and other features of the invention including various novel details of construction and combinations of parts, and other advantages, will now be more particularly described with reference to the accompanying drawings and pointed out in the claims. It will be understood that the particular method and device embodying the invention are shown by way of illustration and not as a limitation of the invention. The principles and features of this invention may be employed in various and numerous embodiments without departing from the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the accompanying drawings, reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale; emphasis has instead been placed upon illustrating the principles of the invention. Of the drawings: 
         FIG. 1  is a schematic diagram of an optical coherence analysis system according to principles of the present invention; 
         FIG. 2A  is a block diagram of one method for processing interference datasets for creating axial profiles and tomographic images of a sample, utilizing a k-clock divided into overlapping k-clock segments; 
         FIG. 2B  is a block diagram of another method for processing interference datasets that is substantially similar to the method of  FIG. 2A , additionally multiplying the number of samples in the k-clock segments for increasing the effective coherence length of the interference dataset during resampling; 
         FIG. 3  is an frequency vs. time plot of a k-clock dataset, and illustrates an exemplary division of the k-clock dataset into overlapping k-clock segments; 
         FIG. 4A  is a block diagram of another method for processing interference datasets, where the processing utilizes a k-clock divided into overlapping k-clock segments and an interference dataset divided into segments of sampling points and overlapping resampling regions; 
         FIG. 4B  is a block diagram of another method for processing interference datasets that is substantially similar to the method of  FIG. 4A , additionally multiplying the number of samples in the k-clock segments for increasing the coherence length of the interference dataset segments of the sample points and resampling regions during resampling; 
         FIG. 5  is a frequency vs. time plot of an interference dataset, and illustrates an exemplary division of the interference dataset into the segments of sample points, and division of the interference dataset into resampling regions; 
         FIG. 6A-6C  show plots of an exemplary k-clock segment that has its number of samples expanded by multiplying the samples by depth factors of 1×, 2×, and 3× the number of samples, respectively, for resampling an interference dataset at a 5 mm Nyquist imaging depth; 
         FIG. 7A  shows a plot of an interference dataset resampled using the k-clock segment of  FIG. 6A , illustrating that using a depth factor of 1× the number of k-clock segment samples for resampling the interference dataset was not experimentally sufficient to provide maximum effective coherence length near a 5 mm Nyquist imaging depth; 
         FIG. 7B  shows a plot of an interference dataset resampled using the k-clock dataset of  FIG. 6B , illustrating that using a depth factor of 2× the number of k-clock segment samples was experimentally sufficient to provide maximum effective coherence length near a 5 mm Nyquist imaging depth; 
         FIG. 8A  shows a plot of a resampled interference dataset composed of a single segment of the sample points, and  FIG. 8B  shows a plot of an resampled result for the same interference set divided into two segments of the sample points; and 
         FIG. 9  shows linearized interference datasets of a sample, when the signals of the interference dataset have been resampled at different effective coherence lengths. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The invention now will be described more fully hereinafter with reference to the accompanying drawings, in which illustrative embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. 
     As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. It will be further understood that the terms such as includes, comprises, including and/or comprising, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, it will be understood that when an element is referred to and/or shown as being connected or coupled to another element, it can be directly connected or coupled to the other element or intervening elements may be present. 
       FIG. 1  shows an optical coherence tomography analysis system, or OCT system  100  to which the present invention is applicable. The OCT system  100  uses a swept source  102  to generate swept optical signals on optical fiber  104 . The swept source  102  is typically a tunable laser designed for high speed spectral sweeping. The swept optical signals are narrowband emissions that are scanned, or “swept,” over a spectral scan band. 
     Tunable lasers are constructed from a gain element such as a semiconductor optical amplifier (“SOA”) that is located within a resonant laser cavity, and a tuning element such as a rotating grating, a grating with a rotating mirror, or a Fabry-Perot tunable filter. Currently, some of the highest speed tunable lasers are based on the laser designs described in U.S. Pat. No. 7,415,049 B1, entitled “Laser with Tilted Multi Spatial Mode Resonator Tuning Element,” by D. Flanders, M. Kuznetsov and W. Atia, which is incorporated herein by this reference in its entirety. 
     Another technology for high-speed swept sources is termed tunable amplified spontaneous emission (ASE) sources. An example of an ASE swept source is described in U.S. Pat. No. 8,526,472 B1, “ASE Swept Source with Self-Tracking Filter for OCT Medical Imaging,” by D. Flanders, M. Kuznetsov and W. Atia, which is incorporated herein by this reference in its entirety. 
