Patent Application: US-201414466774-A

Abstract:
an efficient method of evaluating the level of contrast of an oct dataset is presented . the method develops a metric to segregate useful and not - so - useful data in one or more oct b - scans , in order to reduce spurious subsequent analyses of the data by downstream segmentation algorithms . it is designed to be fast and efficient and is applied to determining autofocus of an oct instrument real - time and in identifying a real image from its complex conjugate twin .

Description:
a generalized fourier domain optical coherence tomography ( fd - oct ) system used to collect an oct dataset suitable for use with the present set of embodiments , disclosed herein , is illustrated in fig1 . a fd - oct system includes a light source , 201 , typical sources including but not limited to broadband light sources with short temporal coherence lengths or swept laser sources . light from source 201 is routed , typically by optical fiber 205 , to illuminate the sample 210 , a typical sample being tissues at the back of the human eye . the light is scanned , typically with a scanner 207 between the output of the fiber and the sample , so that the beam of light ( dashed line 208 ) is scanned over the area or volume to be imaged . light scattered from the sample is collected , typically into the same fiber 205 used to route the light for illumination . reference light derived from the same source 201 travels a separate path , in this case involving fiber 203 and retro - reflector 204 . those skilled in the art recognize that a transmissive reference path can also be used . collected sample light is combined with reference light , typically in a fiber coupler 202 , to form light interference in a detector 220 . the output from the detector is supplied to a processor 221 . the results can be stored in the processor or displayed on display 222 . the processing and storing functions may be localized within the oct instrument or functions may be performed on an external processing unit to which the collected data is transferred . the processing unit can be separated into multiple processors wherein one or more of the processors can be remote from the oct instrument . the processing unit could be dedicated to data processing or perform other tasks which are quite general and not dedicated to the oct device . the display can also provide a user interface for the instrument operator to control the collection and analysis of the data . the interface could contain knobs , buttons , sliders , touch screen elements or other data input devices as would be well known to someone skilled in the art . the interference causes the intensity of the interfered light to vary across the spectrum . the fourier transform of the interference light reveals the profile of scattering intensities at different path lengths , and therefore scattering as a function of depth ( z - direction ) in the sample . the profile of scattering as a function of depth is called an axial scan ( a - scan ). a dataset of a - scans measured at neighboring locations in the sample produces a cross - sectional image ( slice , tomogram , or b - scan ) of the sample . a collection of b - scans collected at different transverse locations on the sample comprises a 3d volumetric dataset . typically a b - scan is collected along a straight line but b - scans generated from scans of other geometries including circular and spiral patterns are also possible . the sample and reference arms in the interferometer could consist of bulk - optics , fiber - optics or hybrid bulk - optic systems and could have different architectures such as michelson , mach - zehnder or common - path based designs as would be known by those skilled in the art . light beam as used herein should be interpreted as any carefully directed light path . in time - domain systems , the reference arm needs to have a tunable optical delay to generate interference . balanced detection systems are typically used in td - oct and ss - oct systems , while spectrometers are typically used at the detection port for sd - oct systems . the embodiments described herein could be applied to any type of oct system . the disclosed method relates to processing of an oct dataset that can be done on the oct instrument itself or on a separate computer or workstation to which a collected oct dataset is transferred either manually or over a networked connection . embodiments of the present application are designed to analyze an oct dataset to generate images , metrics , or statistics of an oct dataset quality , not necessarily correlated directly to signal - to - noise ratio of the data contained therein or to its intensity , but to the contrast found between various structures . the level of the contrast is important in the ability to separate various detected features found in an oct dataset . a reduction in contrast could be due to , for example , a poorly aligned or defocused oct instrument . thus such a metric is usable as a guide for focusing the apparatus . the disclosed method is applicable to volumetric data ( 3d ) or to b - scans ( 2d ) or to a - scans ( 1d ). the approaches of the disclosed method are designed to be fast , and yield real - time or at least near - real - time results for each b - scan ( dimensions : z = axial , x = transverse / b - scan , y = perpendicular to b - scan slice ). the end - result of this pre - preprocessing step is a metric of data quality , but not one directly related to signal - to - noise ratio or to the intensity of the data , but rather one related to level of the contrast of the oct signal detections of the various tissues ( i . e ., retinal layers ). the ability to segment the various tissues / retinal layers ( segmentability ) is dependent upon the level of contrast between the tissues . another embodiment of this metric , detailed herein below , is that the segmentability can be the sum of the values in a computed gradient image in the axial direction . thus segmentability will produce a low value of the metric for an oct signal with high intensity pixel or voxel values but a low level of contrast : meaning , in a practical sense , blurred edges or poor layer separation . thus the metric could also be used as a guide for oct focusing and one that distinguishes between a true image and its complex conjugate . ( the complex conjugate image of an oct image has the same energy as the true oct image but with lower level of contrast as compared with that of the true oct image .) the metric derived from this algorithm is a measure of feature strength in the oct dataset . ( a feature is defined by its edges and intersection of its edge lines in an oct dataset .) this can be distinguished from the signal strength and snr . the signal strength and snr are global and statistical metrics whereas the segmentability is calculated from the local contrast . for example , a blurry image can have high signal strength with low segmentability due to poor contrast . in any of the various definitions of a ‘ metric ’ given hereinabove , a map of its values can be displayed to the user . a pre - segmentation algorithm that signals bad or even poor data , such as the one described in the present application , would be a valuable feature to reduce the chances that such deficient data has been included in segmentation analysis , thus leading to erroneous results , not necessarily recognizable by the clinician . the disclosed method has been intentionally designed to be simple and fast . there are few parameters to optimize or choose , it uses linear filtering , and real - time implementation is possible . thus automatic acceptance / rejection of b - scans during acquisition is possible . furthermore , segmentability of outer / inner retina divided by the defined centroid or a layer is likely with no additional computation expenditure . another usage of this algorithm is to aid the segmentation and localization of the optic nerve head ( on h ) in an oct - enface image / dataset due to the fact that the onh region shows a low level of contrast which leads to poor segmentability . in one embodiment of the present application suitable to a volumetric ( 3d ) dataset , the approach first involves defining a 2d surface from within a 3d volume dataset of oct data . from this initial 2d surface , based upon either signal features or pre - determined values in the orthogonal dimension , the 2d surface is expanded into a 3d sub - volume . further manipulation of the data of this volume yields statistics and metrics which then can be used to decide if the data themselves ( pixel or voxel values ) are of sufficient quality for segmentation algorithms to produce a reliable result . in this embodiment specific to volumetric ( 3d ) data , a 2d surface is computed based upon selecting a representative value for the location of a certain signal feature in a - scan data . such signal features could correspond to the ilm or to the rpe or other desired detected features . the collective ensemble of these z - positions then defines a 2d surface . from the latter , a z - interval is applied to each determined location or centroid in the z - dimension to inflate this 2d surface into a 3d volume , which is then a sub - dataset of the original 3d volume of an oct dataset . if the dataset is from a b - scan , then a curve is derived from the 2d dataset . a 2d area can be defined by inflating the curve in the orthogonal dimension by pre - determined amounts or from positions of detected features . this 2d sub - dataset is further processed into a map , or metrics , or statistics which can then be displayed and inspected by a clinician or automatically assessed to determine whether the scan meets a certain criterion for the level of contrast so as to decide to perform further processing or a new scan . in the case of a b - scan ( 2d dataset ), a 1d curve is computed based upon similar criteria as given in the previous paragraph . in this embodiment , the collective ensemble of z - positions then defines this 1d curve . from the latter , a z - interval is applied to each determined location or centroid to inflate the curve into a 2d area , which is a sub - dataset of the original b - scan dataset . the basic method of the present application will be outlined herein below , applicable to any of the embodiments described herein of determining the segmentability on an oct full 3d dataset or the embodiment of processing a b - scan ( meaning the volume contains only a 2d surface , a single b - scan , and is filled only in x and z , and y can be considered to be fixed ). the flow