Improved ocular aberration analysis system and method

Abstract

Techniques are disclosed for systems and methods for providing improved ocular aberration analysis. The ocular aberration analysis system (100) includes a wavefront sensor (120) configured to provide wavefront sensor data associated with an optical target (102) monitored by the ocular aberration analysis system (100), and a logic device (140) configured to communicate with the wavefront sensor (120). The logic device (140) is configured to receive (502) ocular aberration analysis output data including at least the wavefront sensor data provided by the wavefront sensor (120), determine (504) an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target (102) monitored by the ocular aberration analysis system (100) based at least in part on the received ocular aberration analysis output data, and generate (508) user feedback corresponding to the received ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment.

Description

[Technical field]

[0001] One or more embodiments of the present disclosure relate generally to ocular aberration analysis and, more particularly, to systems and methods for improving ocular aberration analysis, for example, clinically or intraoperatively. [Background technology]

[0002] Ophthalmic surgery may involve reshaping the cornea and / or ocular surface, insertion and / or replacement of intraocular devices and / or artificial intraocular lenses (IOLs), and / or other surgical manipulation of the active optical components of the eye. To obtain optimal postoperative visual outcomes, good preoperative clinical evaluation and surgical planning, as well as intraoperative monitoring of the execution of the surgical plan, are important.

[0003] Ocular aberration analysis performed by an ocular aberrometer is a common technique typically used to characterize the eye preoperatively, to monitor the progress of surgery, and to evaluate the success of surgery. Conventional ocular aberrometers often suffer from a variety of measurement errors related to misalignment and optical aberrations of eye movements and / or the aberrometer itself, which can lead to inaccurate surgical planning and suboptimal surgical and visual outcomes for patients.

[0004] Thus, there is a need in the art for systems and methods for improving clinical and / or intraoperative ocular aberration analysis that leads to optimal surgical or visual outcomes for patients. Summary of the Invention [Means for solving the problem]

[0005] Techniques are disclosed for systems and methods for providing improved ocular aberration analysis. According to one or more embodiments, an ocular aberration analysis system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration analysis 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 analysis system based at least in part on an aberrometer model and / or an eye model associated with the ocular aberration analysis system, the aberrometer model and the eye model being based at least in part on 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 analysis system based at least in part on the determined complex analysis engine.

[0006] In other embodiments, the method may include determining a complex analysis engine for the ocular aberration analysis system based at least in part on an aberrometer model and / or an eye model associated with the ocular aberration analysis system, the aberrometer model and eye model being based at least in part on wavefront sensor data provided by a wavefront sensor of the ocular aberration analysis system, and generating a compact analysis engine for the ocular aberration analysis 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 adapted, when executed by one or more processors, to cause the one or more processors to perform a method that may include determining a complex analysis engine for an ocular aberration analysis system based at least in part on an aberrometer model and / or an eye model associated with the ocular aberration analysis system, where the aberrometer model and the eye model are based at least in part on wavefront sensor data provided by a wavefront sensor of the ocular aberration analysis system, and generating a compact analysis engine for the ocular aberration analysis system based at least in part on the determined complex analysis engine.

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

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

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

[0011] The scope of the present invention is defined by the claims, which are incorporated by reference into this paragraph. A more complete understanding of the embodiments of the present invention, together with a realization of further advantages thereof, will be afforded to those skilled in the art by a consideration of the following detailed description of one or more embodiments. First, reference will be made to the accompanying drawings, which are briefly described. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 illustrates a block diagram of an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 2A] FIG. 2A illustrates a block diagram of an aberration analysis characterization target of an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 2B] FIG. 2B illustrates a block diagram of an aberration analysis characterization target of an ocular aberration analysis system according to one embodiment of the present disclosure. [Diagram 3] FIG. 3 illustrates a block diagram of an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 4] FIG. 4 illustrates a flow diagram of a process for characterizing an ocular aberration analysis system according to one embodiment of the present disclosure. [Diagram 5]FIG. 5 illustrates a flow diagram of an operational process of an ocular aberration analysis system according to one embodiment of the present disclosure. [Figure 6] FIG. 6 shows a diagram of a multi-layer neural network according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Embodiments of the present invention and their advantages are best understood by referring to the following detailed description, it should be understood that like reference numerals are used to identify like elements shown in one or more of the figures.

[0014] According to various embodiments, ocular aberration analysis systems and methods provide substantially real-time measurement and monitoring of a patient's ocular aberrations, reducing system and measurement errors typical of conventional systems. For example, a patient's eye naturally drifts when fixed during ocular aberration measurements or examination. This fixation-induced drift includes tip, tilt, and twist rotations of the eye as well as changes in x, y, and z positions. These alignment deviations contribute to errors in the calculation of the wavefront characterization aberrations of the eye. If the average of these alignment deviations is close to zero, averaging the wavefront aberration measurements from individual frames of the examination image stream may be sufficient to remove most of the errors due to misalignment. If it is known that the mean of the alignment mismatch for the test sequence is not near zero or if it is not possible to confirm that the alignment mismatch has a mean near zero, various strategies described herein can be used to reduce the measurement error or noise, including one or a combination of: averaging the wavefront sensor data when the alignment mismatch does not exceed a preset threshold (e.g., represented by a Zernike polynomial expansion), correcting for errors based on misalignment of the wavefront sensor data using complex analysis methods (described herein), determining an ocular fixation status (e.g., for each wavefront measurement) based on a cluster analysis applied to the series of wavefront measurements and / or based on eye tracker data. The estimated alignment mismatches and / or their effect on the wavefront measurements can be reduced relative to a metric of ocular alignment mismatch and can be provided to a user of the ocular aberration analysis system as a display view with corresponding graphics or can be used to cause the ocular aberration analysis system to ignore images from the test image sequence or to stop and / or restart the test. Thus, embodiments provide more reliable and accurate aberration analysis measurements in clinical and intraoperative ocular aberration analysis tests than conventional systems, including by reducing variability due to eye movement during test image sequences, while providing substantially real-time monitoring feedback.

[0015] In further embodiments of the present disclosure, ocular aberration analysis systems and methods provide platforms and techniques for accurately characterizing system aberrations and correcting wavefront sensor data that may be corrupted by alignment misalignments associated with a patient's eye. For example, the ocular aberration analysis systems described herein may employ one or more of two unique forms of calibration for accurate high order aberration (HOA) analysis, namely, a system characterization process and a complex analysis training process, to address errors that may be caused by system aberrations or occur during eye movements.

[0016] For the system characterization process, the ocular aberration analysis system may be used to measure a series of reference interferograms (e.g., in the form of wavefront sensor data) generated by a model target configured to present substantially one type of variable aberration (e.g., defocus aberration) to the ocular aberration analysis system with substantially zero alignment misalignment. The reference interferograms may be used to characterize and / or quantify any system aberrations of a particular ocular aberration analysis system and may be used to correct, such as by removing, the wavefront sensor data provided by the wavefront sensor of that particular ocular aberration analysis system prior to subsequent analysis.

