Improved eye aberration measurement and restoration system and method

Abstract

Techniques for systems and methods for providing improved ocular aberrometry recovery are disclosed. An ocular aberrometry system (100) includes a wavefront sensor (120) configured to provide wavefront sensor data associated with an optical target (102) monitored by the ocular aberrometry system (100) and a logic device (140) configured to communicate with the wavefront sensor (120). The logic device (140) is configured to receive (802) ocular aberrometry output data comprising at least the wavefront sensor data provided by the wavefront sensor (120), identify (806) singularities caused by imaging artifacts in the received ocular aberrometry output data, determine (808) corrected aberrometry output data based at least in part on the identified singularities and the received ocular aberrometry output data, and generate (810) user feedback corresponding to the received ocular aberrometry output data based at least in part on the corrected aberrometry output data.

Description

Technical Field

[0001] One or more embodiments of the present disclosure relate generally to ocular aberrometry, and more particularly, for example, to systems and methods for improving clinical or intraoperative ocular aberrometry. Background Art

[0002] Eye surgery can include reshaping the cornea and / or surface of the eye, inserting and / or replacing intraocular devices and / or artificial intraocular lenses (IOLs), and / or other surgical manipulations of the active optical components of the eye. To achieve the best postoperative visual outcome, good preoperative clinical evaluation and surgical planning, as well as intraoperative monitoring of the execution of the surgical plan, are crucial.

[0003] Ocular aberrometers, as performed by ocular aberrometers, are typically a common method used to characterize the eye prior to surgery, monitor surgical progress, and assess surgical success. Conventional ocular aberrometers often suffer from various measurement errors associated with eye movement and / or misalignment and optical aberrations of the aberrometer itself, which can lead to inaccurate surgical planning and suboptimal surgical or visual outcomes for patients. Furthermore, conventional ocular aberrometers often combine measurement and characterization techniques that can introduce inaccuracies into the resulting aberrational characterization of the eye, thereby complicating the recovery of accurate aberration measurements of the eye.

[0004] Therefore, there is a need in the art for systems and methods for improving clinical and / or intraoperative ocular aberration measurements to provide optimized surgical or visual outcomes for patients. Summary of the Invention

[0005] Technology is disclosed for systems and methods for providing improved ocular aberration measurement. According to one or more embodiments, an ocular aberration measurement system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration measurement system and a logic device configured to communicate with the wavefront sensor. The logic device may be configured to determine a complex analysis engine for the ocular aberration measurement system based at least in part on an aberrometer model and / or an eye model associated with the ocular aberration measurement system, wherein the aberrometer model and the eye model are based at least in part on the wavefront sensor data provided by the wavefront sensor. The logic device may also be configured to generate a compact analysis engine for the ocular aberration measurement system based at least in part on the determined complex analysis engine.

[0006] In other embodiments, a method may include determining a complex analysis engine for an eye aberration measurement system based at least in part on an aberrometer model and / or an eye model associated with the eye aberration measurement system, wherein the aberrometer model and the eye model are at least in part based on wavefront sensor data provided by a wavefront sensor of the eye aberration measurement system; and generating a compact analysis engine for the eye aberration measurement system based at least in part on the determined complex analysis engine.

[0007] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions that, when executed by one or more processors, are adapted to cause the one or more processors to perform a method. The method may include determining a complex analysis engine for an eye aberration measurement system based at least in part on an aberrometer model and / or an eye model associated with the eye aberration measurement system, wherein the aberrometer model and the eye model are based at least in part on wavefront sensor data provided by a wavefront sensor of the eye aberration measurement system; and generating a compact analysis engine for the eye aberration measurement system based at least in part on the determined complex analysis engine.

[0008] In a further embodiment, an ocular aberrometry system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberrometry system and a logic device configured to communicate with the wavefront sensor. The logic device may be configured to receive ocular aberrometry output data including at least the wavefront sensor data provided by the wavefront sensor, determine an estimated ocular alignment deviation corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberrometry system based at least in part on the received ocular aberrometry output data, and generate user feedback corresponding to the received ocular aberrometry output data based at least in part on the estimated ocular alignment deviation.

[0009] In other embodiments, the method may include receiving eye aberration measurement output data from an eye aberration measurement system including a wavefront sensor, wherein the eye aberration measurement output data includes at least wavefront sensor data associated with an optical target monitored by the eye aberration measurement system, determining an estimated eye alignment deviation corresponding to the relative position and / or orientation of the optical target monitored by the eye aberration measurement system based at least in part on the received eye aberration measurement output data, and generating user feedback corresponding to the received eye aberration measurement output data based at least in part on the estimated eye alignment deviation.

[0010] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions that, when executed by one or more processors, are adapted to cause the one or more processors to perform a method. The method may include receiving ocular aberration measurement output data from an ocular aberration measurement system including a wavefront sensor, wherein the ocular aberration measurement output data includes at least wavefront sensor data associated with an optical target monitored by the ocular aberration measurement system, determining an estimated ocular alignment deviation corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberration measurement system based at least in part on the received ocular aberration measurement output data, and generating user feedback corresponding to the received ocular aberration measurement output data based at least in part on the estimated ocular alignment deviation.

[0011] In a further embodiment, an ocular aberrometry system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberrometry system and a logic device configured to communicate with the wavefront sensor. The logic device may be configured to receive ocular aberrometry output data including at least the wavefront sensor data provided by the wavefront sensor, identify singularities caused by imaging artifacts in the received ocular aberrometry output data, determine corrected aberrometry output data based at least in part on the identified singularities and the received ocular aberrometry output data, and generate user feedback corresponding to the received ocular aberrometry output data based at least in part on the corrected aberrometry output data.

[0012] In other embodiments, a method may include receiving ocular aberrometry output data from an ocular aberrometry system including a wavefront sensor, wherein the ocular aberrometry output data includes at least wavefront sensor data associated with an optical target monitored by the ocular aberrometry system, identifying singularities caused by imaging artifacts in the received ocular aberrometry output data, determining corrected aberrometry output data based at least in part on the identified singularities and the received ocular aberrometry output data, and generating user feedback corresponding to the received ocular aberrometry output data based at least in part on the corrected aberrometry output data.

[0013] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions that, when executed by one or more processors, are adapted to cause the one or more processors to perform a method. The method may include receiving ocular aberrometric output data from an ocular aberrometric system including a wavefront sensor, wherein the ocular aberrometric output data includes at least wavefront sensor data associated with an optical target monitored by the ocular aberrometric system, identifying singularities caused by imaging artifacts in the received ocular aberrometric output data, determining corrected aberrometric output data based at least in part on the identified singularities and the received ocular aberrometric output data, and generating user feedback corresponding to the received ocular aberrometric output data based at least in part on the corrected aberrometric output data.

[0014] The scope of the present invention is defined by the claims, which are incorporated herein by reference. By considering the following detailed description of one or more embodiments, those skilled in the art may more fully understand the embodiments of the present invention and may realize additional advantages of the present invention. Reference will be made to the attached sheets of the drawings, which will first be briefly described. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A block diagram of an eye aberration measurement system according to an embodiment of the present disclosure is illustrated.

[0016] Figures 2A to 2B A block diagram of an aberration measurement characterization target of an ocular aberration measurement system according to an embodiment of the present disclosure is illustrated.

[0017] Figure 3 A block diagram of an eye aberration measurement system according to an embodiment of the present disclosure is illustrated.

[0018] Figure 4 A flow chart illustrating a process of characterizing an ocular aberration measurement system according to an embodiment of the present disclosure is illustrated.

[0019] Figure 5 A flow chart illustrating a process of operating an ocular aberration measurement system according to an embodiment of the present disclosure is illustrated.

[0020] Figure 6 Illustrated is a diagram of a multi-layer neural network according to an embodiment of the present disclosure.

[0021] 7A to 7C A process diagram illustrating a process for reducing errors caused by imaging artifacts in an ocular aberration measurement system according to an embodiment of the present disclosure is illustrated.

[0022] Figure 8 A flow chart illustrating a process of operating an ocular aberration measurement system according to an embodiment of the present disclosure is illustrated.

[0023] Embodiments of the present invention and their advantages may be best understood by referring to the following detailed description.It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures. DETAILED DESCRIPTION

[0024] According to various embodiments of the present disclosure, ocular aberration measurement systems and methods provide substantially real-time measurement and monitoring of aberrations of a patient's eye, reducing the systematic and measurement errors typical of conventional systems. For example, as a patient's eye is fixated during an ocular aberration measurement or examination, the eye naturally drifts. This gaze drift includes eye rolling, squinting, twisting, and changes in x, y, and z position of the eye. These misalignment deviations can lead to errors in the calculation of wavefront characterizing aberrations of the eye. If the average value of these misalignment deviations is close to zero, averaging the wavefront aberration measurements of each frame of the examination image stream is sufficient to eliminate most of the errors caused by misalignment. When it is known that the mean of the alignment deviations of an examination sequence is not near zero, or it is not possible to confirm that the alignment deviations have a mean near zero, various strategies described herein can be used to reduce measurement error or noise, including one or a combination of averaging wavefront sensor data (e.g., represented by a Zernike polynomial expansion) where the alignment deviations do not exceed a preset threshold, using complex analytical methods (described herein) to correct for misalignment-based errors in the wavefront sensor data, determining the gaze state of the eye (e.g., for each wavefront measurement) based on cluster analysis applied to a series of wavefront measurements and / or based on eye tracker data. The estimated alignment deviation and / or its effect on the wavefront measurement can be reduced to an eye alignment deviation metric and provided to a user of the ocular aberrometer system as a display view with a corresponding graphic, or used to cause the ocular aberrometer system to ignore images from the examination image sequence or to abort and / or restart the examination. In this way, embodiments provide substantially real-time monitoring feedback while providing more reliable and accurate aberrometer measurements than conventional systems, such as by reducing variability in clinical and intraoperative ocular aberrometer examinations due to eye movement during the examination image sequence.