charts of fig2 , 3 , and 4 present the overall method to obtain a map or metrics or statistics that can be used to distinguish low contrast data from that of high contrast data . the analysis can be carried out by a processor 221 ( or processors ) that is part of the oct dataset collection system or to which a collected oct dataset is transferred either via network or temporary storage device . fig2 presents the overall method , applicable to either 3d or 2d datasets , with fig3 dealing with the specific processing of a 3d dataset , and fig4 associated with the same steps as fig3 but for a 2d ( b - scan ) dataset . in the case of a b - scan , there is the understanding that the y variable is fixed or can be ignored , thus defining a 2d dataset within a 3d dataset . referring to fig2 , let v ( x , y , z ) be an oct dataset 100 . in the first step 101 of the method , a feature or representative value of the dataset is identified . if z is the axial dimension and x / y are the transverse dimensions , then a representative value of the location of the retina in the axial direction can be derived generally from : for classical centroids , f ( z )= z and v 0 = 0 . v 0 may be a constant in any combination of x , y , z . in this latter case , the centroid is taken as the representative value of the location of the retina or any other desired feature . any representative value of the location of the retina or any other feature in the a - scan is appropriate , such as : an average of the pre - determined retinal minimum and maximum z - values ; any normalized linear combination of these two z - values ; convolving the volumetric intensity data with a non - constant , non - linear functional form ; or , convolving with an f ( z ), but where the intensity of the dataset has had the background level removed . in this last case , a possibility would be to clip the background , and calculate a representative value of the location of the retina on the remaining signal or to binarize it ( thresholding the remaining signal , in which signals above a certain threshold are set to a value of one , and anything less than this threshold is set to zero intensity ). in the next step 102 / 102 a of the method , the representative value can be used to define a sub - dataset within the full dataset . in one embodiment , the centroids or representative values of the location in the a - scan 101 of some anatomical feature can be used to define a surface ( for 3d dataset — fig3 ) or a curve ( 2d dataset — fig4 ) within the original full dataset 100 . a region of interest about each centroid ( along the z - axis ) can then be selected , thus selecting a sub - dataset within the full dataset . a sub - dataset v 1 ( x , y , z ) can be extracted from v ( x , y , z ) within a range along the axial direction ( z ) as follows : z 1 ε [ min ( c ( x , y ))− a max ( c ( x , y ))+ b ] ( 2 ) where a and b are constants or offsets from the minimum and maximum value of centroid c ( x , y ), or the representative value of the location of some anatomical feature . the values of a and b can be estimated from the approximate retinal thickness from an ensemble of oct datasets , from some pre - determined values , or from detected features found within the signal . the next step 103 / 103 a in the method is to create a gradient image along each a - scan by convolving each z - segment of v 1 ( x , y , z ) with a function . the gradient image represents the directional change in the intensity in an image . in this embodiment , the directional change in axial direction is calculated , although in another embodiment presented hereinbelow this technique is generalized to two directions . the absolute value of the gradient image is taken as a local indicator of the local image contrast . the integral of the gradients in the axial direction for a given a - scan indicates the overall contrast of that a - scan . higher local contrast leads to better segmentation in general . in this particular embodiment , the derivative of a gaussian function is used as depicted in fig5 a . the advantage of the use of the gradient image is that it is not dependent upon local absolute intensity values . the created gradient image dataset , v 2 ( x , y , z ), can be represented by : the next step 104 / 104 a is to smooth or reduce the noise in the x - dimension ( along a slice ), at each fixed y - value for each slice ( meaning for each value of y and each value of z ) according to : where g ( x ) is the same functional form as g ( z ) just with x substituted for z and with the same or different standard deviation ( as depicted in fig5 b ). optional smoothing techniques involve standard median or mean filtering . low - pass filtering done in the frequency domain is likewise applicable to this step . besides the use of a derivative gaussian function , optional functions that could be convolved 103 / 103 a with the intensity data in the axial dimension to create a gradient image are prewitt or sobel operators , laplacian , kirsch compass , marr - hildreth , difference of gaussians , laplacian of gaussians , higher - order gaussian derivatives , roberts cross , scharr operator , ricker wavelet , frei - chen or any discrete differentiation operator well known to the ordinary skilled person in the art . additional approaches