[0017] For the complex analysis training process, the ocular aberration analysis system may be used to capture a series of wavefront measurements generated for a model target configured to present a selection of different types and varying intensities of aberrations (e.g., up to sixth order Zernike expansion coefficients) to the ocular aberration analysis system with tip, tilt, and twist rotations of the aberration elements of the model target and variable misalignments in x, y, and z positions. The series of wavefront measurements may be used to train and / or improve a complex analysis engine executed by the ocular aberration analysis system as described herein and configured to generate substantially accurate estimated misalignments and / or corrected wavefront sensor data based on, for example, uncorrected wavefront sensor data or a combination of uncorrected wavefront sensor data and eye tracker data. Thus, the embodiments provide more reliable and accurate aberration analysis measurements than conventional systems, such as by increasing the accuracy of the aberration analysis measurements through reduction of errors resulting from system aberrations and aberration analysis measurements of an off-axis or distorted eye. Furthermore, embodiments provide a more robust (e.g., reliable and rapid) aberration analysis system by increasing the range of alignment misalignments that can be accurately compensated for or corrected for in a particular inspection and therefore included (e.g., present and / or detected within a given image of an inspection image sequence).

[0018] 1 illustrates a block diagram of an ocular aberration analysis system 100 according to one embodiment of the present disclosure. In the embodiment illustrated in FIG. 1, ocular aberration analysis system 100 may be implemented to provide substantially real-time (e.g., 30 Hz updates) 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 degrade the ocular aberration analysis provided by ocular aberration analysis system 100.

[0019] 1, the ocular aberration analysis system 100 includes a beacon 110 that generates a probe beam 111 used to illuminate an optical target 102 of the wavefront sensor 120 and / or other elements of the ocular aberration analysis system 100. The ocular aberration analysis system 100 may also include various other sensor-specific beacons and / or light sources, such as a light emitting diode (LED) array 132 that is used to illuminate the optical target 102 of the eye tracker 130 and an OCT beacon 123 that is used to generate an OCT probe beam 124 for illuminating the optical target 102 of the 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 provided by the optical target 102 (e.g., the probe beam 111, the OCT probe beam 124, and a portion of the light reflected by the optical target 102 generated by the LED array 132). The beacon 110, the OCT beacon 123, the LED array 132, and each sensor element of the ocular aberration analysis system 100 may be controlled by a control unit 140 (e.g., via communication links 141-144), which may also serve as an interface between the beacon 110, the OCT beacon 123, the LED array 132, and the sensor elements of the ocular aberration analysis system 100 and other elements of the ocular aberration analysis system 100, including a user interface 146, a server 150, a distributed server 154, and other modules 148 (e.g., accessed via any of the communication links 145, 149, and 155) as shown.

[0020] In a typical operation, the controller 140 activates one or more of the wavefront sensor 120, optional OCT sensor 122, and optional eye tracker 130, control beacon 110, OCT beacon 123, and / or LED array 132 to illuminate the optical target 102, and receives ocular aberration analysis output data (e.g., wavefront sensor data, eye tracker data, OCT sensor data) from various sensor elements of the ocular aberration analysis system 100. As described herein, the controller 140 may process the ocular aberration analysis output data itself (e.g., to detect or correct alignment misalignments and / or extract aberration analysis parameters from the wavefront sensor data) or may provide the ocular aberration analysis output data to the server 150 and / or distributed server system 154 (e.g., via the network 152) for processing. As described herein, controller 140 and / or server 150 may be configured to receive user input at user interface 146 (e.g., to control operation of ocular aberration analysis system 100) and / or generate user feedback, such as ocular aberration analysis output data and / or a display view of characteristics of the ocular aberration analysis output data, for display to a user via a display of user interface 146. As described herein, controller 140, server 150, and / or distributed server system 154 may be configured to store, process, and / or otherwise manipulate data associated with operation and / or characterization of ocular aberration analysis system 100, including, for example, machine learning and / or training of a complex analysis engine (e.g., a neural network based classification and / or regression engine) for characterizing system aberrations of ocular aberration analysis system 100, detecting alignment misalignments associated with optical target 102, and / or correcting wavefront sensor data and / or associated aberration classification coefficients. In various embodiments, the ocular aberration analysis system 100 may be configured to provide substantially real-time monitoring and user feedback (e.g., updates at a frequency of 30 Hz or greater) of the optical aberration analysis of the optical target 102 while continuously compensating for common characterization errors, as described herein.This makes the ocular aberration analysis system 100 particularly suitable for clinical and intra-operative examinations.

[0021] The beacon 110 may be implemented with a laser source (e.g., generating substantially coherent light) and / or a superluminescent diode (e.g., a “superluminescent diode (SLD)” generating relatively low coherence light) that may be controlled by the controller 140 to generate a probe beam 111 primarily for use with the wavefront sensor 120. The OCT beacon 123 may be implemented with a laser source and / or a superluminescent diode (e.g., generating relatively low coherence light that is particularly suitable for the OCT sensor 122) that may be controlled by the controller 140 to generate an OCT probe beam 124 primarily for use with the OCT sensor 122. In various embodiments, the OCT beacon may be integrated with the OCT sensor 122 as shown, for example, integrated with the beacon 110, and / or implemented as its own stand-alone beacon similar to the beacon 110 (e.g., using appropriately positioned beam splitters). The LED array 132 may be implemented by an array of shaped or patterned LEDs that may be controlled by the controller 140 to illuminate the target 102, primarily for use with the eye tracker 130. The beam splitters 112-116 may be implemented by any of a number of optical components (e.g., pellicle beam splitters, mirrors) configured to direct and / or pass the probe beam 111 to the optical target 102, and to deflect at least a portion of the probe beam 111 and / or source beam generated by the optical target 102 (e.g., the probe beam 111 or 124 and / or the reflected portion of the light emitted from the LED array 132) towards various sensor elements of the ocular aberration analysis system 100 to form sensor beams 113-117 (e.g., sensor beams). The optical target 102 may be, for example, a patient's eye, as described herein, or may be implemented by, for example, a single pass (probe beam 111 is turned off and optical target 102 generates its own illumination) or double pass (e.g., probe beam 111 is turned on in normal operation) model target.

[0022] The wavefront sensor 120 may be implemented as any one or combination of devices or device architectures configured to measure aberrations of an optical wavefront, such as the optical wavefront of the sensor beam 117 caused by reflection of at least the probe beam 111 from the optical target 102, and the wavefront sensor 120 may be configured to provide associated wavefront sensor data. For example, the wavefront sensor 120 may be implemented as any one or combination of Shack-Hartmann wavefront sensors, phase-shifted Schlieren wavefront sensors, wavefront curvature sensors, pyramidal wavefront sensors, common-path interferometers, multi-faceted shearing interferometers, Ronchi testers, shearing interferometers, and / or other wavefront sensors that may be configured for use in ophthalmology. The wavefront sensor data provided by the wavefront sensor 120 may be represented in a variety of formats, including Zernike coefficients, e.g., Fourier transforms, cosine transforms, or Hartley transforms, or Taylor polynomials in cylindrical or Cartesian coordinates, or as an interferogram.

[0023] The OCT sensor 122 may be implemented with any one or combination of devices or device architectures configured to use relatively low coherence light and low coherence interferometry to capture micrometer and / or sub-micrometer resolution two-dimensional and three-dimensional images from within an optically scattering medium, such as the optical target 102, and provide associated OCT sensor data. For example, the OCT sensor 122 may be implemented as any one or combination of OCT sensor architectures that may be configured for use in ophthalmology. The eye tracker 130 may be implemented with 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 a conventional eye tracker or a fundus camera, and configured to provide associated eye tracker data. In some embodiments, the eye tracker 130 may be configured to capture one or more types of images of the Purkinje reflection associated with the target 102.