[0025] In additional embodiments of the present disclosure, ocular aberration measurement systems and methods provide a platform and techniques to accurately characterize system aberrations and correct wavefront sensor data that would otherwise be degraded by alignment errors associated with a patient's eye. For example, to account for possible errors caused by system aberrations or generated during eye movement, the ocular aberration measurement systems described herein can employ one or more of two unique calibration modalities for accurate high-order aberration (HOA) analysis: a system characterization process, and a complex analysis training process.

[0026] For the system characterization process, the ocular aberrometry system can be used to measure a series of reference interferograms (e.g., in the form of wavefront sensor data) generated by a phantom target that is configured to present substantially a single type of variable aberration (e.g., defocus aberration) to the ocular aberrometry system with substantially zero alignment deviation. The reference interferograms can be used to characterize and / or quantify any systematic aberrations of a particular ocular aberrometry system, which can be used to correct the wavefront sensor data provided by the wavefront sensor of the particular ocular aberrometry system, such as by removing the systematic aberrations prior to subsequent analysis.

[0027] For a complex analysis training process, an eye aberrometer system can be used to capture a set of wavefront measurements generated using a model target that is configured to present a selection of aberrations of different types and varying intensities to the eye aberrometer system with variable alignment deviations in eye roll, squint, twist, and x, y, and z positions of the aberration elements of the model target (e.g., Zernike expansion coefficients up to 6th order). The wavefront measurement sets can be used to train and / or improve a complex analysis engine performed by the eye aberrometer system, and, for example, be configured to generate substantially accurate estimates of alignment deviations and / or corrected wavefront sensor data based on uncorrected wavefront sensor data, or a combination of uncorrected wavefront sensor data and eye tracker data as described herein. In this way, embodiments provide more reliable and accurate aberrometer measurements than conventional systems, such as by increasing the precision and accuracy of aberrometer measurements due to reducing errors caused by system aberrations and off-axis or skew eye aberrometer measurements. Furthermore, embodiments provide a more robust (e.g., reliable and fast) aberration measurement system by increasing the range of alignment deviations that can be accurately compensated or corrected and therefore included in a particular inspection (e.g., present and / or detected in a given image of an inspection image sequence).

[0028] In other embodiments of the present disclosure, ocular aberration measurement systems and methods provide techniques for correcting wavefront sensor data that would otherwise be degraded by imaging artifacts associated with a patient's eye. For example, to account for possible errors caused by imaging of vitreous bubbles and / or other image artifacts during an eye characterization procedure, the ocular aberration measurement systems described herein can decompose wavefront sensor data (e.g., a representation of an interferogram of the eye) into a form including a phase component (e.g., a spatial phase component) that can be weighted to reduce or eliminate portions of the wavefront sensor data that contribute to the error.

[0029] Figure 1 FIG. 1 illustrates a block diagram of an eye aberration measurement system 100 according to an embodiment of the present disclosure. Figure 1In the illustrated embodiment, the ocular aberration measurement system 100 can be implemented to provide substantially real-time (e.g., 30 Hz update) monitoring of an optical target 102 (e.g., a patient's eye) while continuously compensating for common characterization errors, such as patient motion, system optical aberrations, thermal variations, vibrations, and / or other characterization errors that would otherwise degrade the ocular aberration measurements provided by the ocular aberration measurement system 100.

[0030] like Figure 1 As shown, the eye aberration measurement system 100 includes a beacon 110 that generates a probe beam 111 for illuminating an optical target 102 for a wavefront sensor 120 and / or other components of the eye aberration measurement system 100. The eye aberration measurement system 100 may also include various other sensor-specific beacons and / or light sources, such as a light emitting diode (LED) array 132 for illuminating the optical target 102 for an eye tracker 130, and an OCT beacon 123 for generating an OCT probe beam 124 for illuminating the optical target 102 for an OCT sensor 122. Beam splitters 112-116 are used to provide the probe beam 111 to the optical target 102 and to generate associated sensor beams 113, 115, 117 originating from the optical target 102 (e.g., the probe beam 111, the OCT probe beam 124, and a portion of the light generated by the LED array 132 and reflected by the optical target 102). The beacon 110, OCT beacon 123, LED array 132, and each sensor element of the ocular aberration measurement system 100 can be controlled by a controller 140 (e.g., via communication links 141-144), and the controller 140 can also serve as an interface between the beacon 110, OCT beacon 123, LED array 132, and sensor elements of the ocular aberration measurement system 100 and other elements of the ocular aberration measurement system 100, including a user interface 146, a server 150, a distributed server 154, and other modules 148 as shown (e.g., accessed via optional communication links 145, 149, and 155).

[0031] In typical operation, the controller 140 initializes one or more of the wavefront sensor 120, the optional OCT sensor 122, and the optional eye tracker 130, controls the beacon 110, the OCT beacon 123, and / or the LED array 132 to illuminate the optical target 102, and receives ocular aberration measurement output data (e.g., wavefront sensor data, eye tracker data, OCT sensor data) from the various sensor elements of the ocular aberration measurement system 100. As described herein, the controller 140 may process the ocular aberration measurement output data itself (e.g., to detect or correct alignment deviations and / or extract aberration parameters from the wavefront sensor data) or may provide the ocular aberration measurement output data to the server 150 and / or the distributed server system 154 (e.g., via the network 152) for processing. The controller 140 and / or the server 150 can be configured to receive user input at the user interface 146 (e.g., to control the operation of the ocular aberrometry system 100) and / or generate user feedback for display to the user via a display of the user interface 146, such as a display view of ocular aberrometry output data and / or features of the ocular aberrometry output data as described herein. The controller 140, the server 150, and / or the distributed server system 154 can be configured to store, process, and / or otherwise manipulate data associated with the operation and / or characterization of the ocular aberrometry system 100, for example, including machine learning and / or training complex analysis engines (e.g., neural network-based classification and / or regression engines) to characterize system aberrations of the ocular aberrometry system 100, detect alignment deviations associated with the optical target 102, and / or correct wavefront sensor data and / or associated aberration classification coefficients as described herein. In various embodiments, the ocular aberration measurement system 100 can be configured to provide substantially real-time monitoring and user feedback (e.g., 30 Hz or higher frequency updates) of optical aberration measurements of the optical target 102 while continuously compensating for common characterization errors as described herein, which makes the ocular aberration measurement system 100 particularly suitable for clinical and intraoperative examinations.

[0032] The beacon 110 can be implemented as a laser source (e.g., producing significantly coherent light) and / or a superluminescent diode (e.g., an "SLD" producing relatively low coherent light), which can be controlled by the controller 140 to produce a probe beam 111 primarily for the wavefront sensor 120. The OCT beacon 123 can be implemented as a laser source and / or a superluminescent diode (e.g., producing relatively low coherent light, which is particularly suitable for use with the OCT sensor 122), which can be controlled by the controller 140 to produce an OCT probe beam 124 primarily for the OCT sensor 122. In various embodiments, the OCT beacon can be integrated with the OCT sensor 122, as shown, can be integrated with the beacon 110, for example, and / or can be implemented as its own stand-alone beacon, similar to the beacon 110 (e.g., using an appropriate beam splitter arrangement). The LED array 132 can be implemented as a shaped or patterned LED array, which can be controlled by the controller 140 to illuminate the target 102 primarily for the eye tracker 130. The beam splitters 112-116 can be implemented by any of a number of optical components (e.g., pellicle beam splitters, mirrored surfaces) configured to aim the probe beam 111 at and / or pass through the optical target 102 and to steer at least a portion of the probe beam 111 and / or a source beam generated by the optical target 102 (e.g., the probe beams 111 or 124 and / or a reflected portion of light emitted from the LED array 132) toward various sensor elements of the ocular aberration measurement system 100 to form sensor beams 113-117 (e.g., sensor beams). The optical target 102 can be, for example, a patient's eye, or can be implemented by a single-pass (probe beam 111 is off and the optical target 102 generates its own illumination) or double-pass (e.g., normal operation with the probe beam 111 on) model target, for example, as described herein.

[0033] The wavefront sensor 120 can be implemented as any one or combination of devices or device architectures configured to measure aberrations of an optical wavefront (e.g., an optical wavefront of a sensor beam 117 generated by at least one reflection of the probe beam 111 from the optical target 102), and can be configured to provide associated wavefront sensor data. For example, the wavefront sensor 120 can be implemented as a Shack-Hartmann wavefront sensor, a phase-shifted schlieren wavefront sensor, a wavefront curvature sensor, a pyramid wavefront sensor, a common-path interferometer, a multi-edge shearing interferometer, a Ronchi meter, a shearing interferometer, and / or any other wavefront sensor configured for ophthalmology. The wavefront sensor data provided by the wavefront sensor 120 can be represented in a variety of formats, including Zernike coefficients, e.g., Fourier, cosine, or Hartley transforms, or Taylor polynomials in cylindrical or Cartesian coordinates, or as an interferogram.