can use multi - scale techniques such as log - gabor wavelets and phase congruency ( kovesi 2003 ) to generate gradient images and extract image features as well . phase congruency is highly localized and invariant to image contrast which leads to reliable image feature detection under varying contrast and scale . the method of phase congruency applies to features with small derivative or smooth step where other methods have failed . the next step 105 / 105 a in the algorithm is to apply a nonlinear transform function to each absolute value of v 3 ( x , y , z ). the nonlinear transformation is the symmetrized sigmoid function : where α and β are sigmoid function parameters . this function is depicted in fig6 . the resultant dataset is then subjected to a clipping ( or threshold ) value = t 106 / 106 a : meaning only pixels that exceed t are counted in the result . the symbol └ ┘ denotes that the enclosed quantity is equal to itself when its value is positive , and zero otherwise 106 / 106 a . this step eliminates noise as well as facilitating faster processing of the remaining data . an optional approach is to threshold the individual a - scans comprising each b - scan . the next step 107 in the algorithm is to generate a 2d dataset from the volumetric dataset or a 1d dataset ( curve ) from the b - scan dataset by integrating v 5 ( x , y , z ) from the previous step in the z - axis ( axial or depth ) direction as follows : s ( x , y ) =∫ v 5 ( x , y , z ) dz ( 9 ) ( for volumetric 3d data ) s ( x ) =∫ v 5 ( x , 0 , z ) dz ( 10 ) ( for b - scan / 2d data ) in the case of the b - scan , the variable y in equation 10 , is naturally ignored or can be considered to be a constant . s ( x , y ) or s ( x ) is the segmentability map which can be scaled between a range of zero and one . this map represents the local segmentability quality of an oct dataset . moreover , this map can be input to other algorithms to derive additional metrics or to identify a region of interest on a retina such as the optic nerve head ( e . g ., fig7 , 701 ). at this point the surface s ( x , y ) or s ( x ) can be normalized 107 to facilitate display or further processing . fig7 and 8 are segmentability maps that have been generated based on the integration of v 5 along the axial direction and represent the local segmentability quality of an oct volume based on the contrast or level of the contrast of the data . fig7 is the result of applying the process described above to a volume scan containing the optic nerve head ( onh , 701 ). this map can be used to detect the location and to segment the boundary of the onh . fig8 is the result of a volume around the macula . both fig7 and 8 are from scans of a volume 6 × 6 × 2 mm 2 . these figures were generated using a classical centroid of the a - scans and the z - interval about the determined centroid was chosen to be approximately the limiting ilm and the rpe z - positions . fig8 shows an area that cannot be segmented . it is outlined by the two oblique lines 801 . a sequence of images as shown in fig9 demonstrates the sequence of steps in the method for a single b - scan : fig9 a is a slice of intensity data , fig9 b is the convolution of these data with g ′( z ) and g ′( x ), and fig9 c is the threshold image , a slice of v 5 ( x , y , z ). the segmentability map s ( x , y ) can be displayed ( 108 on a display 222 ) to the clinician for review on a user interface and the clinician or instrument operator can indicate acceptance or rejection when optionally prompted for a response . an alternative is to have an automatic rejection or acceptance of the map . the display can be in a full - frame window , or in a sub - window of the corresponding b - scan ( the x and z dimensions displayed ). if the data are rejected either manually or automatically , the scan or scans can be redone 109 . in the case of acceptance 110 , the original dataset v ( x , y , z ) then can undergo pre - processing as outlined in the background section of the present application , and / or then further analysis via segmentation algorithms . image quality statistics can be determined and provided to the clinician on the display 222 from analysis of the aforementioned metrics , e . g ., percentage of a - scans above a certain threshold or location of deficient a - scans to identify potentially diseased areas . weighting the above - threshold number of a - scans , or similarly the below - threshold number of a - scans , with distance from some physical location , such as the fovea or the optical nerve head ( onh ) could serve as a useful diagnostic statistic . the segmentability map can be analyzed 108 to derive metrics and statistics ( which could be displayed to a user ), including but not limited to : 1 ) extracting spatial information such as onh or regions with low segmentability by applying a threshold the values contained within the map ; 2 ) statistical analyses such as moments as classification features could help classify the map which results in acceptance or rejection of the scanned volume ; 3 ) the map could be divided into various sub - maps , or centric subfields , which contains the mean or median segmentability value which then could be display or communicated to the clinician to accept / reject the scan ; 4 ) further analyses of the map could