[0024] 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 suitable instructions, such as, for example, software instructions implementing control loops or processes for controlling various operations of ocular aberration analysis system 100 and / or elements of ocular aberration analysis system 100). Such software instructions may also implement methods for processing sensor signals, determining sensor information, providing user feedback (e.g., via user interface 146), querying a device for operating parameters, selecting operating parameters for a device, or performing any of the various operations described herein (e.g., operations performed by logic devices of the various devices of ocular aberration analysis system 100).

[0025] Additionally, 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, 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 analysis system 100, as appropriate. For example, the controller 140 may be adapted to store, for example, sensor signals, sensor information, complex analysis parameters, calibration parameters, a series of calibration points, training data, reference data, and / or other operating parameters over time, and provide such stored data to other elements of the ocular aberration analysis system 100. In some embodiments, the controller 140 may be integrated with a user interface 146.

[0026] The user interface 146 may be implemented as one or more of a display, a touch screen, a keyboard, a mouse, a joystick, a knob, a virtual reality headset, and / or any other device capable of accepting user input and / or providing feedback to a user. In various embodiments, the user interface 146 may be adapted to provide user input to other devices of the ocular aberration analysis system 100, such as the control unit 140. The user interface 146 may also be implemented by one or more logic devices, which may be adapted to execute instructions, such as software instructions, that perform any of the various processes and / or methods described herein. For example, the user interface 146 may 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 for one or more operations, and / or perform various other processes and / or methods described herein.

[0027] In some embodiments, the user interface 146 may be adapted to accept user input, for example, to form a communication link (e.g., to the server 150 and / or the distributed server system 154), to select certain parameters for operation of the ocular aberration analysis system 100, to select a manner of processing the sensor data, to adjust the position and / or orientation of the articulated model target, and / or to otherwise facilitate operation of the ocular aberration analysis system 100 and devices therein. Once the user interface 146 accepts the user input, the user input may be transmitted to other devices of the system 100 via one or more communication links. In one embodiment, the user interface 146 may be adapted to display time series of various sensor data and / or other parameters as part of a display including graphs or maps of such data and / or parameters. In some embodiments, the user interface 146 may be adapted to accept the user input and, for example, to 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 analysis system 100.

[0028] Other module 148 may include any one or combination of sensors and / or devices configured to facilitate operation of ocular aberration analysis system 100. For example, other module 148 may include a temperature sensor configured to measure one or more temperatures associated with operation of one or more elements of ocular aberration analysis system 100, a humidity sensor configured to measure ambient humidity around ocular aberration analysis system 100, a vibration sensor configured to measure amplitude and / or presence associated with operation of ocular aberration analysis system 100, a patient sensor configured to measure a posture, movement or other characteristic of a patient-supplied optical target 102, and / or other sensors that may provide sensor data useful for facilitating operation of ocular aberration analysis system 100 and / or correcting common system errors typical to operation of ocular aberration analysis system 100. In further embodiments, other module 148 may include additional illumination and camera systems configured to capture images of one or more types of Purkinje reflexes associated with target 102, similar to the combination of LED array 132 and eye tracker 130.

[0029] The server 150 may be implemented, for example, similarly to the controller 140 and may include various elements of a personal computer or server computer used to store, process, and / or otherwise manipulate relatively large data sets associated with one or more patients, including relatively large training data sets for training neural networks or performing other types of machine learning, as described herein. The distributed server system 154 may be implemented, for example, as a distributed combination of multiple embodiments of the controller 140 and / or the server 150 and may include networking and storage devices and capabilities configured to facilitate the storage, processing, and / or other manipulation of relatively large data sets, including relatively large training data sets for training neural networks or performing other types of machine learning, in a distributed setting, as described herein. The network 152 may 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.

[0030] 2A-2B show block diagrams of aberration analysis characterization targets 202A-202B of the ocular aberration analysis system 100 according to an embodiment of the present disclosure. In the embodiment shown in FIG. 2A, the aberration analysis characterization target 202A may be implemented as a single-pass model target configured to characterize optical aberrations associated with the ocular aberration analysis system 100. As shown in FIG. 2A, the aberration analysis characterization target / single-pass model target 202A includes a laser or SLD source 260 generating a source beam 211 that passes 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, the source 260 may be configured to generate, for example, a diverging spherical wavefront with controllable dispersion power, a converging spherical wavefront with controllable dispersion power, or a plane wave with zero power. Lens system 262 is coupled to a linear motion actuator 264 via mount 265, which allows lens system 262 to move along its optical axis to vary the defocus aberration of single pass model target 202A and generate a plurality of reference interferograms and corresponding wavefront sensor data (e.g., provided by wavefront sensor 120). Such reference interferograms and / or corresponding wavefront sensor data may be collected and stored, for example, as aberrometer model 360 of FIG. 3, and used to correct system aberrations associated with ocular aberration analysis system 100, as described herein.

[0031] In some embodiments, lens system 262 may be implemented as a lens traceable by the National Institute of Standards and Technology (NIST), and linear motion actuator 264 may be implemented as a relatively high precision actuator stage configured to position lens system 262 at a series of spaced positions from source 260 to generate source beam 211 having known and predefined defocus powers (e.g., defocus aberrations), such as, for example, −12 to 6 diopters or 0 to +−5 diopters, in 5.0D steps or higher resolution steps, according to various defocus aberrations commonly experienced by patients, as observed by ocular aberration analysis system 100. The resulting aberrometer model 360 may be used to compensate for various system aberrations, including those due to shot noise, thermal variations, and vibrations.

[0032] As shown in FIG. 2B, the aberration analysis characterization target / double-pass model target 202B includes an exchangeable ocular aberration model 270 removably coupled to a six degree of freedom (6DOF) motion actuator 272 via one or more mounts 273. The 6DOF motion actuator 272 is configured to vary the position and / or orientation of the exchangeable ocular aberration model 270 to generate a plurality of selected (e.g., known) alignment misalignments (e.g., relative to an optical axis of the ocular aberration analysis system 100) and a corresponding plurality of wavefront sensor data sets. Such alignment misalignments and corresponding wavefront sensor data may be collected and stored, for example, as the eye model 370 of FIG. 3, and may be used to train a complex or compact analysis engine to detect alignment misalignments associated with the optical target 102 and correct the corresponding wavefront sensor data, as described herein.

[0033] In some embodiments, the interchangeable ocular aberration model 270 may form one element of a series of interchangeable ocular aberration models, each formed by an exact amount of predefined ocular aberration (e.g., represented by exact and predefined Zernike coefficient amplitudes, such as amplitudes expressed in microns for a sixth order Zernike expansion). In one embodiment, such an interchangeable ocular aberration model may be cut on a contact lens lathe using transparent poly(methyl methacrylate) (PMMA). More generally, such an interchangeable ocular aberration model may be measured by a third party profiler with traceability to NIST for ground truth comparison with measurements performed by the ocular aberration analysis system 100. In various embodiments, the 6DOF motion actuator 272 may be configured to provide micrometer resolution positioning of the interchangeable ocular aberration model 270 along the x-, y-, and z-axes and microradian orientation of the interchangeable ocular aberration model 270 in the θy (tip), θx (tilt), and θz (twist) directions, as shown by representative coordinate frames 280A-280B.