     A fiber coupler  106  or other optical splitter divides the swept optical signal from the swept source  102  into a portion that is provided to an OCT interferometer  108  and a portion that is provided to a k-clock module  110 . In alternate embodiments, the swept optical signal could be transmitted in free space or internally as part of an integrated system that includes the swept source  102 , interferometer  108 , and the k-clock module  110 . 
     A controller  190  controls the swept source  102  using a source control signal that configures the swept source  102  to scan over the scan band. The controller  190  also controls a Data Acquisition System (“DAQ”)  112 . 
     In the current embodiment, the interferometer  108  is a Mach-Zehnder-type that sends optical signals to a sample  122 , analyzes the optical signals reflected from the sample  122 , and generates an optical interference signal in response. 
     In the illustrated embodiment of the OCT system  100 , the optical interference signal generated by the sample interferometer  108  is detected by a sample optical receiver  114 . The optical receiver  114  converts the optical interference signal into an electronic interference signal  152 . In the preferred embodiment, the sample optical receiver  114  is a balanced detector system, which generates the electronic interference signal  152 . 
     The k-clock module  110  generates optical k-clock signals at equally spaced optical frequency sampling intervals as the swept optical signal is tuned or swept over the scan band. Optical receiver  115  detects the optical signals generated by the k-clock module  110  and converts the optical signals into electronic k-clock signals  156 . The electronic k-clock signals  156  are used by the data acquisition system  112  to track the frequency tuning of the optical swept source  102 . 
     There are a number of ways to implement the k-clock module  110 . One example utilizes a Michelson interferometer. These generate a sinusoidal response to the frequency scanning of the swept optical signal. In specific implementations, a fiber Michelson interferometer is used. In other implementations, etalons are used in the k-clock module  110  to filter the swept optical signal. An example of a clock integrated with a swept source laser is described in U.S. Pat. No. 8,564,783 B2 “Optical Coherence Tomography Laser with Integrated Clock,” by D. Flanders, W. Atia, B. Johnson, M. Kuznetsov, and C. Melendez, which is incorporated herein by this reference. 
     The DAQ  112  accepts the electronic interference signals  152  and the electronic k-clock signals  156  on input channels Ch 1 , Ch 2  of the DAQ  112 . The DAQ  112  accepts a sweep trigger signal  158  indicating the start of the sweeps of the swept source  102 . 
     Based on an initial signal sampling rate, the DAQ  112  performs analog to digital conversion to sample the electronic k-clock signals  156  and electronic interference signals  152  for the scan band into a k-clock dataset  146  and interference dataset  142 , respectively. A rendering system  120  accepts the k-clock dataset  146  and interference dataset  142  as inputs, and performs operations upon the datasets to create interferometric A-line  184  depth scans of the sample. 
     The DAQ  112  and the rendering system  120  are preferably included as part of a computer system  180 . The controller  190 , in one example, accepts commands from software running on the computer system  180  to control components of the OCT system  100  such as the rendering system  120 . 
     The rendering system  120  preferably includes an FPGA  154  or other data processing system that implements resampling algorithms for resampling of the interference dataset  142 . The rendering system  120  creates a clock phase function  124 , also labeled as F(φ), from phase information extracted from the k-clock dataset  146 . 
     The interference dataset  142  is spaced uniformly in time, but non-uniformly in frequency. In examples, the rendering system  120  uses the phase function  124  to resample the interference dataset  142  to produce a frequency-uniform version of the interference dataset  142 . The frequency-uniform version of the interference dataset  142  is also known as a linearized interference dataset  174 . The rendering system  120  creates the linearized interference dataset  174  to enable subsequent Fourier Transform based signal processing upon the linearized interference dataset  174 . This creates reflectivity profiles, or A-lines  184  of the sample  122  from interference dataset  174 . 
     The Fourier Transform based processing preferably utilizes a Fast Fourier Transform (FFT). The memory associated with performing FFT calculations is often referred to as the FFT core. The FFT core is capable of performing resampling of signals using a maximum number of sampling points per resampling cycle, such as 4096 or 8192, in examples. This is also known as the memory depth of the FFT core. In examples, a 12-bit FFT core can provide 2 exp 12=4096 samples per resampling cycle, and a 13-bit FFT core can provide 2 exp 13=8092 samples per resampling cycle. 