provide simple instructions to the user on potential changes in scan setup that could result in a better image . segmentability can be used as a metric correlated to the focus quality of the optical coherence tomography ( oct ) instrument . in fig1 , the basic steps to achieve the best focus are outlined . the oct system is first initialized ( step 301 ) with control over the position of the ocular / objective lens 302 within the instrument . an acquisition /- processing loop is then entered ( either by manual action or via an automatic procedure ) which takes a b - scan 303 dataset at each position of the ocular lens , processes the dataset ( first step will be that of 101 in fig1 , then the steps 102 a to 106 a according to the flow chart in fig4 ) to compute a segmentability map 304 , sums the segmentability map over s ( x ) 305 with the summed value = f , stores the position of the ocular lens 223 and f 306 , performs a check to see if the lens has reached the end of its longitudinal travel 307 , and if not repeats the steps . once the entire sequence of steps has been performed , the maximum value f is found and its associated value of the position of the ocular lens is determined ( 308 ), and the lens position can be set accordingly ( 309 ). a deficiency in the use of fourier - domain oct , is that its practical use is limited by the complex conjugate ambiguity , which is inherent to real fourier transforms . due to this artifact , an fd - oct image or dataset obtained from a real - valued spectral interferogram will consist of two overlapped images symmetrical with respect to the zero phase delay of the interferometer . thus , only one half of the imaging depth range is useful in practice . resolving the complex conjugate ambiguity would double the imaging depth range and provide additional flexibility by allowing arbitrary positioning of the zero plane , e . g ., moving it inside the sample . it is well known that the complex conjugate image is of low quality , meaning that the level of contrast between its layers or features is substantially lower than the contrast between those equivalent layers found in the real image . thus a measure of the of the level of the contrast via a segmentability map is applicable to distinguish the real image from that of the complex conjugate image . fig1 is a flow chart of the basic procedure for this purpose . a b - scan is performed 401 , and the obtained dataset is processed 402 as indicated by step 101 ( fig1 ) and then steps 102 a - 106 a ( fig4 ). the segmentability map is then summed (= f ) as in the case of the focus procedure described in fig1 403 . if the value of f is greater than a threshold value 404 , the latter of which can be pre - determined from previous analyses , then the image is the real one and leads to further analyses 405 as described in this application . if the value of f is less than the threshold value , then the complex conjugate image has been identified and the appropriate reconfiguration of the instrument can occur 406 , which is normally an adjustment of the zero delay point , and then the scan is repeated 401 . another embodiment of the present application is a methodology to automatically classify the metric or metrics of segmentability into sharp vs blurred categories . there is also a third possibility — neither ; in other words , noise or background , possessing little signal . the axial scan ( either gradient or intensity ) is divided into portions or segments , as a function of the z - dimension . for each of these portions , one or more metrics , as described previously , are computed . from the ensemble of metrics obtained from a collection of a - scans , an unsupervised classifier is used to segregate the metrics into different types or levels and as a function of the axial dimension . for a sharp structure in the a - scan , e . g . a real image , a metric would possess a value distinct from that of one which was derived from a more blurred structure found in a different part of that a - scan ( i . e ., complex conjugate image ). noise or background would possess a third characteristic , one which is referred to here as ‘ neither .’ in the case of focus , however , blurring an image by defocus would affect both real and complex conjugate images . the metrics derived from real and complex conjugate images respectively would approach each other in value . if the blurring was sufficiently strong , then the respective metrics would nearly be the same . thus by comparing the statistics of the metrics , the blurring effects of focus can be separated from the blurred images of the complex conjugate . once these metrics have been classified , additional processing of the results can occur , such as taking a ratio of the mean value of one isolated distribution ( or clustering of values ) to that of another , or by partitioning of the data into dirichlet tessellations ( see , e . g ., okabe et al . 