[0034] In general operation, each interchangeable ocular aberration model 270 in a set (e.g., a set of 10 or more) may be further attached to a 6DOF motion actuator 272, and the controller 140 may be configured to control the 6DOF motion actuator 272 to position and / or orient the interchangeable ocular aberration model 270 in a series of relative positions and / or orientations (e.g., relative to the optical axis of the ocular aberration analysis system 100) within a range of misalignments typically experienced by patients as observed by the ocular aberration analysis system 100. In one embodiment, the series of misalignments may include approximately 40,000 different misalignments. In various embodiments, the combined series of misalignments and the corresponding series of wavefront sensor data (e.g., provided by the wavefront sensor 120) may form a supervised data set (e.g., eye model 370), which may be used to determine a complex analysis engine or a compact analysis engine, as described herein. Such an analysis engine may be used to compensate for misalignment of the optical target 102, which may also be measured by the eye tracker 130. More generally, the combination of characterizations performed by the aberration analysis characterization targets 202A-202B may be used to compensate or correct both system aberrations and errors in the wavefront sensor data due to misalignment of the optical target 102.

[0035] Fig. 3 illustrates a block diagram of an ocular aberration analysis system 300 according to one embodiment of the present disclosure. In the embodiment illustrated in Fig. 3, the ocular aberration analysis system 300 may be configured to use characterization data generated by the ocular aberration analysis system 100 via the aberration analysis characterization targets 202A-202B to generate a complex analysis engine 350 and / or a compact analysis engine 340, which may be used during operation of the ocular aberration analysis system 100 to provide substantially real-time monitoring and user feedback (e.g., updates at a frequency of 30 Hz or greater) of the optical aberration analysis of the optical target 102 while continuously compensating for common characterization errors, as described herein.

[0036] 3, ocular aberration analysis system 300 is similar to ocular aberration analysis system 100, but with additional details regarding various data structures and executable program instructions used in the operation of ocular aberration analysis system 100 or ocular aberration analysis system 300. For example, controller 140 is shown implemented with compact analysis engine 340, and server 150 and distributed server system 154 are each shown implemented with or storing one or more of aberrometer model 360, eye model 370, training data 392, supervised learning engine 390, complex analysis / neural network engine 350, and compact analysis engine 340. Although dashed lines generally indicate optional storage and / or implementation of certain elements, in various embodiments, controller 140, server 150, and distributed server system 154 may each implement or store any of the identified elements and / or additional elements, as described herein.

[0037] 6, the aberrometer model 360 may be generated by collecting sensor data associated with use of the single pass model target 202A and characterizing the ocular aberration analysis system 100, the eye model 370 may be generated by collecting sensor data associated with use of the double pass model target 202B and characterizing a parameter space associated with the optical target 102, and the training data 392 may be generated by incorporating the aberrometer model 360 and / or the eye model 370 and / or by generating and collecting simulation training data sets. The supervised learning engine 350 may be implemented as a static learning engine and / or according to a procedurally generated learning engine (e.g., a learning engine updatable with a genetic algorithm) configured to generate the complex analysis engine 350 using the training data 392. The complex analysis engine 350 may be implemented using other complex analysis techniques, including, for example, a deep neural network and / or various other neural network architectures or complex analysis techniques, including dense K-Nearest Neighbor (k-NN) databases for classification and / or regression, as described herein. The compact analysis engine 340 may be implemented as a compact form of the complex analysis engine 350, such as a form more suitable for relatively low resource but high performance execution by the control unit 140, and thus may be implemented as a deep neural network and / or other complex analysis techniques. In certain embodiments, the compact analysis engine 340 may be implemented as a neural network having fewer hidden layers and / or neurons / layers than the complex analysis engine 350.

[0038] FIG. 4 illustrates a flow diagram of a process 400 for characterizing the ocular aberration analysis system 100 and / or the ocular aberration analysis system 300 according to an embodiment of the present disclosure. It should be understood that any step, sub-step, sub-process, or block of the process 400 may be performed in a different order or arrangement than the embodiment illustrated by FIG. 4. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, the inputs of the blocks, the outputs of the blocks, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to the next portion of the corresponding process. Although the process 400 is described with reference to the systems, processes, control loops, and images described with respect to FIGS. 1-3, the process 400 may be performed by other systems that are different from those systems, processes, control loops, and images, including, for example, different selections of electronic devices, sensors, assemblies, mobile structures, and / or attributes of the mobile structures.

[0039] In block 402, an aberrometer model associated with the ocular aberration analysis system is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to control a source 260 of a single-pass model target 202A positioned as an optical target 102 observed by the ocular aberration analysis system 100 to generate a source beam 211 that passes through a lens system 262 and illuminates the wavefront sensor 120 and / or other elements of the ocular aberration analysis system 100. The controller 140 may be configured to vary a defocus aberration of the single-pass model target 202A according to a number of selected defocus powers, for example, to generate a number of wavefront sensor data sets provided by the wavefront sensor 120. The controller 140, the server 150, and / or the distributed server system 154 may be configured to determine system aberrations associated with the ocular aberration analysis system 100 based at least in part on the number of wavefront sensor data sets provided by the wavefront sensor 120. The system aberrations and / or the associated wavefront sensor data set may be stored (eg, on server 150 and / or distributed server system 154) as an aberrometer model 360.

[0040] In block 404, an eye model associated with the ocular aberration analysis system is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to control the beacon 110 of the ocular aberration analysis system 100 to generate the probe beam 111 to illuminate the double-pass model target 202B positioned as the optical target 102 observed by the ocular aberration analysis system 100, and further to illuminate (e.g., by 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 analysis system 100. The controller 140 may be configured to, for example, vary the position and / or orientation of the exchangeable ocular aberration model 270 of the double-pass model target 202B relative to the optical axis 111 of the ocular aberration analysis system 100 according to a number of selected alignment misalignments to generate a number of wavefront sensor data sets provided by the corresponding wavefront sensors 120. The selected misalignments and / or the corresponding wavefront sensor data sets may be stored (e.g., in the server 150 and / or the distributed server system 154) as an eye model 370. Similar techniques may be used to incorporate the eye tracker data from the eye tracker 130 and the OCT sensor data from the OCT sensor 122 into the eye model 370.

[0041] At 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 at block 402 and / or the eye model 370 generated at 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 including 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 hidden layer including a plurality of neurons. The controller 140, server 150, and / or distributed server system 154 may be configured to train at least a trainable weight matrix W associated with each neuron of the input layer, output layer, and hidden layer of the neural network 600 via the supervised learning engine 390 using the alignment misalignments of the eye model 370 as ground truth output data and the corresponding wavefront sensor data set of the eye model 370 as training input data, as described herein. The resulting deep neural network may be stored and used as the complex analysis engine 350. In other embodiments, the controller 140, server 150, and / or distributed server system 154 may be configured to generate a plurality of corrected wavefront sensor data sets corresponding to a plurality of selected alignment misalignments of the eye model 370 based at least in part on system aberrations associated with an ocular aberration analysis system in the aberrometer model 360, before forming the neural network 600 and training 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.