     To achieve maximum efficiency of the FFT-based processing of the interference dataset  142 , operators optimally select an FFT core with memory depth that is equal to or greater than the expected number of sample points in the dataset. However, the cost of FFT cores and their support logic increases significantly with increasing core size. Moreover, as business demands require operators to create ever increasing dataset sizes, current systems and methods typically do not scale with increasing dataset size. FFT cores typically cannot be removed and replaced in response to changing operational conditions in a cost-effective manner. 
     To address this problem, the rendering system  120  preferably divides the interference dataset  142  into segments of sample points. The rendering system  120  typically divides the number of samples in the interference dataset by the FFT core depth to determine the number of segments of the sample points. The FPGA  154  accepts the segments of sample points, resamples them using the phase function  124  into segments of resampled points  182 , and creates a linearized interference dataset  174  by combining the segments of resampled points  182 . The FPGA  154  then performs a final Fourier Transform upon the linearized interference dataset  174  to create the axial profiles  184  of the sample.  122 . With the assistance of the FPGA  154 , the rendering system  120  combines the axial profiles or A-lines  184  to create 2D and 3D images  186  of the sample  122 . 
     The computer system  180  communicates with a display device  196  for displaying information about the OCT system  100  and its components to the operator. The computer system  180  preferably stores data related to scanning of the sample  122  to a media storage device  200 , such as a database. 
       FIG. 2A  shows a block diagram of a method for creating interferometric depth profiles or A-lines  184  of the sample  122  and tomographic images  186  from the A-lines  184  using an OCT system  100  such as that of  FIG. 1 . 
     An operator uses the OCT system  100  to scan a sample  122  and create a k-clock dataset  146  and an interference dataset  142 . The k-clock dataset  146  is then passed through spectral filter  202  to remove artifacts in the signals of the k-clock dataset  146 . The artifacts are typically associated with optical imperfections in the k-clock module  110 . The k-clock dataset  146 , in step  204 , is then divided into one or more overlapping k-clock segments  206  of sampling points  308  in response to memory space requirements of the rendering system  120 . 
     In  FIG. 2A , the k-clock dataset  146  has been divided into multiple k-clock segments. In the example, two segments  206 - 1  and  206 - 2  are shown. In some examples, the k-clock dataset  146  has been divided into five (5) or more k-clock segments, and divided into even more than ten (10) in still other examples. The overlapping of the k-clock segments  206 - 1  and  206 - 2  is provided by an overlap region  312  that include points in common between adjacent k-clock segments  206 - 1  and  206 - 2 . In one implementation, the rendering system  120  divides the k-clock dataset  146  into the fewest number of segments  206  having a number of samples less than or equal to the FFT core depth of the FPGA  154 . In another example, an operator performs this step manually. 
     In phase extraction steps  208 - 1  and  208 - 2 , the rendering system  120  extracts linear phase information from the real component of k-clock segments  206 - 1  and  206 - 2 , respectively. Preferably, the rendering system  120  uses a Hilbert Transform to extract the phase information. Hilbert Transforms include at least two memory-intensive FFT operations for extracting phase information from the k-clock dataset  146 . In addition, as the operator increases the number of samples in the k-clock dataset  146  above the FFT core depth, the number of FFT operations required increases. Performing separate Hilbert Transforms on the smaller k-clock segments  206 - 1  and  206 - 2  typically provides more efficient utilization of system resources than performing a Hilbert transform on the entire k-clock dataset  146 . 
     In step  210 , the rendering system  120  combines or stitches the extracted phases  208 - 1  and  208 - 2  into a linear phase function  124 , using the regions of overlap in the original segments  206 . The rendering system  120  accepts the interference dataset  142 , and in step  220 , resamples the interference dataset  142  using the phase function  124  to create a linearized interference dataset  174 . In examples, the resampling includes linear interpolation between samples in the k-clock dataset  146  and band-limited interpolation of the samples in the interference dataset  142 . The method then performs a final processing stage  240  upon the linearized interference dataset  174 . 
     For the final processing stage  240 , the method first eliminates edge effects and noise in the linearized interference dataset  174  using a windowing function, such as a Hanning window, in step  230 . According to step  232 , the method accepts the windowed linearized interference dataset  174  and performs an FFT to create A-lines  184  for each sample location in the linearized interference dataset  174 . Finally, the method combines the A-lines  184  into 2D and 3D tomographic images  186  of the sample  122  to complete the final processing stage  240 . 