2000 ), or by providing a weighted composite metric . this will help identify more clearly the relative contrast of the various signals derived from the a - scan . by identification of which portion of the a - scan ( or a - scans ) correspond to the complex - conjugate image , then the zero delay position can be altered and the data re - acquired . there are two types of learning that are commonly used in statistical analyses : supervised and unsupervised . from a theoretical point of view , supervised and unsupervised learning differ only in the causal structure of the model . in supervised learning , the model defines the effect of one set of observations , called inputs , has on another set of observations , called outputs . in other words , the inputs are assumed to be at the beginning and outputs at the end of the causal chain . the models can include mediating variables between the inputs and outputs . in unsupervised learning , all the observations are assumed to be caused by latent variables , that is , the observations are assumed to be at the end of the causal chain . in practice , models for supervised learning often leave the probability for inputs undefined . this model is not needed as long as the inputs are available , but if some of the input values are missing , it is not possible to infer anything about the outputs . if the inputs are also modelled , then missing inputs cause no problem since they can be considered latent variables as in unsupervised learning . the goal of unsupervised learning is to build representations of the input data that can be used for decision making . it can be thought of as finding patterns in the input data above and beyond that which would be considered pure unstructured noise . two examples of unsupervised learning are clustering and dimensional reduction . moreover , almost all work in unsupervised learning can be viewed in terms of learning a probabilistic model of the data . thus such a probabilistic model can be used for classification . ( for an introduction to statistical learning techniques , please see , hastie et al . 2009 .) once the data have been collected and the measured metrics segregated by the unsupervised classifier , then additional collection of data to establish the relationship between metrics of sharp images , those of blurred image , and those which are neither , will be less important . in an embodiment , the unsupervised classifier may automatically organize the a - scans or metrics derived therefrom on the basis of at least one clustering algorithm . the unsupervised classifier may automatically organize the a - scans or metrics derived from each a - scan or subimages thereof on the basis of at least one of the following : self - organizing map of neural computing , t - distributed stochastic neighbor embedding , principal component analysis , sammon mapping method , gtm ( general topographic mapping ), lle ( locally linear embedding ) mapping , isomap , agglomerative or hierarchial hierarchal clustering , including single - link -, complete - link -, average - link clustering , clustering error minimization , distance error minimization , k - means clustering , k - method , and graph - based methods like single - or complete link clustering , density based method , density - based spatial cluster of applications with noise ( dbscan ), autoclass , snob , birch , mclust , or model based clustering cobweb or classit , simulated annealing for clustering , genetic algorithms , bayesian method , kernel method , multidimensional scaling , principal curve , t - sne , some of their combination or the like . ( for an introduction to these and other clustering algorithms , please see duda et al . 2001 .) in supervised learning , the statistical classifier could be any of the following , or even any combination of the following : artificial neural network , bayesian statistics , hopfield networks , boltzmann machines , decision trees , inductive logic programming , random forests , ordinal classification , regression analysis , fuzzy networks or clustering , conditional random field , support vector machine , multilayer perception network , principal component analysis , hybrid realization , and symbolic machine learning . in addition , the person having ordinary skill in the art would readily recognize alternatives to these listed algorithms , as well as those alternatives for the unsupervised classifying algorithms . upon automatic organization of the metrics , a - scans , or their subimages , by the unsupervised classifier , and the various distributions identified , a classification score can be assigned . this score represents a distribution of input values . the classification scores will be related to the level of contrast , which might be caused as focused , unfocused , real , or complex conjugate images , noise ( or none - of - the - above ), or any combination of these characteristics . in the simplest case , these scores would be sharp , blurred , and neither . the score can be interpreted as a probability and as such the sum total of scores should be one . an additional embodiment would be defining a metric based upon a measure of the breadth of the autocorrelation function . examples would be its standard deviation , a measure of its width ( e . g ., fwhm ), or any measure of the sharpness of the autocorrelation function . although various applications and embodiments that incorporate the teachings of the present method have been shown and described in detail herein , those skilled in the art can readily devise other varied embodiments that still incorporate these teachings . tan et al . 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