[0042] At block 408, a compact analysis engine is generated. For example, the controller 140, the server 150, and / or the distributed server system 154 may 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 weight matrix W associated with each neuron of the input layer, the output layer, and / or the hidden layer of the compact neural network 600 based at least in part on one or more complex analysis parameters associated with the multiple hidden layers 630-639 of the complex analysis engine 350. Once generated, the compact analysis engine 340 may be stored or integrated or implemented by the controller 140. The controller 140 may use the compact analysis engine 340 to generate user feedback (e.g., displaying a view including various graphics) and reliable and accurate monitoring of ocular alignment deviations, ocular aberrations, and / or other characteristics of the optical target 102 in substantially real time (e.g., 30 frames / second), as described herein.

[0043] FIG. 5 illustrates a flow diagram of a process 500 for operating the ocular aberration analysis system 100 and / or the ocular aberration analysis system 300 according to an embodiment of the present disclosure. It should be understood that any step, sub-step, sub-process, or block of the process 500 may be performed in a different order or arrangement than the embodiment illustrated by FIG. 5. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, the inputs of the blocks, the outputs of the blocks, various sensor signals, sensor information, calibration parameters, and / or other operating parameters may be stored in one or more memories before moving to the next portion of the corresponding process. Although the process 500 is described with reference to the systems, processes, control loops, and images described with respect to FIGS. 1-3, the process 500 may be performed by other systems that are different from those systems, processes, control loops, and images, including, for example, different selections of electronic devices, sensors, assemblies, mobile structures, and / or attributes of the mobile structures.

[0044] At block 502, an output of the ocular aberration analysis is received. For example, controller 140, server 150, and / or distributed server system 154 may be configured to receive ocular aberration analysis output data that includes at least wavefront sensor data provided by wavefront sensor 120. More generally, the ocular aberration analysis output data may include any one or more of wavefront sensor data provided by wavefront sensor 120, OCT sensor data provided by OCT sensor 122, eye tracker data provided by eye tracker 130, and / or other output data provided by ocular aberration analysis system 100, as described herein.

[0045] In block 504, an estimated ocular alignment misalignment is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target 102 (e.g., the patient's eye) observed by the ocular aberration analysis system 100 based at least in part on the ocular aberration analysis output data received in block 502. In some embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine an estimated ocular alignment misalignment 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, the server 150, and / or the distributed server system 154 may be configured to determine the estimated ocular alignment misalignment by determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment from a wavefront corresponding to the wavefront sensor measurement.

[0046] For example, in one embodiment, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine an estimated ocular alignment shift by: determining, for each wavefront sensor measurement, a corresponding estimated relative position and / or orientation of the optical target 102 (e.g., an estimated ocular alignment of the optical target 102); identifying one or more clusters of the estimated relative positions and / or orientations of the optical targets based at least in part on one or more pre-set or adaptive cluster thresholds; determining a fixed alignment based at least in part on the centroid of a largest cluster among the one or more identified clusters; and determining, for each wavefront sensor measurement, an estimated ocular alignment shift based at least in part on a difference between the fixed alignment and the estimated relative position and / or orientation of the optical target corresponding to the wavefront sensor measurement (e.g., a difference between the fixed alignment and an estimated ocular alignment of the optical target 102 corresponding to a time series of wavefront sensor measurements). In a related embodiment, the ocular aberration analysis system 100, including the eye tracker 130, the controller 140, the server 150, and / or the distributed server system 154, may be configured to determine an estimated ocular alignment misalignment by determining, for each wavefront sensor measurement, a corresponding fixation status of the optical target based at least in part on a fixation threshold parameter corresponding to the wavefront sensor measurement and the eye tracker sensor data, and filtering out a subset of the wavefront sensor measurements based at least in part on the determined corresponding fixation status before determining a corresponding estimated relative position and / or orientation of the optical target 102. For example, such an approach may filter out wavefront sensor measurements obtained while the optical target 102 is detected by the eye tracker 130 as not being fixed.

[0047] In block 506, the corrected ocular aberration analysis output data is determined. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the corrected ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment determined in block 504 and / or the wavefront sensor data received in block 502. In some embodiments in which 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 may be configured to determine the corrected ocular aberration analysis output data by determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with the wavefront sensor measurement and generating an average wavefront sensor measurement as the corrected wavefront sensor measurement based at least in part on each wavefront sensor measurement with an associated estimated ocular alignment misalignment less than or equal to a preset maximum allowable misalignment. Such a preset maximum allowable misalignment may be selected or set by a manufacturer and / or a user of the ocular aberration analysis system 100.

[0048] In other embodiments, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the corrected ocular aberration analysis output data by determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with the wavefront sensor measurement, and for each wavefront sensor measurement with an associated estimated ocular alignment misalignment less than or equal to a preset maximum allowable misalignment, determining a corrected wavefront sensor measurement based at least in part on the wavefront sensor measurement and / or the associated estimated ocular alignment misalignment, and generating an average wavefront sensor measurement based at least in part on the corrected wavefront sensor measurement. For example, the controller 140, the server 150, and / or the distributed server system 154 may be configured to determine the corrected wavefront sensor measurement by applying the complex analysis engine 350 or the compact analysis engine 340 of the ocular aberration analysis system 100 to each wavefront sensor measurement and generating an estimated ocular alignment misalignment and / or a corrected wavefront sensor measurement from the corresponding wavefront, as described herein.

[0049] 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 analysis output data received in block 502 based at least in part on the estimated ocular alignment misalignment 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 a metric of the ocular alignment misalignment based at least in part on the estimated ocular alignment misalignment and reporting the metric of the ocular alignment misalignment via the user interface 146 of the ocular aberration analysis 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 misalignment determined in block 504 and / or the corrected ocular aberration analysis output data determined in block 506. In various embodiments, such user feedback may include a substantially real-time display view of an ocular alignment misalignment metric based at least in part on the estimated ocular alignment misalignment determined in block 504, as described herein, a substantially real-time display view of an ocular aberration map based at least in part on the corrected ocular aberration analysis output data determined in block 504 and / or an audible and / or visual alarm indicating that at least one of the estimated ocular alignment misalignments is greater than a pre-set maximum allowable misalignment.

[0050] Thus, as described herein, embodiments of the present disclosure can provide user feedback (e.g., displaying views including various graphics) and reliable and accurate monitoring in substantially real-time (e.g., 30 frames / second) of ocular alignment misalignments, ocular aberrations, and / or other characteristics of the optical target 102. Such embodiments can be used to assist in various types of clinical and intra-operative eye examinations and help improve surgical outcomes.