[0034] The OCT sensor 122 can be implemented as any one or combination of devices or device architectures configured to capture two-dimensional and three-dimensional images with micron and / or sub-micron resolution from within an optically scattering medium (such as the optical target 102) using relatively low coherence light and low coherence interferometry, and configured to provide associated OCT sensor data. For example, the OCT sensor 122 can be implemented as any one or combination of OCT sensor architectures that can be configured for ophthalmology. The eye tracker 130 can be implemented as any one or combination of devices or device architectures configured to track the orientation and / or position of the optical target 102 and / or features of the optical target 102 (e.g., retina, pupil, iris, cornea, lens), including conventional eye trackers or fundus cameras, and configured to provide associated eye tracker data. In some embodiments, the eye tracker 130 can be configured to capture images of one or more types of Purkinje reflexes associated with the target 102.

[0035] The controller 140 may be implemented as any suitable logic device (e.g., a processing device, a microcontroller, a processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a memory storage device, a memory reader, or other device or combination of devices) that may be adapted to execute, store, and / or receive appropriate instructions, such as software instructions that implement control loops or processes for controlling, for example, various operations of the ocular aberration measurement system 100 and / or elements of the ocular aberration measurement system 100. Such software instructions may also implement methods for processing sensor signals, determining sensor information, providing user feedback (e.g., via the user interface 146), querying operating parameters of the device, selecting operating parameters of the device, or performing any of the various operations described herein (e.g., operations performed by the logic devices of the various devices of the ocular aberration measurement system 100).

[0036] In addition, a machine-readable medium may be provided for storing non-transitory instructions for loading into and executing by the controller 140. In these and other embodiments, the controller 140 may be implemented with other components, as appropriate, such as volatile memory, non-volatile memory, one or more interfaces, and / or various analog and / or digital components for interfacing with devices of the ocular aberration measurement system 100. For example, the controller 140 may be adapted to store sensor signals, sensor information, complex analysis parameters, calibration parameters, calibration point sets, training data, reference data, and / or other operating parameters, for example, over time, and to provide such stored data to other components of the ocular aberration measurement system 100. In some embodiments, the controller 140 may be integrated with a user interface 146.

[0037] The user interface 146 can be implemented as one or more of a display, a touch screen, a keyboard, a mouse, a joystick, a knob, a virtual reality head-mounted device, and / or any other device capable of accepting user input and / or providing feedback to the user. In various embodiments, the user interface 146 can be adapted to provide user input to other devices of the ocular aberration measurement system 100 (such as the controller 140). The user interface 146 can also be implemented with one or more logic devices, which can be adapted to execute instructions, such as software instructions, to implement any of the various processes and / or methods described herein. For example, the user interface 146 can be adapted to form a communication link, send and / or receive communications (e.g., sensor data, control signals, user input, and / or other information), determine parameters of one or more operations, and / or perform various other processes and / or methods described herein.

[0038] In some embodiments, the user interface 146 can be adapted to accept user input, for example, to establish a communication link (e.g., to the server 150 and / or the distributed server system 154), select specific parameters for the operation of the ocular aberration measurement system 100, select a method for processing sensor data, adjust the position and / or orientation of an articulated phantom target, and / or otherwise facilitate the operation of the ocular aberration measurement system 100 and devices within the ocular aberration measurement system 100. Once the user interface 146 accepts the user input, the user input can be transmitted to other devices of the system 100 via one or more communication links. In one embodiment, the user interface 146 can be adapted to display a time series of various sensor data and / or other parameters as part of a display of a graph or map including such data and / or parameters. In some embodiments, the user interface 146 can be adapted to accept user input to, for example, modify a control loop or process parameter of the controller 140 or a control loop or process parameter of any other element of the ocular aberration measurement system 100.

[0039] The other modules 148 may include any one or combination of sensors and / or devices configured to facilitate operation of the ocular aberrometry system 100. For example, the other modules 148 may include a temperature sensor configured to measure one or more temperatures associated with operation of one or more elements of the ocular aberrometry system 100, a humidity sensor configured to measure ambient humidity surrounding the ocular aberrometry system 100, a vibration sensor configured to measure the amplitude and / or presence of vibrations associated with operation of the ocular aberrometry system 100, a patient sensor configured to measure posture, motion, or other characteristics of the patient-supplied optical target 102, and / or other sensors capable of providing sensor data that helps facilitate operation of the ocular aberrometry system 100 and / or correct for common systematic errors typical of operation of the ocular aberrometry system 100. In additional embodiments, the other modules 148 may include an additional lighting and camera system, similar to the combination of the LED array 132 and the eye tracker 130, configured to capture images of one or more types of Purkinje reflexes associated with the target 102.

[0040] For example, the server 150 can be implemented similarly to the controller 140 and can include various elements of a personal computer or server computer for storing, processing, and / or otherwise manipulating relatively large data sets associated with one or more patients, for example, including relatively large training data sets as described herein, to train neural networks or implement other types of machine learning. For example, the distributed server system 154 can be implemented as a distributed combination of multiple embodiments of the controller 140 and / or server 150 and can include networking and storage devices and capabilities configured to facilitate storage, processing, and / or other manipulation of relatively large data sets (including relatively large training data sets as described herein) to train neural networks or implement other types of machine learning in a distributed transaction. The network 152 can be implemented as one or more of a wired and / or wireless network, a local area network, a wide area network, the Internet, a cellular network, and / or according to other network protocols and / or topologies.

[0041] Figures 2A to 2B FIG2 illustrates a block diagram of aberration measurement characterization targets 202A-B for an ocular aberration measurement system 100 according to an embodiment of the present disclosure. Figure 2A In the illustrated embodiment, the aberration measurement characterization target 202A may be implemented as a single-pass model target configured to characterize optical aberrations associated with the ocular aberration measurement system 100. Figure 2A As shown, the aberration measurement characterization target / single-pass model target 202A includes a laser or SLD source 260, which generates a source beam 211 through a lens system 262 and is directed along the optical axis of the optical aberration system 100 (e.g., aligned with the probe beam 111 exiting the beam splitter 116). In various embodiments, for example, the source 260 can be configured to generate a diverging spherical wavefront with controllable vergence power, a converging spherical wavefront with controllable vergence power, or a plane wave with zero power. The lens system 262 is coupled to a linear motion actuator 264 via a mount 265, which allows the lens system 262 to be moved along its optical axis to vary the defocus aberration of the single-pass model target 202A to generate a plurality of reference interferograms and corresponding wavefront sensor data (e.g., provided by the wavefront sensor 120). For example, such reference interferograms and / or corresponding wavefront sensor data can be aggregated and stored as Figure 3 The aberrometer model 360 is configured to correct for system aberrations associated with the ocular aberration measurement system 100 as described herein.

[0042] In some embodiments, the lens system 262 can be implemented as a National Institute of Standards and Technology (NIST) trackable lens, and the linear motion actuator 264 can be implemented as a relatively high precision actuator stage configured to position the lens system 262 at a set of positions spaced apart from the source 260 to generate a source beam 211 having known and predefined defocus powers (e.g., defocus aberrations), such as -12 to 6 diopters or 0 to +-5 diopters, for example, in steps of 5.0D or at higher resolutions, according to the range of defocus aberrations typically experienced by patients monitored by the ocular aberration measurement system 100. The resulting aberrometer model 360 can be used to compensate for various system aberrations, including those attributable to shot noise, thermal variations, and vibrations.

[0043] like Figure 2B As shown, the aberration measurement characterization target / dual-path model target 202B includes an interchangeable eye aberration model 270 that is releasably coupled to a six-degree-of-freedom (6DOF) motion actuator 272 via one or more mounts 273, wherein the 6DOF motion actuator 272 is configured to change the position and / or orientation of the interchangeable eye aberration model 270 to generate a plurality of selected (e.g., known) alignment deviations (e.g., relative to the optical axis of the eye aberration measurement system 100) and a corresponding plurality of wavefront sensor data sets. For example, such alignment deviations and corresponding wavefront sensor data may be aggregated and stored as Figure 3 The eye model 370 is used to train a complex analysis engine or a compact analysis engine as described herein to detect alignment deviations associated with the eye target 102 and correct the corresponding wavefront sensor data.

[0044] In some embodiments, the interchangeable ocular aberration model 270 can form one element of a set of interchangeable ocular aberration models, each model being formed with precise amounts of predefined ocular aberrations (e.g., represented by precise and predefined Zernike coefficient magnitudes, such as the magnitude of the Zernike expansion to the 6th order expressed in microns). In one embodiment, such an interchangeable ocular aberration model can be cut on a contact lens lathe using transparent poly(methyl methacrylate) (PMMA). More generally, such an interchangeable ocular aberration model can be measured by a 3rd party profilometer with traceability to NIST for ground truth comparison with measurements performed by the ocular aberration measurement system 100. In various embodiments, the 6DOF motion actuator 272 can be configured to provide micron-resolution positioning of the interchangeable ocular aberration model 270 along the x, y, and z axes, and micro-radian orientation of the interchangeable ocular aberration model 270 about the θy (eye roll), θx (squint), and θz (twist) directions, as shown in representative coordinate systems 280A-B.