     In examples, the rendering system  120  in conjunction with the DAQ  112  resamples the interference dataset  142  by upsampling the interference dataset  142  via linear interpolation, followed by zero-padding of the frequency spectra. In other examples, the rendering system  120  resamples the interference dataset  142  by performing a band-limited Fast Fourier Transform (FFT) upon the interference dataset  142 . 
       FIG. 2B  shows another method for processing interference datasets  146  that is substantially similar to the method of  FIG. 2A . The method additionally multiplies the number of samples in the k-clock segments  206  prior to resampling the interference dataset  142  for increasing the effective coherence length of the interference dataset  142  after resampling. 
     The method spectrally filters the k-clock dataset in step  202  and divides the k-clock dataset  146  in step  204  into overlapping k-clock segments  206  as in the method of  FIG. 2A . However, prior to phase extraction of the k-clock segments  206 , the method multiplies the number of points in the k-clock segments  206  by a selected depth factor in step  214 . The depth factor can be integer or non-integer in value. 
     During the phase extraction process, the method extracts sample points from the k-clock segments  206  according to the depth factor for resampling the signals in the interference dataset  142  to a desired imaging depth. Increasing the depth factor by value X, where X is integer or non-integer, correspondingly extracts X times the number of points from the k-clock segments  206 , and correspondingly resamples the signals in the interference dataset  142  to X times the effective coherence length/depth. Experimentation has shown, however, that higher multiples for the depth factor may introduce artifacts in the linearized interference dataset  174 . 
     After expanding the number of points in the k-clock segments  206  in step  214 , the method extracts linear phase information  208 - 1  and  208 - 2  from the expanded/multiplied k-clock segments  206 - 1  and  206 - 2 . The method stitches the phase information in step  210  to create phase function  124  and resamples the interference dataset  142  in step  220  to create the linearized interference dataset  174 . 
     In step  222 , the method provides the operator with the ability to determine if the effective coherence length of the linearized interference dataset  174  is maximized at imaging depths near the Nyquist frequency upon resampling. If this is true, the method performs the same steps associated with the final processing stage  240  in the method of  FIG. 2A  to create the A-lines  184  and the tomographic images  186  of the sample  122 . 
     If the operator is not satisfied with the results of the test in step  222 , the operator adjusts the depth factor of the k-clock segments in step  224 . 
     The adjustment of the depth factor in step  224  is a step included within an iterative depth factor processing loop  242 - 1 . The depth factor processing loop  242 - 1  provides the operator with the ability to maximize the effective coherence length of the linearized interference dataset  174 , and therefore to maximize the depth resolution of A-lines  184  and images  186  created from the linearized interference dataset  174 . 
     Specifically, the depth factor processing loop  242 - 1  includes the following steps: step  214  to multiply/expand the number of points in the k-clock segments  206  for a selected depth factor; phase extraction step  208 ; step  210  for stitching the extracted phases to create the linear phase function  124 ; step  220  for resampling the interference dataset  142  using the expanded “grid” of linearly-spaced samples in k-space of the linear phase function  124 ; step  222  to test for maximum effective coherence length of the linearized interference dataset  174 ; and, step  224  to adjust the depth factor of the k-clock segments and repeat the steps of the depth factor processing loop  242 - 1  if the test in step  222  does not yield the desired or maximum effective coherence length of the linearized interference dataset  174 . 
     The depth factor processing loop  242 - 1  exits if the operator is satisfied in step  222  that the effective coherence length of the linearized interference dataset  174  is maximized. The method then completes the steps associated with final processing stage  240 . 
     In step  222 , the operator can save the linearized interference dataset  174  and the k-clock segments  206  to database  200 . In this way, the operator can store and retrieve the output from each iteration of the depth factor processing loop  242 - 1  based on experimental results. The operator also has the ability to store the A-lines  184  and the images  186  generated from final processing stage  240  to the database  200 . 
       FIG. 3  shows an exemplary k-clock dataset  146  divided into segments by the rendering system  120  in response to system memory requirements. The electronic k-clock signal  156  is sampled by the DAQ  112  at samples  308  to create the k-clock dataset  146 . The rendering system  120  then breaks or divides the k-clock dataset  146  into overlapping segments of the sample points  206 . In the example, the k-clock dataset  146  is divided into two segments of the sample points  206 - 1  and  206 - 2 . 