[0051] FIG. 6 illustrates a diagram of a multi-layer or “deep” neural network (DNN) 600 according to an embodiment of the present disclosure. In some embodiments, the neural network 600 may represent a neural network used to implement each of the one or more models and / or analytical engines described with respect to system 100 and / or system 300. The neural network 600 processes input data 610 using an input layer 620. In various embodiments, the input data 610 may correspond to aberration analysis output data and / or training data provided to one or more models and / or analytical engines to generate and / or train one or more models and / or analytical models, as described herein. In some embodiments, the input layer 620 may include multiple neurons or nodes used to condition the input data 610 by scaling, biasing, filtering, range limiting, and / or otherwise conditioning the input data 610 for processing by the remainder of the neural network 600. In other embodiments, the input layer 620 may be configured to echo the input data 610 (e.g., if the input data 610 has already been appropriately scaled, biased, filtered, range-limited, and / or otherwise adjusted). 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 of the neurons in the hidden layer 630 generates an output that is then propagated through one or more additional hidden layers terminating in the hidden layer 639. The hidden layer 639 includes multiple neurons / nodes that process the output from the previous hidden layer. In the embodiment shown in FIG. 6, the output of the hidden layer 639 is provided to the output layer 640. In various embodiments, the output layer 640 includes one or more neurons / nodes that may be used to adjust the output from the hidden layer 639 by scaling, biasing, filtering, range-limiting, and / or otherwise adjust the output from the hidden layer 639 to form the output data 650.In alternative embodiments, neural network 600 may be implemented according to different neural network or other processing architectures, including neural networks with only one hidden layer, neural networks with recurrent layers, and / or various other neural network architectures including K-Nearest Neighbor (k-NN) databases or complex analysis techniques for classification and / or regression.

[0052] In some embodiments, the input layer 620, the hidden layers 631-639, and / or the output layer 640 each include one or more neurons. In one embodiment, the input layer 620, the hidden layers 631-639, and / or the output layer 640 each may include the same or different numbers of neurons. In a particular embodiment, the neural network 600 may include about 6 layers in total with up to 2000-4000 neurons in each layer. In various embodiments, each such constituent neuron may be configured to receive a combination of its inputs x (e.g., a weighted sum generated using a trainable weight matrix / vector W), to receive any trainable bias b, and to apply an activation function f to generate an output a, e.g., according to the formula a=f(Wx+b). The activation function f may be implemented, for example, as a normalized linear unit activation function, or according to any one or combination of activation functions with upper and / or lower bounds, a logistic sigmoid function, a hyperbolic tangent function, and / or other activation function forms. As described herein, 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. In certain embodiments, corresponding to regression applications, only the neurons in the output layer 640 may be configured to apply such linear activation functions to generate their respective outputs.

[0053] In various embodiments, the neural network 600 may be trained using supervised learning (e.g., implemented as a supervised learning engine 390) such as by systematically providing selected training data sets (e.g., training data 392) (each training data set including an input training data set 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) to the neural network 600, and then determining and / or otherwise comparing the difference (e.g., "training error") between the resulting output data 650 (e.g., the training output data provided by the neural network 600) and the ground truth output data. In some embodiments, the training error may be fed back to the neural network 600 to adjust various trainable weights, biases, and / or other complex analytical parameters of the neural network 600. In some embodiments, such training error may be provided as feedback to the neural network 600 using, for example, one or various backpropagation and / or other backpropagation methods including stochastic gradient descent. In one or more embodiments, a relatively large set of selected training datasets may be presented to neural network 600 multiple times until an overall loss function (e.g., mean squared error based on the difference between each training dataset) converges to or below a pre-set maximum tolerable loss threshold.

[0054] In further embodiments, the supervised learning engine 390 may be configured to include semi-supervised learning, weakly supervised learning, active learning, structured prediction, and / or other general machine learning methods to aid in training the complex analysis parameters of the neural network 600 (e.g., to generate the complex analysis engine 350), as described herein. For example, the supervised learning engine 390 may be configured to generate simulation training data sets, each set including simulation input training data and a corresponding simulation ground truth output data set, and to perform supervised learning based at least in part on the simulation training data sets. Each of the simulation input training data set and the simulation ground truth data set may be generated by modifying the input training data (e.g., to adjust aberration parameters and / or alignment misalignments associated with the simulation double-pass model target) and interpolating non-simulated ground truth data to generate the corresponding simulation ground truth data. In various embodiments, the supervised learning engine 390 may be configured to simulate millions of sets of simulated training data (each of which deviates at least slightly from the training data set corresponding to the eye model 370) and train the neural network 600 according to the millions of such sets of simulated training data.

[0055] Where appropriate, the various embodiments provided by the present disclosure may be implemented using hardware, software, or a combination of hardware and software. Also, where appropriate, the various hardware and / or software components described herein may be combined into composite components including software, hardware, and / or both without departing from the spirit of the present disclosure. Where appropriate, the various hardware and / or software components described herein may be separated into subcomponents including software, hardware, or both without departing from the spirit of the present disclosure. Furthermore, where appropriate, it is contemplated that software components may be implemented as hardware components and vice versa.

[0056] Software according to the present disclosure, such as non-transitory instructions, program code, and / or data, may be stored on one or more non-transitory machine-readable media. It is also contemplated that the software specified herein may be implemented using one or more general-purpose or special-purpose computers and / or computer systems, networked and / or not. Where appropriate, the order of the various steps described herein may be changed, combined into composite steps, and / or separated into sub-steps to provide the features described herein.