[0045] In general operation, each interchangeable eye aberration model 270 in the group (e.g., a group of 10 or more interchangeables) can be sequentially mounted to the 6DOF motion actuator 272, and the controller 140 can be configured to control the 6DOF motion actuator 272 to position or orient the interchangeable eye aberration model 270 at a set of relative positions and / or orientations (e.g., relative to the optical axis of the eye aberration measurement system 100) within a range of alignment deviations typically experienced by a patient monitored by the eye aberration measurement system 100. In one embodiment, the alignment deviation set can include approximately 40,000 different alignment deviations. In various embodiments, combining the alignment deviation set with a corresponding wavefront sensor data set (e.g., provided by the wavefront sensor 120) can form a supervised data set (e.g., the eye model 370) that can be used to determine a complex or compact analysis engine as described herein. Such an analysis engine can be used to compensate for alignment deviations of the optical target 102, which can also be measured by the eye tracker 130. More generally, the combination of characterizations performed by aberration measurement characterization targets 202A-B may be used to compensate for or correct both systematic aberrations and errors in wavefront sensor data caused by misalignment of optical target 102 .

[0046] Figure 3 FIG. 3 illustrates a block diagram of an eye aberration measurement system 300 according to an embodiment of the present disclosure. Figure 3 In the illustrated embodiment, the ocular aberrometry system 300 can be configured to use characterization data generated by the ocular aberrometry system 100 via the aberration measurement characterization targets 202A-B to generate a complex analysis engine 350 and / or a compact analysis engine 340 that can be used during operation of the ocular aberrometry system 100 to provide substantially real-time monitoring and user feedback (e.g., 30 Hz or higher frequency updates) of optical aberration measurements of the optical target 102 while continuously compensating for common characterization errors as described herein.

[0047] like Figure 3As shown, ocular aberrometry system 300 is similar to ocular aberrometry system 100, but with additional details regarding various data structures and executable program instructions used in the operation of ocular aberrometry system 100 or 300. For example, controller 140 is shown implemented with a compact analysis engine 340, and server 150 and distributed server system 154 are each shown implemented with or storing one or more of an aberrometer model 360, an eye model 370, training data 392, a supervised learning engine 390, a complex analysis / neural network engine 350, and the compact analysis engine 340. Dashed lines generally indicate optional storage and / or implementation of particular elements, but in various embodiments, each of controller 140, server 150, and distributed server system 154 may implement or store any of the identified elements and / or additional elements as described herein.

[0048] In general, the aberrometer model 360 can be generated by aggregating sensor data associated with the use of the single-pass model target 202A to characterize the ocular aberrometer measurement system 100, the eye model 370 can be generated by aggregating sensor data associated with the use of the two-pass model target 202B to characterize the parameter space associated with the optical target 102, and the training data 392 can be generated by combining the aberrometer model 360 and / or the eye model 370 and / or by generating and aggregating simulated training data sets, as described herein with respect to Figure 6 . The supervised learning engine 350 can be implemented as a static learning engine and / or as a learning engine generated according to a program configured to generate the complex analysis engine 350 using the training data 392 (e.g., a genetic algorithm updateable learning engine). For example, the complex analysis engine 350 can be implemented as a deep neural network, and / or can be implemented using other complex analysis methodologies, including various other neural network architectures or complex analysis methodologies, including dense K-nearest neighbor (k-NN) databases for classification and / or regression as described herein. The compact analysis engine 340 can be implemented as a compact form of the complex analysis engine 350, such as a relatively low-resource but high-performance form that is more suitable for execution by the controller 140, and as such can be implemented as a deep neural network and / or other complex analysis methodologies. In particular embodiments, the compact analysis engine 340 can be implemented as a neural network having fewer hidden layers and / or neurons / layers than the complex analysis engine 350.

[0049] Figure 4 A flow chart of a process 400 for characterizing an ocular aberration measurement system 100 and / or 300 according to an embodiment of the present disclosure is illustrated. It should be understood that any step, sub-step, sub-process, or block of the process 400 may be performed in different ways. Figure 4For example, in other embodiments, one or more blocks may be omitted from or added to the process. In addition, block inputs, block outputs, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to subsequent portions of the corresponding process. Although process 400 is a block diagram of a process, the process of FIG. Figures 1 to 3 Although the process 400 is described with reference to the systems, processes, control loops, and images described above, the process 400 may be performed by other systems that differ from those systems, processes, control loops, and images and include, for example, different selections of electronics, sensors, components, mobile structures, and / or mobile structure attributes.

[0050] In block 402, an aberrometer model associated with an ocular aberrometer system is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 can be configured to control the source 260 of the single-pass model target 202A (arranged as the optical target 102 monitored by the ocular aberrometer system 100) to generate a source beam 211 through the lens system 262 to illuminate the wavefront sensor 120 and / or other elements of the ocular aberrometer system 100. For example, the controller 140 can be configured to vary the defocus aberration of the single-pass model target 202A according to a plurality of selected defocus powers to generate a plurality of wavefront sensor data sets provided by the wavefront sensor 120. The controller 140, the server 150, and / or the distributed server system 154 can be configured to determine a system aberration associated with the ocular aberrometer system 100 based, at least in part, on the plurality of wavefront sensor data sets provided by the wavefront sensor 120. The system aberrations and / or associated wavefront sensor data sets may be stored (eg, on the server 150 and / or the distributed server system 154 ) as an aberrometer model 360 .

[0051] In block 404, an eye model associated with the ocular aberration measurement system is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 can be configured to control the beacon 110 of the ocular aberration measurement system 100 to generate a probe beam 111 to illuminate the two-way phantom target 202B (the optical target 102 arranged to be monitored by the ocular aberration measurement system 100), which in turn illuminates (e.g., via reflection of the probe beam 111) one or more of the wavefront sensor 120, the eye tracker 130, the OCT sensor 122, and / or other elements of the ocular aberration measurement system 100. For example, the controller 140 can be configured to change the position and / or orientation of the interchangeable ocular aberration model 270 of the two-way phantom target 202B relative to the optical axis 111 of the ocular aberration measurement system 100 according to a plurality of selected alignment deviations to generate a corresponding plurality of wavefront sensor data sets provided by the wavefront sensor 120. The plurality of selected alignment deviations and / or the corresponding plurality of wavefront sensor data sets may be stored (e.g., on the server 150 and / or the distributed server system 154) as the eye model 370. Similar techniques may be used to merge the eye tracker data from the eye tracker 130 and the OCT sensor data from the OCT sensor 122 into the eye model 370.

[0052] In block 406, a complex analysis engine is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the complex analysis engine 350 based at least in part on the aberrometer model 360 generated in block 402 and / or the eye model 370 generated in block 404. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to form a deep neural network 600 comprising an input layer 620, an output layer 640, and at least one hidden layer 630-639 coupled between the input layer 620 and the output layer 640, each layer comprising a plurality of neurons. The controller 140, server 150, and / or distributed server system 154 can be configured to train at least one trainable weight matrix W associated with each neuron of the input, output, and hidden layers of the neural network 600 via the supervised learning engine 390, using the alignment deviations of the eye model 370 as ground truth output data and using the corresponding wavefront sensor data set of the eye model 370 as training input data as described herein. The resulting deep neural network can be stored and used as the complex analysis engine 350. In other embodiments, the controller 140, server 150, and / or distributed server system 154 can be configured to generate a plurality of corrected wavefront sensor data sets corresponding to a plurality of selected alignment deviations of the eye model 370 based at least in part on the system aberrations associated with the eye aberrometer system in the aberrometer model 360, and then form the neural network 600 and train one or more complex analysis parameters of the neural network 600 using the supervised learning engine 390 to determine the complex analysis engine 350 as described herein.

[0053] In block 408, a compact analysis engine is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 can be configured to form a compact neural network 600 (including an input layer 620, an output layer 640, and a single hidden layer 630 coupled between the input layer 620 and the output layer 640) and generate a weighting matrix W associated with each neuron of the input, output, and / or hidden layers of the compact neural network 600 based at least in part on one or more complex analysis parameters associated with the hidden layers 630-639 of the plurality of complex analysis engines 350. Upon generation, the compact analysis engine 340 can be stored or otherwise integrated with or implemented by the controller 140, which can use the compact analysis engine 340 to generate substantially real-time (e.g., 30 frames / second) user feedback (e.g., including various graphical display views) and reliable and accurate monitoring of ocular alignment deviations, ocular aberrations, and / or other characteristics of the optical target 102 as described herein.

[0054] Figure 5A flow chart of a process 500 of operating the ocular aberration measurement system 100 and / or 300 according to an embodiment of the present disclosure is illustrated. It should be understood that any step, sub-step, sub-process, or block of the process 500 may be performed in different ways. Figure 5 For example, in other embodiments, one or more blocks may be omitted from or added to the process. In addition, block inputs, block outputs, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to subsequent portions of the corresponding process. Although process 500 is a block diagram of a process, the process of FIG. Figures 1 to 3 Although the process 500 is described with reference to the systems, processes, control loops, and images described above, the process 500 may be performed by other systems that differ from those systems, processes, control loops, and images and include, for example, different selections of electronics, sensors, components, mobile structures, and / or mobile structure attributes.

[0055] In block 502, ocular aberration output is received. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to receive ocular aberration measurement output data including at least wavefront sensor data provided by the wavefront sensor 120. More generally, the ocular aberration measurement output data may include any one or more of the wavefront sensor data provided by the wavefront sensor 120, the OCT sensor data provided by the OCT sensor 122, the eye tracker data provided by the eye tracker 130, and / or other output data provided by the ocular aberration measurement system 100, as described herein.