     For the methods of  FIGS. 2A and 2B  included herein above, the rendering system  120  preferably selects an overlap region  312  between adjacent k-clock segments  206 , such as between k-clock segments  206 - 1  and  206 - 2 . The overlap region  312  includes sample points  308  in common between adjacent k-clock segments  206 - 1  and  206 - 2 . Preferably, the overlap region  312  includes at least 10% of the sample points  308  in common with adjacent k-clock segments  206 - 1  and  206 - 2 . The methods of  FIGS. 2A and 2B  utilize the common sample points  308  in the overlap region  312  when performing the phase stitching step  210  that creates the linear phase function  124 . 
       FIG. 4A  shows yet another method for processing interference datasets  142  in an OCT system  100 . The method spectrally filters the k-clock dataset in step  202  and divides the k-clock dataset  146  in step  204  into overlapping k-clock segments  206  as in the method of  FIG. 2A . However, after performing the phase extraction steps  208 - 1  and  208 - 2  from the k-clock segments  206 - 1  and  206 - 2 , the method saves the extracted phase information as separate linear phase functions  124 - 1  and  124 - 2  without combining/stitching them into a single phase function  124  as in the methods of  FIGS. 2A and 2B . 
     In parallel, in step  502 , the method divides the interference dataset  142  into segments of sample points  308 . In step  504 , the method then selects a resampling region  306  of the interference dataset  142  for each segment of the sample points  308 , the resampling regions  306  including the sample points  308  of their respective segments, and including additional sample points  308  beyond their segments. According to step  506 , the method then selects overlap regions  312  between adjacent resampling regions  306 , the overlap regions  312  defining a percentage of sample points in common between adjacent resampling regions  306 . 
     In  FIG. 4A , the interference dataset  142  has been divided into multiple segments, such as two, of the sample points  308  with associated resampling regions  306 - 1  and  306 - 2 . In one implementation, the rendering system  120  divides the interference dataset  142  into the fewest number of segments/resampling regions  306  having a number of samples less than or equal to the FFT core depth of the FPGA  154 . In another example, an operator performs this step manually. In some examples, the interference dataset  142  is divided into five (5) or more segments, and divided into even more than ten (10) in still other examples. 
     In steps  220 - 1  and  220 - 2 , the method resamples resampling regions  306 - 1  and  306 - 2  using the previously extracted linear phase functions  124 - 1  and  124 - 2 , respectively. This creates linearized segments of resampled points  182 - 1  and  182 - 2 , respectively. Then, in step  508 , the rendering system  120  stitches the segments of the resampled points  182 - 1  and  182 - 2  to create the linearized interference dataset  174 . As in the methods of  FIGS. 2A and 2B , the method performs final processing stage  240  upon the linearized interference dataset  174  to create the A-lines  184  and the 2D and 3D tomographic images  186  of the sample  122 . 
       FIG. 4B  shows yet another method for processing interference datasets  146  that is substantially similar to the method of  FIG. 4A . As in the method of  FIG. 2A , the method of  FIG. 4B  additionally multiplies the number of samples in the k-clock segments  206  prior to resampling the interference dataset  142  for increasing the effective coherence length of the samples in the interference dataset  142  during resampling. 
     The method spectrally filters the k-clock dataset  146  in step  202  and divides the k-clock dataset  146  in step  204  into overlapping k-clock segments  206  as in the method of  FIG. 4A . However, prior to extracting phase information from the k-clock segments  206 , the method multiplies the number of points in the k-clock segments  206  by a depth factor in step  214 . The depth factor can be integral or non-integral in value. 
     After performing the phase extraction steps  208 - 1  and  208 - 2  from the k-clock segments  206 - 1  and  206 - 2 , the method saves the extracted phase information as separate linear phase functions  124 - 1  and  124 - 2  without combining/stitching them into a single phase function  124  as in the methods of  FIGS. 2A and 2B . 
     In parallel, in step  502 , the method divides the interference dataset  142  into segments of sample points  308 . In step  504 , the method then selects a resampling region  306  of the interference dataset  142  for each segment of the sample points  308 , the resampling regions  306  including the sample points  308  of their respective segments, and including additional sample points  308  beyond their segments. According to step  506 , the method then selects overlap regions  312  between adjacent resampling regions  306 , the overlap regions  312  defining a percentage of sample points in common between adjacent resampling regions  306 . 