[0057] The above-described embodiments illustrate but do not limit the present invention. It should also be understood that numerous modifications and variations are possible in accordance with the principles of the present invention. Therefore, the scope of the present invention is defined only by the following claims. The present disclosure also includes the following aspects. [Aspect 1] 1. An ocular aberration analysis system, comprising: a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system; a logic device configured to communicate with the wavefront sensor, the logic device comprising: receiving ocular aberration analysis output data including at least the wavefront sensor data provided by the wavefront sensor; determining an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberration analysis system based at least in part on the received ocular aberration analysis output data; generating user feedback corresponding to the received ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment. It is configured as follows: A logical device; An ocular aberration analysis system comprising: [Aspect 2] the received ocular aberration analysis output data includes eye tracker sensor data provided by an eye tracker of the ocular aberration analysis system; determining the estimated eye alignment misalignment based at least in part on the received eye tracker sensor data; generating the user feedback includes determining an ocular alignment misalignment metric based at least in part on the estimated ocular alignment misalignment; and reporting the ocular alignment misalignment metric via a user interface of the ocular aberration analysis system. 2. An ocular aberration analysis system according to embodiment 1. [Aspect 3] The logic device is determining corrected ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment and / or the received wavefront sensor data; generating the user feedback based at least in part on the estimated ocular alignment misalignment and / or the corrected ocular aberration analysis output data; 2. The ocular aberration analysis system according to claim 1, Aspect 4 The user feedback may include: a substantially real-time display view of an ocular alignment misalignment metric based at least in part on said estimated ocular alignment misalignment; a substantially real-time display view of an ocular aberration map based at least in part on said corrected ocular aberration analysis output data; and / or an audible and / or visual alarm indicating that the estimated ocular alignment misalignment is greater than a pre-set maximum allowable misalignment; 4. An ocular aberration analysis system according to claim 3, comprising: Aspect 5 The wavefront sensor data includes a time series of wavefront sensor measurements, and the determining of the corrected ocular aberration analysis output data includes: determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with said wavefront sensor measurement; generating an average wavefront sensor measurement based at least in part on each wavefront sensor measurement with an associated estimated ocular alignment misalignment that is less than or equal to a preset maximum allowable misalignment; 4. An ocular aberration analysis system according to claim 3, comprising: Aspect 6 The wavefront sensor data includes a time series of wavefront sensor measurements, and the determining of the corrected ocular aberration analysis output data includes: determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with said wavefront sensor measurement; - for each wavefront sensor measurement with an associated estimated ocular alignment misalignment less than or equal to a preset maximum tolerable misalignment, determining a corrected wavefront sensor measurement based at least in part on said wavefront sensor measurement and / or said associated estimated ocular alignment misalignment; generating an average wavefront sensor measurement based at least in part on the corrected wavefront sensor measurement; 4. An ocular aberration analysis system according to claim 3, comprising: Aspect 7 The determining of the corrected wavefront sensor measurements includes: applying a complex or compact analysis engine of the ocular aberration analysis system to the wavefront sensor measurements to generate estimated ocular alignment misalignments from corresponding wavefronts and / or the corrected wavefront sensor measurements. 7. The ocular aberration analysis system of claim 6, comprising: Aspect 8 The wavefront sensor data includes a time series of wavefront sensor measurements, and the determining the estimated ocular alignment misalignment includes: determining, for each wavefront sensor measurement, an ocular alignment misalignment estimated from the wavefront corresponding to said wavefront sensor measurement; 2. The ocular aberration analysis system of embodiment 1, comprising: Aspect 9 The wavefront sensor data includes a time series of wavefront sensor measurements, and the determining the estimated ocular alignment misalignment includes: determining, for each wavefront sensor measurement, a corresponding estimated relative position and / or orientation of the optical target; identifying one or more clusters of estimated relative positions and / or orientations of the optical targets based at least in part on one or more pre-defined or adaptive cluster thresholds; determining a fixed alignment based at least in part on a centroid of a largest cluster of the one or more identified clusters; determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment based at least in part on a difference between the fixed alignment and the estimated relative position and / or orientation of the optical target corresponding to the wavefront sensor measurement; 2. The ocular aberration analysis system of embodiment 1, comprising: Aspect 10 the received ocular aberration analysis output data includes eye tracker sensor data provided by an eye tracker of the ocular aberration analysis system, and the determining the estimated ocular alignment misalignment comprises: determining, for each wavefront sensor measurement, a corresponding fixation status of the optical target based at least in part on a fixation threshold parameter corresponding to the wavefront sensor measurement and eye tracker sensor data; filtering out a subset of the wavefront sensor measurements based at least in part on the determined corresponding fixation status prior to determining an estimated relative position and / or orientation of the corresponding optical target; 10. The ocular aberration analysis system of embodiment 9, comprising: Aspect 11 receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, said ocular aberration analysis output data including at least wavefront sensor data associated with an optical target monitored by said ocular aberration analysis system; determining an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberration analysis system based at least in part on the received ocular aberration analysis output data; generating user feedback corresponding to the received ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment; A method comprising: Aspect 12 the received ocular aberration analysis output data includes eye tracker sensor data provided by an eye tracker of the ocular aberration analysis system; determining the estimated eye alignment misalignment based at least in part on the received eye tracker sensor data; generating the user feedback includes determining an ocular alignment misalignment metric based at least in part on the estimated ocular alignment misalignment; and reporting the ocular alignment misalignment metric via a user interface of the ocular aberration analysis system. The method according to embodiment 11. Aspect 13 determining corrected ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment and / or the received wavefront sensor data; generating said user feedback based at least in part on said estimated ocular alignment misalignment and / or said corrected ocular aberration analysis output data; 12. The method of embodiment 11, further comprising: Aspect 14 The user feedback may include: a substantially real-time display view of an ocular alignment misalignment metric based at least in part on said estimated ocular alignment misalignment; a substantially real-time display view of an ocular aberration map based at least in part on said corrected ocular aberration analysis output data; and / or an audible and / or visual alarm indicating that the estimated ocular alignment misalignment is greater than a pre-set maximum allowable misalignment; 14. The method of embodiment 13, comprising: Aspect 15 The wavefront sensor data includes a time series of wavefront sensor measurements, and the determining of the corrected ocular aberration analysis output data includes: determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with said wavefront sensor measurement; generating an average wavefront sensor measurement based at least in part on each wavefront sensor measurement with an associated estimated ocular alignment misalignment that is less than or equal to a preset maximum allowable misalignment; 14. The method of embodiment 13, comprising: Aspect 16 The wavefront sensor data includes a time series of wavefront sensor measurements, and the determining of the corrected ocular aberration analysis output data includes: determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with said wavefront sensor measurement; - for each wavefront sensor measurement with an associated estimated ocular alignment misalignment less than or equal to a preset maximum tolerable misalignment, determining a corrected wavefront sensor measurement based at least in part on said wavefront sensor measurement and / or said associated estimated ocular alignment misalignment; generating an average wavefront sensor measurement based at least in part on the corrected wavefront sensor measurement; 14. The method of embodiment 13, comprising: Aspect 17 The determining of the corrected wavefront sensor measurements includes: applying a complex or compact analysis engine of the ocular aberration analysis system to the wavefront sensor measurements to generate estimated ocular alignment misalignments from corresponding wavefronts and / or the corrected wavefront sensor measurements. 17. The method of embodiment 16, comprising: Aspect 18 The wavefront sensor data includes a time series of wavefront sensor measurements, and the determining the estimated ocular alignment misalignment includes: determining, for each wavefront sensor measurement, an ocular alignment misalignment estimated from the wavefront corresponding to said wavefront sensor measurement; 12. The method of embodiment 11, comprising: Aspect 19 The wavefront sensor data includes a time series of wavefront sensor measurements, and the determining the estimated ocular alignment misalignment includes: determining, for each wavefront sensor measurement, a corresponding estimated relative position and / or orientation of the optical target; identifying one or more clusters of estimated relative positions and / or orientations of the optical targets based at least in part on one or more pre-defined or adaptive cluster thresholds; determining a fixed alignment based at least in part on a centroid of a largest cluster of the one or more identified clusters; determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment based at least in part on a difference between the fixed alignment and the estimated relative position and / or orientation of the optical target corresponding to the wavefront sensor measurement; 12. The method of embodiment 11, comprising: Aspect 20 the received ocular aberration analysis output data includes eye tracker sensor data provided by an eye tracker of the ocular aberration analysis system, and the determining the estimated ocular alignment misalignment comprises: determining, for each wavefront sensor measurement, a corresponding fixation status of the optical target based at least in part on a fixation threshold parameter corresponding to the wavefront sensor measurement and eye tracker sensor data; filtering out a subset of the wavefront sensor measurements based at least in part on the determined corresponding fixation status prior to determining an estimated relative position and / or orientation of the corresponding optical target; 20. The method of embodiment 19, comprising:

Claims

1. 1. An ocular aberration analysis system, comprising: a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system; a logic device configured to communicate with the wavefront sensor, the logic device comprising: receiving ocular aberration analysis output data including at least the wavefront sensor data provided by the wavefront sensor; determining an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberration analysis system based at least in part on the received ocular aberration analysis output data; generating user feedback corresponding to the received ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment. It is configured as follows: A logical device; Including, The logic device is determining corrected ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment and / or the received wavefront sensor data; generating the user feedback based at least in part on the estimated ocular alignment misalignment and / or the corrected ocular aberration analysis output data; It is structured as follows: The wavefront sensor data includes a time series of wavefront sensor measurements, and determining the corrected ocular aberration analysis output data includes: determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with said wavefront sensor measurement; - for each wavefront sensor measurement with an associated estimated ocular alignment misalignment less than or equal to a preset maximum tolerable misalignment, determining a corrected wavefront sensor measurement based at least in part on said wavefront sensor measurement and / or said associated estimated ocular alignment misalignment; generating an average wavefront sensor measurement based at least in part on the corrected wavefront sensor measurement; An ocular aberration analysis system comprising:

2. the received ocular aberration analysis output data includes eye tracker sensor data provided by an eye tracker of the ocular aberration analysis system; determining the estimated eye alignment misalignment based at least in part on the received eye tracker sensor data; generating the user feedback includes determining an ocular alignment misalignment metric based at least in part on the estimated ocular alignment misalignment; and reporting the ocular alignment misalignment metric via a user interface of the ocular aberration analysis system. The ocular aberration analysis system according to claim 1 .

