Dry eye relief for ocular aberrometry systems and methods

Abstract

Techniques are disclosed for systems and methods for providing dry eye and / or tear film hydration monitoring in the context of ocular aberrometry. 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) can be configured to receive (1102) ocular aberrometry output data including at least wavefront sensor data provided by the wavefront sensor (120), determine (1106) a tear film hydration state associated with the optical target based at least in part on the received ocular aberrometry output data, and generate (1110) user feedback corresponding to the determined tear film hydration state based at least in part on the received aberrometry output data and / or the determined tear film hydration state.

Description

Technical Field

[0001] One or more embodiments of this disclosure relate generally to ocular aberration measurement, and more specifically, for example, to systems and methods for improving clinical or intraoperative ocular aberration measurement. Background Technology

[0002] Eye surgery may 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 eye's active optical components. For optimal postoperative visual outcomes, sound preoperative clinical assessment and surgical planning, as well as intraoperative monitoring of the execution of the surgical plan, are crucial.

[0003] Ocular aberration measurements performed by an ocular aberrometer are typically a common method for characterizing the eye preoperatively, monitoring surgical progress, and assessing 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 the patient. Furthermore, conventional ocular aberrometers are often combined with measurement and characterization techniques that may introduce inaccuracies into the resulting aberration characterization of the eye, complicating the recovery of accurate aberration measurements.

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

[0005] Techniques for providing improved eye aberration measurements are disclosed. According to one or more embodiments, the eye aberration measurement system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the eye aberration measurement system, and logic means configured to communicate with the wavefront sensor. The logic means may be configured to determine a complex analysis engine for the 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 the wavefront sensor data provided by the wavefront sensor. The logic means may also be configured to generate a compact analysis engine for the eye 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 based at least in part on wavefront sensor data provided by wavefront sensors 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, which, 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 wavefront sensors 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, the eye aberration measurement system may include a wavefront sensor configured to provide wavefront sensor data associated with an optical target monitored by the eye aberration measurement system, and logic means configured to communicate with the wavefront sensor. The logic means may be configured to receive eye aberration measurement output data including at least the wavefront sensor data provided by the wavefront sensor, determine, at least in part, 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 on the received eye aberration measurement output data, and generate user feedback corresponding to the received eye aberration measurement output data based, at least in part, on the estimated eye 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, which, 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 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, at least in part, 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 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.

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

[0012] 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, identifying singularities caused by imaging artifacts in the received eye aberration measurement output data, determining corrected aberration measurement output data based at least in part on the identified singularities and 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 corrected aberration measurement output data.

[0013] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions, which, 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 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; identifying singularities caused by imaging artifacts in the received eye aberration measurement output data; determining corrected aberration measurement output data based at least in part on the identified singularities and 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 corrected aberration measurement output data.

[0014] In a further embodiment, the 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 receive ocular aberration measurement output data, including at least the wavefront sensor data provided by the wavefront sensor, determine a tear film hydration state associated with the optical target based at least in part on the received ocular aberration measurement output data, and generate user feedback corresponding to the determined tear film hydration state based at least in part on the received aberration measurement output data and / or the determined tear film hydration state.

[0015] In other embodiments, 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 a tear film hydration state associated with the optical target based at least in part on the received ocular aberration measurement output data, and generating user feedback corresponding to the determined tear film hydration state based at least in part on the received aberration measurement output data and / or the determined tear film hydration state.

[0016] According to some embodiments, a non-transitory machine-readable medium may include a plurality of machine-readable instructions, which, 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 a tear film hydration state associated with the optical target based at least in part on the received ocular aberration measurement output data, and generating user feedback corresponding to the determined tear film hydration state based at least in part on the received aberration measurement output data and / or the determined tear film hydration state.

[0017] The scope of this 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 will gain a more complete understanding of the embodiments of the invention, and additional advantages of the invention may be realized. Reference will be made to the accompanying drawings, which will first be briefly described. Attached Figure Description

[0018] Figure 1 A block diagram of an eye aberration measurement system according to an embodiment of the present disclosure is shown.

[0019] Figures 2A to 2B The diagram illustrates a block diagram of an aberration measurement characterization target of an eye aberration measurement system according to an embodiment of the present disclosure.

[0020] Figure 3 A block diagram of an eye aberration measurement system according to an embodiment of the present disclosure is shown.