[0056] In block 504, an estimated eye alignment deviation is determined. For example, the controller 140, server 150, and / or distributed server system 154 may be configured to determine an estimated eye alignment deviation corresponding to the relative position and / or orientation of the optical target 102 (e.g., the patient's eye) monitored by the eye aberration measurement system 100 based at least in part on the eye aberration measurement output data received in block 502. In some embodiments, the controller 140, server 150, and / or distributed server system 154 may be configured to determine the estimated eye alignment deviation based at least in part on the eye tracker sensor data received in block 502. In other embodiments, the wavefront sensor data received in block 502 includes a time series of wavefront sensor measurements, and the controller 140, server 150, and / or distributed server system 154 may be configured to determine the estimated eye alignment deviation by determining, for each wavefront sensor measurement, a wavefront estimated eye alignment deviation corresponding to the wavefront sensor measurement.

[0057] For example, in one embodiment, the controller 140, server 150, and / or distributed server system 154 can be configured to determine an estimated eye alignment deviation by determining a corresponding estimated relative position and / or orientation of the optical target 102 (e.g., an estimated eye alignment of the optical target 102) for each wavefront sensor measurement value, identifying one or more clusters of estimated relative positions and / or orientations of the optical target based at least in part on one or more preset or adaptive cluster thresholds, determining a gaze alignment based at least in part on a centroid of a largest cluster in the one or more identified clusters, and determining an estimated eye alignment deviation for each wavefront sensor measurement value based at least in part on a difference between the gaze alignment and the estimated relative position and / or orientation of the optical target corresponding to the wavefront sensor measurement value (e.g., a difference between the gaze alignment and the estimated eye alignment of the optical target 102 corresponding to a time series of wavefront sensor measurement values). In a related embodiment, wherein the eye aberration measurement system 100 includes an eye tracker 130, a controller 140, a server 150, and / or a distributed server system 154, the eye tracker, controller, server, and / or distributed server system can be configured to determine an estimated eye alignment deviation by determining a corresponding gaze state of the optical target for each wavefront sensor measurement based at least in part on a gaze threshold parameter and eye tracker sensor data corresponding to the wavefront sensor measurements and omitting a subset of the wavefront sensor measurements, and then determining a corresponding estimated relative position and / or orientation of the optical target 102 based at least in part on the determined corresponding gaze states. For example, such a technique can eliminate wavefront sensor measurements acquired when the optical target 102 is detected as unfixated (as detected by the eye tracker 130).

[0058] In block 506, corrected ocular aberrometry output data is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 can be configured to determine the corrected ocular aberrometry output data based at least in part on the estimated ocular alignment deviation determined in block 504 and / or the wavefront sensor data received in block 502. In some embodiments, where the wavefront sensor data comprises a time series of wavefront sensor measurements, the controller 140, the server 150, and / or the distributed server system 154 can be configured to determine the corrected ocular aberrometry output data by determining, for each wavefront sensor measurement, an estimated ocular alignment deviation associated with the wavefront sensor measurement value, and generating an average wavefront sensor measurement value as the corrected wavefront sensor measurement value based at least in part on each wavefront sensor measurement value having an associated estimated ocular alignment deviation that is equal to or less than a preset maximum allowable deviation. This preset maximum allowable deviation can be selected or set by a manufacturer and / or user of the ocular aberrometry system 100.

[0059] In other embodiments, the controller 140, the server 150, and / or the distributed server system 154 can be configured to determine the corrected ocular aberration measurement output data by determining, for each wavefront sensor measurement value, an estimated eye alignment deviation associated with the wavefront sensor measurement value, determining a corrected wavefront sensor measurement value for each wavefront sensor measurement value having an associated estimated eye alignment deviation that is equal to or less than a preset maximum allowable deviation based at least in part on the wavefront sensor measurement value and / or the associated estimated eye alignment deviation, and generating an average wavefront sensor measurement value based at least in part on the corrected wavefront sensor measurements. For example, the controller 140, the server 150, and / or the distributed server system 154 can be configured to determine the corrected wavefront sensor measurement value by applying the complex analysis engine 350 or the compact analysis engine 340 of the ocular aberration measurement system 100 to each wavefront sensor measurement value to generate a corresponding wavefront estimated eye alignment deviation and / or corrected wavefront sensor measurement value as described herein.

[0060] In block 508, user feedback is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate user feedback corresponding to the ocular aberration measurement output data received in block 502 based at least in part on the estimated ocular alignment deviation determined in block 504. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate user feedback by determining an ocular alignment deviation metric based at least in part on the estimated ocular alignment deviation and reporting the ocular alignment deviation metric via the user interface 146 of the ocular aberration measurement system 100. In other embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to generate user feedback based at least in part on the estimated ocular alignment deviation determined in block 504 and / or the corrected ocular aberration measurement output data determined in block 506. In various embodiments, such user feedback may include a substantially real-time display view of an eye alignment deviation metric based at least in part on the estimated eye alignment deviation determined in box 504, a substantially real-time display view of an eye aberration map based at least in part on the corrected eye aberration measurement output data determined in box 504, and / or an auditory and / or visual alert indicating that at least one estimated eye alignment deviation is greater than a preset maximum allowable deviation as described herein.

[0061] Thus, embodiments of the present disclosure can provide substantially real-time (e.g., 30 frames / second) user feedback (e.g., including various graphical display views) and reliable and accurate monitoring of ocular alignment deviations, ocular aberrations, and / or other characteristics of the optical target 102, as described herein. Such embodiments can be used to assist in various types of clinical and intraoperative ophthalmic examinations and help provide improved surgical outcomes.

[0062] Figure 6 A diagram illustrates a multi-layer or "deep" neural network (DNN) 600, according to an embodiment of the present disclosure. In some embodiments, neural network 600 may represent a neural network for implementing each of one or more models and / or analysis engines described in connection with systems 100 and / or 300. Neural network 600 uses an input layer 620 to process input data 610. In various embodiments, input data 610 may correspond to aberration measurement output data and / or training data provided to one or more models and / or analysis engines to generate and / or train the one or more models and / or analysis models, as described herein. In some embodiments, input layer 620 may include a plurality of neurons or nodes that are used to condition input data 610 by scaling, biasing, filtering, range limiting, and / or otherwise conditioning input data 610 for processing by the remainder of neural network 600. In other embodiments, input layer 620 may be configured to echo input data 610 (e.g., where input data 610 has already been appropriately scaled, biased, filtered, range limited, and / or otherwise conditioned). Each neuron in the input layer 620 generates an output that is provided to a neuron / node in the hidden layer 630. The hidden layer 630 includes multiple neurons / nodes that process the output from the input layer 620. In some embodiments, each neuron in the hidden layer 630 generates an output that is then propagated through one or more additional hidden layers (ending with hidden layer 639). The hidden layer 639 includes multiple neurons / nodes that process the output from the previous hidden layer. Figure 6 In the illustrated embodiment, the output of the hidden layer 639 is fed into an output layer 640. In various embodiments, the output layer 640 includes one or more neurons / nodes that can be used to condition the output from the hidden layer 639 by scaling, biasing, filtering, range limiting, and / or otherwise conditioning the output from the hidden layer 639 to form output data 650. In alternative embodiments, the neural network 600 can be implemented according to various neural network or other processing architectures, including a neural network having only one hidden layer, a neural network having recurrent layers, and / or various other neural network architectures or complex analytical methodologies, including a K-Nearest Neighbor (k-NN) database for classification and / or regression.

[0063] In some embodiments, each of the input layer 620, hidden layers 631-639, and / or output layer 640 includes one or more neurons. In one embodiment, each of the input layer 620, hidden layers 631-639, and / or output layer 640 may include the same or different numbers of neurons. In a particular embodiment, the neural network 600 may include a total of approximately 6 layers, with up to 2,000 to 4,000 neurons in each layer. In various embodiments, each of such constituent neurons may be configured to receive a combination of its inputs x (e.g., a weighted sum generated using a trainable weight matrix / vector W), receive an optional trainable bias b, and apply an activation function f to generate an output a, such as according to the equation a=f(Wx+b). For example, the activation function f may be implemented as a rectified linear unit activation function, or any one or combination of an activation function with upper and / or lower bounds, a log-sigmoid function, a hyperbolic tangent function, and / or according to other activation function forms. Each neuron in such a network may be configured to operate according to the same or different activation functions and / or different types of activation functions as described herein. In certain embodiments, corresponding to regression applications, only the neurons of the output layer 640 may be configured to apply such a linear activation function to generate their respective outputs.

[0064] In various embodiments, the neural network 600 can be trained using supervised learning (e.g., implemented as a supervised learning engine 390), such as by systematically providing the neural network 600 with a selected set of training data (e.g., training data 392), wherein each set of training data includes a set of input training data and a corresponding set of ground truth (e.g., expected) output data (e.g., a combination of the aberrometer model 360 and the eye model 370), and then determining and / or otherwise comparing the difference between the resulting output data 650 (e.g., the training output data provided by the neural network 600) and the ground truth output data (e.g., "training error"). In some embodiments, the training error can be fed back to the neural network 600 to adjust various trainable weights, biases, and / or other complex analysis parameters of the neural network 600. In some embodiments, such training error can be provided as feedback to the neural network 600 using one or more backpropagation techniques (including, for example, stochastic gradient descent techniques and / or other backpropagation techniques). In one or more embodiments, a relatively large group of selected training data sets may be presented to neural network 600 multiple times until the total loss function (e.g., the mean squared error based on the differences in each set of training data) converges to or below a preset maximum allowable loss threshold.