     After expanding the number of points in the k-clock segments  206  in step  214 , the method extracts linear phase information  208 - 1  and  208 - 2  from the expanded/multiplied k-clock segments  206 - 1  and  206 - 2 , respectively. The method then operates in a substantially similar way as the method of  FIG. 4A  for resampling the resampling regions  306 - 1  and  306 - 2  in steps  220 - 1  and  220 - 2 , creating segments of the resampled points  182 - 1  and  182 - 2 , respectively. 
     Then, in step  223 , the operator tests if the effective coherence length of the segments of the resampled points  182 - 1  and  182 - 2  are maximized at imaging depths near the Nyquist frequency upon resampling. If this is true, in step  508 , the method stitches the segments of the resampled points  182 - 1  and  182 - 2  to create the linearized interference dataset  174 . Upon completion of step  508 , as in the methods of  FIGS. 2A, 2B, and 4A , the method performs the steps associated with the final processing stage  240  to create the A-lines  184  and the tomographic images  186  of the sample  122 . 
     If the operator is not satisfied with the results of the test in step  223 , the operator adjusts the depth factor of the k-clock segments in step  224 . 
     The adjustment of the depth factor in step  224  is a step included within an iterative depth factor processing loop  242 - 2 . The depth factor processing loop  242 - 2  provides the operator with the ability to maximize the effective coherence length of the segments of the resampled points  182 - 1  and  182 - 2 . Because the segments of the resampled points  182 - 1  and  182 - 2  are ultimately stitched into a linearized interference dataset  174  in step  508 , the depth resolution of A-lines  184  and images  186  created from the linearized interference dataset  174  are correspondingly maximized. 
     Specifically, the depth factor processing loop  242 - 2  includes the following steps: step  214  to multiply/expand the number of points in the k-clock segments  206 - 1  and  206 - 2  for the selected depth factor; phase extraction step  208 - 1  and  208 - 2  to create the linear phase functions  124 - 1  and  124 - 2 ; steps  220 - 1  and  220 - 2  for resampling the resampling regions  306 - 1  and  306 - 2  using the expanded “grid” of linearly-spaced samples in k-space of the linear phase functions  124 - 1  and  124 - 2 , respectively; step  223  to test for maximum effective coherence length of the resulting segments of the resampled points  182 - 1  and  182 - 2 ; and, step  224  to adjust the depth factor of the k-clock segments and repeat the steps of the depth factor processing loop  242 - 1  if the test in step  223  does not yield the desired or maximum effective coherence length of the segments of the resampled points  182 - 1  and  182 - 2 . 
     The depth factor processing loop  242 - 2  exits if the operator is satisfied in step  223  that the effective coherence length of the segments of the resampled points  182 - 1  and  182 - 2  is maximized. The method then transitions to step  508  to stitch the segments of the resampled points  182 - 1  and  182 - 2  into the linearized interference dataset  174  and complete final processing stage  240 . 
     In step  223 , the operator can save the segments of the resampled points  182 , the linearized interference dataset  174  and the k-clock segments  206  to database  200 . In this way, the operator can store and retrieve the output from each iteration of the depth factor processing loop  242 - 2  based on experimental results. The operator also has the ability to store the A-lines  184  and the images  186  generated from final processing stage  240  to the database  200 . 
       FIG. 5  shows an exemplary interference dataset  142  including sample points  308  as previously described in the methods of  FIGS. 2A / 2 B and  4 A/ 4 B herein above. The DAQ  112  creates the interference dataset  142  by sampling the interference signals at the sample points  308 . In the methods of  FIG. 2A / 2 B, the interference dataset  142  is resampled “as is.” In the methods of  FIG. 4A / 4 B, the interference dataset  142  is divided into two or more segments of sample points. 
     For the methods of  FIG. 4A / 4 B, the rendering system  120  preferably selects resampling regions  306  of the interference dataset  142  for resampling the segments of the sample points  308 . In the example of  FIG. 5 , the rendering system selects two overlapping resampling regions  306 - 1  and  306 - 2 . 