3. The user feedback may include: a substantially real-time display view of an ocular alignment misalignment metric based at least in part on said estimated ocular alignment misalignment; a substantially real-time display view of an ocular aberration map based at least in part on the corrected ocular aberration analysis output data; and / or an audible and / or visual alarm indicating that the estimated ocular alignment misalignment is greater than a pre-set maximum allowable misalignment; The ocular aberration analysis system according to claim 1 ,

4. Determining the corrected ocular aberration analysis output data comprises: determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with said wavefront sensor measurement; generating an average wavefront sensor measurement based at least in part on each wavefront sensor measurement with an associated estimated ocular alignment misalignment that is less than or equal to a preset maximum allowable misalignment; The ocular aberration analysis system according to claim 1 ,

5. Determining the corrected wavefront sensor measurements includes: applying a complex or compact analysis engine of the ocular aberration analysis system to the wavefront sensor measurements to generate estimated ocular alignment misalignments from corresponding wavefronts and / or the corrected wavefront sensor measurements. The ocular aberration analysis system according to claim 1 ,

6. Determining the estimated eye alignment misalignment comprises: determining, for each wavefront sensor measurement, an ocular alignment misalignment estimated from the wavefront corresponding to said wavefront sensor measurement; The ocular aberration analysis system according to claim 1 ,

7. Determining the estimated eye alignment misalignment comprises: determining, for each wavefront sensor measurement, a corresponding estimated relative position and / or orientation of the optical target; identifying one or more clusters of estimated relative positions and / or orientations of the optical targets based at least in part on one or more pre-defined or adaptive cluster thresholds; determining a fixed alignment based at least in part on a centroid of a largest cluster of the one or more identified clusters; determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment based at least in part on a difference between the fixed alignment and the estimated relative position and / or orientation of the optical target corresponding to the wavefront sensor measurement; The ocular aberration analysis system according to claim 1 ,

8. The received ocular aberration analysis output data includes eye tracker sensor data provided by an eye tracker of the ocular aberration analysis system, and determining the estimated ocular alignment misalignment comprises: determining, for each wavefront sensor measurement, a corresponding fixation status of the optical target based at least in part on a fixation threshold parameter corresponding to the wavefront sensor measurement and eye tracker sensor data; filtering out a subset of the wavefront sensor measurements based at least in part on the determined corresponding fixation status prior to determining an estimated relative position and / or orientation of the corresponding optical target; The ocular aberration analysis system according to claim 7 ,

9. receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, the ocular aberration analysis output data including at least wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system; determining an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberration analysis system based at least in part on the received ocular aberration analysis output data; generating user feedback corresponding to the received ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment; Including, determining corrected ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment and / or the received wavefront sensor data; generating said user feedback based at least in part on said estimated ocular alignment misalignment and / or said corrected ocular aberration analysis output data; Further comprising: The wavefront sensor data includes a time series of wavefront sensor measurements, and determining the corrected ocular aberration analysis output data includes: determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with said wavefront sensor measurement; - for each wavefront sensor measurement with an associated estimated ocular alignment misalignment less than or equal to a preset maximum tolerable misalignment, determining a corrected wavefront sensor measurement based at least in part on said wavefront sensor measurement and / or said associated estimated ocular alignment misalignment; generating an average wavefront sensor measurement based at least in part on the corrected wavefront sensor measurement; A method comprising:

10. the received ocular aberration analysis output data includes eye tracker sensor data provided by an eye tracker of the ocular aberration analysis system; determining the estimated eye alignment misalignment based at least in part on the received eye tracker sensor data; generating the user feedback includes determining an ocular alignment misalignment metric based at least in part on the estimated ocular alignment misalignment; and reporting the ocular alignment misalignment metric via a user interface of the ocular aberration analysis system.

10. The method of claim 9.

11. The user feedback may include: a substantially real-time display view of an ocular alignment misalignment metric based at least in part on said estimated ocular alignment misalignment; a substantially real-time display view of an ocular aberration map based at least in part on the corrected ocular aberration analysis output data; and / or an audible and / or visual alarm indicating that the estimated ocular alignment misalignment is greater than a pre-set maximum allowable misalignment; 10. The method of claim 9, comprising:

12. The method of claim 11, further comprising: determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment associated with said wavefront sensor measurement; generating an average wavefront sensor measurement based at least in part on each wavefront sensor measurement with an associated estimated ocular alignment misalignment that is less than or equal to a preset maximum allowable misalignment; 10. The method of claim 9, comprising:

13. Determining the corrected wavefront sensor measurements includes: applying a complex or compact analysis engine of the ocular aberration analysis system to the wavefront sensor measurements to generate estimated ocular alignment misalignments from corresponding wavefronts and / or the corrected wavefront sensor measurements.

10. The method of claim 9, comprising:

14. Determining the estimated ocular alignment misalignment comprises: determining, for each wavefront sensor measurement, an ocular alignment misalignment estimated from the wavefront corresponding to said wavefront sensor measurement; 10. The method of claim 9, comprising:

15. A method for detecting ocular aberrations comprising: receiving ocular aberration analysis output data from an ocular aberration analysis system including a wavefront sensor, the ocular aberration analysis output data including at least wavefront sensor data associated with an optical target monitored by the ocular aberration analysis system; and determining an estimated ocular alignment misalignment corresponding to a relative position and / or orientation of the optical target monitored by the ocular aberration analysis system based at least in part on the received ocular aberration analysis output data; generating user feedback corresponding to the received ocular aberration analysis output data based at least in part on the estimated ocular alignment misalignment; Including, The wavefront sensor data includes a time series of wavefront sensor measurements, and determining the estimated ocular alignment misalignment includes: determining, for each wavefront sensor measurement, a corresponding estimated relative position and / or orientation of the optical target; identifying one or more clusters of estimated relative positions and / or orientations of the optical targets based at least in part on one or more pre-defined or adaptive cluster thresholds; determining a fixed alignment based at least in part on a centroid of a largest cluster of the one or more identified clusters; determining, for each wavefront sensor measurement, an estimated ocular alignment misalignment based at least in part on a difference between the fixed alignment and the estimated relative position and / or orientation of the optical target corresponding to the wavefront sensor measurement; A method comprising:

16. The received ocular aberration analysis output data includes eye tracker sensor data provided by an eye tracker of the ocular aberration analysis system, and determining the estimated ocular alignment misalignment comprises: determining, for each wavefront sensor measurement, a corresponding fixation status of the optical target based at least in part on a fixation threshold parameter corresponding to the wavefront sensor measurement and eye tracker sensor data; filtering out a subset of the wavefront sensor measurements based at least in part on the determined corresponding fixation status prior to determining an estimated relative position and / or orientation of the corresponding optical target; 16. The method of claim 15, comprising:

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