[0065] In additional embodiments, the supervised learning engine 390 can be configured to include semi-supervised learning, weakly supervised learning, active learning, structured prediction, and / or other general machine learning techniques to assist in training the complex analysis parameters of the neural network 600 as described herein (e.g., and generating the complex analysis engine 350). For example, the supervised learning engine 390 can be configured to generate simulated training data sets, each set including simulated input training data and a corresponding simulated ground truth output data set, and perform supervised learning based at least in part on the simulated training data sets. Each of the simulated input training data and the ground truth data set can be generated by modifying the input training data (e.g., adjusting aberration parameters and / or alignment deviations associated with the simulated two-pass model target) and interpolating non-simulated ground truth data to generate corresponding simulated ground truth data. In various embodiments, the supervised learning engine 390 can be configured to simulate one or millions of simulated training data sets, each set at least slightly deviating from the training data set corresponding to the eye model 370, and train the neural network 600 based on one or millions of such simulated training data sets.

[0066] In various embodiments, the ocular aberration measurement system 100 and / or 300 can be configured as a phase-based ocular aberration measurement system. In such embodiments, one of the processing steps of such a phase-based ocular aberration measurement system generates a wrapped phase array as part of the representation of the wavefront sensor data provided by the wavefront sensor 120. Typically, for example, the wrapped phase array has real values ​​constrained to the range of [-π to π] or [0 to 2π] as a natural consequence of calculating the phase from complex numbers using the inverse tangent function. These wrapped phase values ​​are then "unwrapped" to reveal the true range of phase values, which are generally proportional to the first-order wavefront aberration derivative and can be used to determine wavefront aberration values ​​that characterize the wavefront aberration.

[0067] In order to generate an accurate final set of wavefront aberration values ​​(e.g., Zernike polynomial coefficients), the resulting unwrapped phase array should be approximately continuous (e.g., corresponding to a continuous function). However, due to various reasons, such as image artifacts caused by glass bubbles (in the initial interferogram captured by the wavefront sensor), a typical unwrapped phase array may include various "singularities" or discontinuities and is not continuous, which can lead to errors in the final calculated aberration fit. Typically, this aberration fit is usually a Zernike polynomial expansion, but other functional aberration signatures are also possible. Due to the way the Zernike polynomial coefficients are fit to the wavefront sensor data in the scaled, unwrapped phase array, local singularity errors (e.g., points where the unwrapped phase is discontinuous) can lead to errors in the overall final aberration characterization result. Some type of repair can be applied to attempt to recover the true phase, but such repair processes are often unsatisfactory because the "repaired" data points do not accurately reflect the true wavefront sensor data and may introduce additional errors. The embodiments disclosed herein do not attempt this type of repair, but instead apply a linear system of weighted normal equations to the wavefront sensor data represented by the unfolded phase array, using the Zernike polynomial coefficients to fit the data in a least-squares error sense. The weights of the rows in the linear system of equations are chosen to reduce or eliminate the effect of each singularity.

[0068] In one embodiment, the weights may be chosen as follows: Let d be the closest distance from a point in the unfolded phase array to an identified singularity. The weight of the point is: 0 if the distance d is less than or equal to a threshold DO; 1 if the distance d is greater than or equal to a threshold D1; and [(d-DO) / (D1-D0)]^p if the distance d is between DO and D1. In other words, the weight of each point is: 0 for elements whose distance from any identified singularity is less than or equal to a threshold distance D0; 1 for elements whose distance from all identified singularities is greater than or equal to a threshold distance D1; and a function of the closest distance, the threshold, and a scaling parameter p for all elements whose distance from an identified singularity is between the two threshold distances. Formulation:

[0069]

[0070] The hyperparameters DO, Dl, and p can be optimized depending on the application, but preferred values ​​are: DO = P * 0.05, Dl = P * 0.1, and p = 2, where the value P is the characterized / reconstructed pupil diameter. In various embodiments, this weighted fitting reduces the variability of phase-based ocular aberrometry examinations caused by singularities in the wavefront sensor data without the risk of introducing new errors, which in turn improves the precision and accuracy of the resulting ocular aberrometry output data (e.g., a characterization of aberrations in the eye), as described herein.

[0071] Figure 7A A process diagram of a process 700 for reducing errors caused by imaging artifacts in an ocular aberration measurement system according to an embodiment of the present disclosure is illustrated. Figure 7A In the illustrated embodiment, initial wavefront sensor data in the form of an interferogram 710 of the human eye (e.g., comprising a plurality of substantially concentric fringe lines / patterns 712) may be decomposed (e.g., step 702) into a set of at least wrapped phase components / arrays 724 (φ i ) of the wrapped phase wavefront sensor data 720. The wrapped phase wavefront sensor data 720 may include additional components (e.g., an amplitude / scaling array 722 (ρ i )), so that the wrapped phase wavefront sensor data 720 forms a discrete representation of the interferogram 710, which can be similar to a discrete spatial Fourier transform of the interferogram 710. The wrapped phase wavefront sensor data 720 can be converted (e.g., step 704) to include at least the unwrapped phase array 734 (φ i ') of the unwrapped phase wavefront sensor data 730: by identifying the phases that are approximately 2π (or multiples thereof) in amplitude and / or appear in the wrapped phase array 724 (φ i ) around artificial numerical boundaries (e.g., +-π or 0 and 2π) of the wrapped phase array 724 (φ i ) in the phase array 724 (φ i ) elements by adding or subtracting 2π to eliminate discontinuities and forming an unwrapped phase array 734 (φ i ').

[0072] Once the unwrapped phase wavefront sensor data 730 is determined, singularities or discontinuities attributable to image artifacts (e.g., associated with vitreous bubbles 714 imaged as part of the interferogram 710) can be identified in the unwrapped phase wavefront sensor data 730, such as by identifying an unwrapped phase array 734 (φ) having a magnitude greater than a predetermined singularity threshold (e.g., provided by a user or manufacturer of the ocular aberration measurement system 100 and / or 300). i Such a predetermined singularity threshold may be selected to account for various types of image noise in the interferogram 710 and / or in the unfolded phase array 734 (φ i ').

[0073] Once such a discontinuity is identified (eg, as the unwrapped phase array 734 (φ i'), the distance between any element of the unfolded phase wavefront sensor data 730 and such a discontinuity can be determined, and a fitting weight for each element of the unfolded phase wavefront sensor data 730 can be determined, such as by using a linear system of weighted normal equations for wi(1) as described herein. In some embodiments, such a weight can be calculated for only the most recent singularity (e.g., as a value of d), or such a weight can be calculated for each detected singularity, and the resulting set of weights combined (e.g., by multiplication) to determine the fitting weight for that element of the unfolded phase wavefront sensor data 730. The aberration characterization fit 740 (e.g., the aberration measurement output data) can be based on the fitting function (e.g., a selected aberration characterization function, such as a Zernike polynomial expansion of a selected order), the determined weight w i and unwrapped phase wavefront sensor data 730 are determined (step 706), and an eye aberration map 750 may be generated (step 708) to visualize the aberration characterization fit 740 and identify spatial regions 752-754 of the examined eye that deviate from the ideal eye.

[0074] In various embodiments, the interference pattern 710 and / or the ocular aberration map 750 may form at least a portion of a display view (e.g., display views 718 and / or 758) that is presented on a display of the user interface 146 and displayed to a user as user feedback regarding the operation of the ocular aberration measurement system 100 and / or 300. In general operation, the diameter 716 of the interference pattern 710 and the diameter 756 of the ocular aberration map 750 may be configured to approximately represent or correspond to the pupil diameter of the eye being examined.

[0075] Figure 7B A process diagram illustrating a process 700B for reducing errors caused by imaging artifacts in an eye aberration measurement system according to an embodiment of the present disclosure is shown, similar to Figure 7A Process 700. Figure 7B In the illustrated embodiment, initial wavefront sensor data in the form of an interference pattern / fringe pattern 710B may be decomposed (e.g., steps 702B and 703B-1 and 703B-2) into a set of wrapped phase wavefront sensor data 724B and 726B, which includes at least a wrapped phase component / array (e.g., similar to Figure 7A The wrapped phase component / array 724(φ i)). In particular, the interference pattern 710B can be Fourier transformed (e.g., step 702B) to produce transformed wavefront sensor data 720B, which can include multiple spatial peaks with corresponding spatial structure neighborhoods 723B-1, 723B-2. In some embodiments, the spatial structure neighborhoods 723B-1, 723B-2 can be selected as circular neighborhoods within the transformed wavefront sensor data 720B, the radii of which are selected to include non-overlapping spatial structures associated with one or more selected aberration characterization function orders (e.g., Zernike polynomial expansion orders), for example, as shown. In some embodiments, each spatial structure neighborhood 723B-1, 723B-2 can be processed separately to determine a corresponding set of wrapped phase wavefront sensor data 724B and 726B as shown. Figure 7B In the particular embodiment shown, spatial structure neighborhood 723B-1 is processed into a first wrapped phase dW / dx array and spatial structure neighborhood 723B-1 is processed into a second wrapped phase dW / dy array.