     In conjunction with the DAQ  112 , the rendering system  120  resamples the segments of sample points to create segments of resampled points  306 . The rendering system  120  preferably selects an overlap region  312  between adjacent resampling regions  306 - 1  and  306 - 2 . The overlap region  312  includes resampled points  310  in common between resampling regions  306 - 1  and  306 - 2 . Preferably, the overlap region  312  includes at least 5%, but can be at least 10%, of the resampled points  310  in common between resampling regions  306 - 1  and  306 - 2 . The rendering system aligns the adjacent segments of the resampled points  182 - 1  and  182 - 2  according to the overlap regions  312  for combining the segments of the resampled points  182 - 1  and  182 - 2  into the linearized interference dataset  174 . 
       FIG. 6A-6C  show exemplary k-clock segments  206  created during the execution of the method of  FIG. 2B .  FIGS. 7A and 7B  display linearized interference datasets  174  created using the k-clock segments  206  in  FIGS. 6A and 6B , respectfully. 
       FIG. 6A  shows an exemplary k-clock dataset segment  206  created using the method of  FIG. 2B . The k-clock dataset segment  206  was created using a selected depth factor of 1, indicated in the figure as M=1. While the k-clock dataset segment  206  was created using the method of  FIG. 2B , the principles can be similarly applied to the execution of the method of  FIG. 4B . 
     The associated resampling rate of the k-clock segments  260  in the method of  FIG. 2B  was selected at  550 M samples/sec, which has an associated Nyquist frequency of 275 MHz. Correspondingly, the maximum effective coherence length in the linearized interference dataset  174  will be at depths near the Nyquist depth, or 5 mm. 
       FIG. 7A  shows the linearized interference dataset  174  created using the k-clock dataset segment  206  of  FIG. 6A  with selected depth factor of M=1. According to the test in the method of  FIG. 2B  step  222 , the linearized interference dataset  174  of  FIG. 7A  does not include signals/points near the desired 5 mm Nyquist depth for maximizing effective coherence length and minimizing roll-off. As a result, the operator increases the depth factor by a factor of two in the method of  FIG. 2B  step  224  to expand the “grid” of samples in the k-clock segment  206 . 
       FIG. 6B  shows k-clock dataset segment  206  created as a result of expanding the depth factor of the k-clock segment  206  of  FIG. 6A  by a factor of two, indicated in  FIG. 6B  as M=2.  FIG. 7B  shows the linearized interference dataset  174  created using the k-clock dataset segment  206  of  FIG. 6B  with selected depth factor of M=2. 
     According to the test in the method of  FIG. 2B  step  222 , the linearized interference dataset  174  of  FIG. 7B  now includes signals/points near the 5 mm Nyquist depth for maximizing effective coherence length and minimizing roll-off. As a result, the method of  FIG. 2B  exits the depth factor processing loop  242 - 1  and completes final stage processing  240  to create the A-lines  184  and images  186 . 
       FIG. 6C  shows k-clock dataset segment  206  created as a result of expanding the depth factor of the k-clock segment  206  of  FIG. 6A  by a factor of three, indicated in  FIG. 6C  as M=3. The linearized interference dataset  174  created using the k-clock segment  206  of  FIG. 6C  did not experimentally provide improved effective coherence length as compared to the results of the linearized interference dataset  174  in  FIG. 7B  created using the k-clock segment  206  of  FIG. 6C  with selected depth factor of M=3. As a result, the operator discontinued experimental adjustment of the depth factor and utilized the results associated with  FIGS. 6B and 7B . 
       FIGS. 8A and 8B  show resampled data for segments of the resampled points  182  for the same interference dataset  142 .  FIG. 8A  shows the resampled data for one selected segment of the resampled points  182 .  FIG. 8B  shows the resampled data for two selected segments of the resampled points  182 - 1 . The resampled data for two segments of the resampled points  182 - 1  in  FIG. 8B  have been stitched/combined to show that the resampled data are virtually identical between the two figures. This illustrates that resampling of interference datasets  142  in two or more segments  182  has no material effect on the resampling data as compared to current systems and methods. 
       FIG. 9  displays linearized interference datasets  174 - 1  through  174 - 6  associated with imaging depths of 1 mm, 2 mm, 5 mm, 8 mm, 10 mm, and 11 mm, respectively. As the imaging depth increases, the resampling performance as indicated by the strength of the signal decreases, requiring more resampling points  310  to reconstruct the interference dataset  142 . As a result, the number of selected segments of the resampled points  182  tends to increase with increasing imaging depth. 
     While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.