[0076] The unwrapped phase wavefront sensor data 724B and 726B may be converted (e.g., steps 704B-1 and 704B-2) to unwrapped phase wavefront sensor data 734B, 736B, which may include at least one unwrapped phase array (e.g., similar to wrapped phase component / array 734 (φ) in FIG. 7 ). i '). Once the unwrapped phase wavefront sensor data 734B, 736B is determined, a singularity or discontinuity 764 (e.g., due to an image artifact of the interferogram 710B) can be identified in the unwrapped phase wavefront sensor data 734B, 736B to generate processed unwrapped phase wavefront sensor data 744B, 746B. Once such a discontinuity 764 is identified (e.g., as an element of a corresponding unwrapped phase array), the distance between any element of the unwrapped phase wavefront sensor data 734B, 736B and such a discontinuity can be determined and can be processed, for example, by using a method described herein. i Equation (1) determines the fitted weights for each element of the unwrapped phase wavefront sensor data 734B, 736B. In particular, for example, the processed unwrapped phase wavefront sensor data 744B, 746B includes darker lines 764 indicating singularities in the processed unwrapped phase wavefront sensor data 744B, 746B and halos 766 about the discontinuities 764 indicating local weights different from 1.

[0077] The aberration characterization fit (eg, aberration measurement output data) may be based on a fitting function (eg, a selected aberration characterization function such as a Zernike polynomial expansion of a selected order or range of orders), the determined weights w iand processed unwrapped phase wavefront sensor data 744B, 746B (e.g., in the form of processed first wrapped phase dW / dx array and second wrapped phase dW / dy array), and an eye aberration map 750B can be generated (steps 706B / 708B) to visualize the aberration characterization fit and identify various spatial regions of the examined eye that deviate from the ideal eye (e.g., shown as a color map gradient according to a selected color palette). Figure 7B An uncorrected eye aberration map 790B (e.g., showing significantly different spatial gradients and aberration distributions) derived directly from the unfolded phase wavefront sensor data 734B, 736B (step 709B) without weighting according to equation (2) is provided for comparison.

[0078] In various embodiments, the interference pattern 710B and / or the ocular aberration map 750B may form at least a portion of a display view (e.g., display views 718B and / or 758B) presented on a display of the user interface 146 and displayed to a user as user feedback regarding the operation of the ocular aberration measurement system 100 and / or 300, together with or separately from any one or combination of display views of intermediate steps, such as display views 728B, 729B, 738B, 739B, and / or 798B as shown. In general operation, the diameter 716B of the interference pattern 710B and the diameter 756B of the ocular aberration map 750B may be configured to approximately represent or correspond to the pupil diameter of the eye being examined.

[0079] Figure 7C A process diagram illustrating a process 700C for reducing errors caused by imaging artifacts in an eye aberration measurement system according to an embodiment of the present disclosure includes: Figure 7A Process 700 and / or Figure 7B In particular, process 700C illustrates a spatially magnified version of the techniques used in steps 705B-1 and 705B-2 to determine and apply weights to the unwrapped phase wavefront sensor data 734B, 736B based on discontinuities 764. For example, the unwrapped phase wavefront sensor data 734C is shown as including a discontinuity profile 764C of the unwrapped phase wavefront sensor data 734C and a discontinuity point 766C that is closest to the elements / points 744-1, 744-2, 744-3 (e.g., points used to fit the Zernike polynomial expansion). Figure 7CAlso illustrated in FIG. 7A is a circle 740C-0 (which includes / discriminates all points whose distance from the nearest discontinuity point 766C of the discontinuity profile 764C is less than or equal to a threshold distance / radius D0) and a circle 740C-1 (which excludes / discriminates all points whose distance from the nearest discontinuity point 766C of the discontinuity profile 764C is greater than or equal to a threshold distance D1). Line 746C can be perpendicular to the discontinuity profile 764C at the nearest discontinuity point 766C and pass through elements / points 744-1, 744-2, and 744-3 of the unwrapped phase wavefront sensor data 734C. Thus, according to equation (1) herein, the weight of element 744-1 would be 0 (e.g., corresponding to discontinuity 764), the weight of element 744-2 would be between 0 and 1 (e.g., corresponding to halo 766), and the weight of element 744-3 would be 1 (e.g., the remainder of the unwrapped phase wavefront sensor data 736B).

[0080] In various embodiments, process 700C may form at least a portion of a display view (e.g., display view 738C) that is presented on a display of user interface 146 and displayed to a user as user feedback regarding the operation of ocular aberration measurement systems 100 and / or 300, together with or separately from any one or combination of the other display views described herein. In general operation, diameter 716C may be configured to approximately represent or correspond to the diameter of the pupil of the eye being examined.

[0081] Figure 8 A flow chart illustrating a process 800 of operating the ocular aberration measurement system 100 and / or 300 according to an embodiment of the present disclosure, reducing errors caused by imaging artifacts. It should be understood that any step, sub-step, sub-process, or block of the process 800 may be performed in a different manner than Figure 8 For example, in other embodiments, one or more blocks may be omitted from or added to the process. In addition, block inputs, block outputs, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to subsequent portions of the corresponding process. Although process 800 is a block diagram of a process, the process may be performed in the order or arrangement shown in the embodiment. For example, in other embodiments, one or more blocks may be omitted from or added to the process. In addition, block inputs, block outputs, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to subsequent portions of the corresponding process. Figure 1 7 , but process 800 may be performed by other systems that differ from those systems, processes, control loops, and images and include, for example, different selections of electronics, sensors, components, mobile structures, and / or mobile structure attributes.

[0082] In block 802, an ocular aberration output is received. For example, the controller 140, the server 150, and / or the distributed server system 154 can be configured to receive ocular aberration measurement output data comprising at least wavefront sensor data provided by the wavefront sensor 120. More generally, the ocular aberration measurement output data can include any one or more of the wavefront sensor data provided by the wavefront sensor 120, the OCT sensor data provided by the OCT sensor 122, the eye tracker data provided by the eye tracker 130, and / or other output data provided by the ocular aberration measurement system 100, as described herein. For example, the wavefront sensor data provided by the wavefront sensor 120 can include an interferogram 710 of the eye under examination, and / or can include wrapped phase wavefront sensor data 720 or unwrapped phase wavefront sensor data 730, depending on, for example, the capabilities and / or configuration of the wavefront sensor 120.

[0083] In block 804, the ocular aberrometry output data is decomposed. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to decompose the wavefront sensor data received in block 802 into at least the wrapped phase component / array 724, such as by generating a discrete Fourier transform of the interferogram 710 as described herein. For example, in embodiments where the wavefront sensor 120 directly provides the wrapped phase wavefront sensor data 720, block 804 may be omitted or incorporated into block 802.

[0084] In block 806, singularities in the ocular aberrometry output data are identified. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to identify singularities caused by imaging artifacts in the unwrapped phase wavefront sensor data corresponding to the ocular aberrometry output data received in block 802. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine an unwrapped phase array 734 and / or the unwrapped phase wavefront sensor data 730 based at least in part on the wrapped phase array 724 of the wrapped phase wavefront sensor data 720 and identify discontinuities caused by imaging artifacts in the unwrapped phase array 734 as described herein. In various embodiments, the controller 140, server 150, and / or distributed server system 154 can be configured to identify singularities by, for example, identifying discontinuities in the unwrapped phase component 734 of the wrapped phase wavefront sensor data 730 having an amplitude greater than a predetermined singularity threshold, or using other discontinuity detection techniques for discrete data, including, for example, spline fitting and element offset techniques and / or numerical derivative and threshold techniques.

[0085] In block 808, corrected ocular aberrometry output data is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 can be configured to determine the corrected ocular aberrometry output data based at least in part on the wrapped phase component / array 724 of the wrapped phase wavefront sensor data decomposed in block 804 and / or received in block 802 and / or the singularities in the received ocular aberrometry output data identified in block 806. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 can be configured to determine a fitting weight for each element of the unwrapped phase wavefront sensor data 730 and determine the corrected ocular aberrometry output data by determining an aberrometry fit based at least in part on the fitting weights and the unwrapped phase wavefront sensor data 730 as described herein.

[0086] In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to operate based on the web as described herein. i Equation (1) of determines the fitting weights for each element of the unfolded phase wavefront sensor data 730, wherein the values ​​of the parameters used in equation (1) are based at least in part on the pupil diameter of the eye being examined, such that the fitting weights themselves are based at least in part on the pupil diameter. In related embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to additionally employ any of the methods described herein to determine the corrected ocular aberrometry output data to compensate for the ocular aberrometry as described herein with respect to processes 400 and 500 and Figures 4 to 6 Said system aberrations and / or eye alignment deviations.

[0087] In block 810, user feedback is generated. For example, the controller 140, server 150, and / or distributed server system 154 may be configured to generate user feedback corresponding to the ocular aberrometry output data received in block 802, such as a display view of the interferogram 710, and / or generate user feedback corresponding to the corrected ocular aberrometry output data determined in block 808, such as a display view of the ocular aberration map 750. In various embodiments, the controller 140, server 150, and / or distributed server system 154 may be configured to use the methods described herein in conjunction with Figures 4 to 6 In various embodiments, such user feedback may include a substantially real-time displayed view of the wavefront sensor data received in block 802, the decomposed ocular aberrometry output data determined in block 804, the singularities identified in block 806, the corrected ocular aberrometry output data determined in block 808, and / or the corresponding ocular aberration map 750.

[0088] Thus, embodiments of the present disclosure can provide substantially real-time (e.g., 30 frames / second) user feedback (e.g., a display view including various graphics) and reliable and accurate monitoring of ocular aberrations and / or other characteristics of the optical target 102 as described herein. Such embodiments can be used to assist in various types of clinical and intraoperative ophthalmic examinations and help provide improved surgical outcomes.

[0089] Where applicable, hardware, software, or a combination of hardware and software may be used to implement the various embodiments provided by the present disclosure. Likewise, where applicable, without departing from the spirit of the present disclosure, the various hardware components and / or software components set forth herein may be combined into composite components comprising software, hardware, and / or both. Where applicable, without departing from the spirit of the present disclosure, the various hardware components and / or software components set forth herein may be divided into subcomponents comprising software, hardware, or both. Additionally, where applicable, it is conceivable that software components may be implemented as hardware components, and vice versa.

[0090] Software according to the present disclosure, such as non-transitory instructions, program code and / or data, can be stored on one or more non-transitory machine-readable media. It is also contemplated that the software identified herein can be implemented using one or more general-purpose or special-purpose computers and / or networked and / or other computer systems. Where applicable, the order of the various steps described herein can be changed, combined into composite steps and / or divided into sub-steps to provide the features described herein.

[0091] The embodiments described above are illustrative and do not limit the present invention. It should also be understood that many modifications and variations based on the principles of the present invention are possible. Therefore, the scope of the present invention is limited only by the following claims.

Claims

1. An eye aberration measurement system, comprising: a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration measurement system; as well as a logic device configured to communicate with the wavefront sensor, wherein the logic device is configured to: receiving ocular aberration measurement output data comprising at least wavefront sensor data provided by the wavefront sensor; identifying singularities caused by imaging artifacts in received ocular aberration measurement output data; determining corrected aberrometric output data based at least in part on the identified singularity and the received ocular aberrometric output data; and generating user feedback corresponding to the received ocular aberrometry output data based at least in part on the corrected aberrometry output data, Wherein, determining the corrected aberration measurement output data comprises: determining a fit weight for each element of unwrapped phase wavefront sensor data corresponding to the received wavefront sensor data, wherein the fit weight is based at least in part on a pupil diameter corresponding to the optical target and a distance between an element of the unwrapped phase wavefront sensor data and an identified singularity in the received ocular aberration measurement output data; and determining an aberration characterization fit as the corrected aberrometry output data, wherein the aberration characterization fit is based at least in part on each element of the unwrapped phase wavefront sensor data and each corresponding fit weight; and Among them, an ocular aberration map is generated to visualize the aberration representation fit and identify spatial regions of the examined eye that deviate from the ideal eye.

2. The eye aberration measurement system according to claim 1, wherein: The received wavefront sensor data includes wrapped phase wavefront sensor data, and wherein the logic device is configured to: Unwrapped phase wavefront sensor data is determined based at least in part on the wrapped phase wavefront sensor data prior to identifying imaging artifact-induced singularities in the received ocular aberration measurement output data.

3. The eye aberration measurement system according to claim 2, wherein: Determining the unwrapped phase wavefront sensor data includes: Identifying a discontinuity in a wrapped phase component of the wrapped phase wavefront sensor data, wherein the discontinuity in the wrapped phase component has a magnitude of approximately 2π relative to adjacent elements of the wrapped phase component of the wrapped phase wavefront sensor data and / or occurs near an artificial numerical boundary of the wrapped phase component of the wrapped phase wavefront sensor data.

4. The eye aberration measurement system according to claim 2, wherein: Identifying singularities caused by imaging artifacts in received eye aberration output data includes: Discontinuities in the unwrapped phase component of the wrapped phase wavefront sensor data having a magnitude greater than a predetermined singularity threshold are identified.

5. The eye aberration measurement system according to claim 1, wherein: each fitting weight is determined based at least in part on a weight formulation that applies a weight of 0 to elements that are less than or equal to a first threshold distance from any identified singularity and a weight of 1 to elements that are greater than a second threshold distance from all identified singularities; as well as The aberration characterization fitting is based at least in part on an aberration characterization function comprising a Zernike polynomial expansion.

6. The eye aberration measurement system according to claim 1, wherein: The user feedback includes: a substantially real-time display view of an interferogram corresponding to received wavefront sensor data; and / or A substantially real-time displayed view of an eye aberration map based at least in part on the determined corrected eye aberration measurement output data.

7. The eye aberration measurement system according to claim 1, wherein: The logic device is configured to: determining an estimated eye alignment deviation corresponding to a relative position and / or orientation of the optical target monitored by the eye aberration measurement system based at least in part on the received eye aberration measurement output data; determining corrected ocular aberrometry output data based at least in part on the estimated ocular alignment deviation and / or the received wavefront sensor data; and The user feedback is generated based at least in part on the estimated eye alignment deviation and / or the corrected eye aberrometry output data.

8. The eye aberration measurement system according to claim 7, wherein: Determining the estimated eye alignment deviation comprises: A complex analysis engine or a compact analysis engine of the ocular aberration measurement system is applied to the received wavefront sensor data to generate a corresponding wavefront estimated eye alignment deviation.

9. The eye aberration measurement system according to claim 7, wherein: The wavefront sensor data comprises a time series of wavefront sensor measurements, and wherein determining the corrected eye alignment deviation comprises: For each wavefront sensor measurement, an eye alignment deviation of the wavefront estimate corresponding to the wavefront sensor measurement is determined.

10. A method for measuring ocular aberrations, comprising: receiving ocular aberrometry output data from an ocular aberrometry system including a wavefront sensor, wherein the ocular aberrometry output data includes at least wavefront sensor data associated with an optical target monitored by the ocular aberrometry system; identifying singularities caused by imaging artifacts in received ocular aberration measurement output data; determining corrected aberrometric output data based at least in part on the identified singularity and the received ocular aberrometric output data; and generating user feedback corresponding to the received ocular aberrometry output data based at least in part on the corrected aberrometry output data, Wherein, determining the corrected aberration measurement output data comprises: determining a fit weight for each element of unwrapped phase wavefront sensor data corresponding to the received wavefront sensor data, wherein the fit weight is based at least in part on a pupil diameter corresponding to the optical target and a distance between an element of the unwrapped phase wavefront sensor data and an identified singularity in the received ocular aberration measurement output data; and determining an aberration characterization fit as the corrected aberrometry output data, wherein the aberration characterization fit is based at least in part on each element of the unwrapped phase wavefront sensor data and each corresponding fit weight; and Among them, an ocular aberration map is generated to visualize the aberration representation fit and identify spatial regions of the examined eye that deviate from the ideal eye.

11. The method according to claim 10, wherein: The received wavefront sensor data includes wrapped phase wavefront sensor data, the method further comprising: Unwrapped phase wavefront sensor data is determined based at least in part on the wrapped phase wavefront sensor data prior to identifying imaging artifact-induced singularities in the received ocular aberration measurement output data.

12. The method of claim 11, wherein: Determining the unwrapped phase wavefront sensor data includes: Identifying a discontinuity in a wrapped phase component of the wrapped phase wavefront sensor data, wherein the discontinuity in the wrapped phase component has a magnitude of approximately 2π relative to adjacent elements of the wrapped phase component of the wrapped phase wavefront sensor data and / or occurs near an artificial numerical boundary of the wrapped phase component of the wrapped phase wavefront sensor data.

13. The method of claim 11, wherein: Identifying singularities caused by imaging artifacts in received eye aberration output data includes: Discontinuities in the unwrapped phase component of the wrapped phase wavefront sensor data having a magnitude greater than a predetermined singularity threshold are identified.

14. The method of claim 10, wherein: each fitting weight is determined based at least in part on a weight formulation that applies a weight of 0 to elements that are less than or equal to a first threshold distance from any identified singularity and a weight of 1 to elements that are greater than a second threshold distance from all identified singularities; as well as The aberration characterization fitting is based at least in part on an aberration characterization function comprising a Zernike polynomial expansion.

15. The method of claim 10, wherein: The user feedback includes: a substantially real-time display of an interferogram corresponding to received wavefront sensor data; and / or A substantially real-time displayed view of an eye aberration map based at least in part on the determined corrected eye aberration measurement output data.

16. The method of claim 10, further comprising: determining an estimated eye alignment deviation corresponding to a relative position and / or orientation of the optical target monitored by the eye aberration measurement system based at least in part on the received eye aberration measurement output data; determining corrected eye aberrometry output data based at least in part on the estimated eye alignment deviation and / or the received wavefront sensor data; and The user feedback is generated based at least in part on the estimated eye alignment deviation and / or the corrected eye aberrometry output data.

17. The method of claim 16, wherein: Determining the estimated eye alignment deviation comprises: A complex analysis engine or a compact analysis engine of the ocular aberration measurement system is applied to the received wavefront sensor data to generate a corresponding wavefront estimated eye alignment deviation.

18. The method of claim 16, wherein: The wavefront sensor data comprises a time series of wavefront sensor measurements, and wherein determining the corrected eye alignment deviation comprises: For each wavefront sensor measurement, an eye alignment deviation of the wavefront estimate corresponding to the wavefront sensor measurement is determined.

Images