[0021] Figure 4 A flowchart illustrating the process of characterizing an eye aberration measurement system according to an embodiment of this disclosure is shown.

[0022] Figure 5 The illustration shows a flowchart of the process of operating an eye aberration measurement system according to an embodiment of the present disclosure.

[0023] Figure 6 A diagram illustrating a multilayer neural network according to an embodiment of this disclosure is shown.

[0024] Figures 7A to 7C The illustration shows a flowchart of a process for reducing errors caused by imaging artifacts in an eye aberration measurement system according to an embodiment of the present disclosure.

[0025] Figure 8 The illustration shows a flowchart of the process of operating an eye aberration measurement system according to an embodiment of the present disclosure.

[0026] Figure 9 The illustration shows a flowchart of a process for monitoring tear film hydration for an ocular aberration measurement system according to an embodiment of this disclosure.

[0027] Figure 10 The illustration shows a display view for monitoring tear film hydration in an ocular aberration measurement system, according to an embodiment of the present disclosure.

[0028] Figure 11 The illustration shows a flowchart of the process of operating an eye aberration measurement system according to an embodiment of the present disclosure.

[0029] The embodiments and advantages of the present invention can be best understood by referring to the following detailed description. It should be understood that the same reference numerals are used to identify the same elements illustrated in one or more figures. Detailed Implementation

[0030] According to various embodiments of this disclosure, the ocular aberration measurement system and method provide substantially real-time measurement and monitoring of aberrations in a patient's eye, reducing typical system and measurement errors in conventional systems. For example, when a patient's eye fixates during an ocular aberration measurement or examination, the eye naturally drifts. This fixation drift includes eye rolls, squints, torsions, and changes in x, y, and z positions. These alignment deviations can cause errors in the calculation of wavefront characterization aberrations of the eye. If the average of these alignment deviations is close to zero, averaging the wavefront aberration measurements across individual frames of the examined image stream is sufficient to eliminate most of the errors caused by misalignment. When the average alignment deviation of a known examination sequence is not close to zero, or when it cannot be confirmed that the alignment deviation has a near-zero mean, various strategies described herein can be used to reduce measurement errors or noise. These include averaging wavefront sensor data (e.g., represented by Zernike polynomial expansion) or a combination thereof, where the alignment deviation does not exceed a preset threshold; using sophisticated analytical methods (described herein) to correct for misalignment-based errors in the wavefront sensor data; determining eye fixation status (e.g., for each wavefront measurement) based on clustering analysis applied to a series of wavefront measurements and / or based on eye-tracking data. The estimated alignment deviation and / or its impact on the wavefront measurements can be reduced to an eye alignment deviation metric and provided to the user of the ocular aberration measurement system as a display view with corresponding graphics, or used to allow the ocular aberration measurement system to ignore images from the examination image sequence or to abort and / or restart the examination. Thus, the embodiments provide substantially real-time monitoring feedback while providing more reliable and accurate aberration measurements than conventional systems, for example, by reducing the variability in clinical and intraoperative ocular aberration measurements due to eye movement during the examination image sequence.

[0031] In additional embodiments of this disclosure, the ocular aberration measurement system and method provide a platform and technique for accurately characterizing systemic aberrations and correcting wavefront sensor data that would otherwise be degraded by alignment biases associated with the patient's eye. For example, to account for potential errors caused by systemic aberrations or generated during eye movements, the ocular aberration measurement system described herein can employ one or more of two distinct calibration methods for accurate high-order aberration (HOA) analysis: a systemic characterization process and a complex analysis training process.

[0032] For the system characterization process, an eye aberration measurement system can 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 a substantially single type of variable aberration (e.g., defocus aberration) to an eye aberration measurement system with substantially zero alignment bias. The reference interferograms can be used to characterize and / or quantify any systematic aberrations of a particular eye aberration measurement system, which can be used to correct wavefront sensor data provided by the wavefront sensor of that particular eye aberration measurement system, for example, by removing systematic aberrations prior to subsequent analysis.

[0033] For complex analysis training processes, an eye aberration measurement system can be used to capture a set of wavefront measurements generated using a model target configured to present different types and varying intensities of aberrations (e.g., Zernike expansion coefficients up to order 6) to an eye aberration measurement system with variable alignment biases in the aberration elements of the model target, such as rollover, squinting, torsion, and x, y, and z positions. The set of wavefront measurements can be used to train and / or improve a complex analysis engine performed by the eye aberration measurement system, and can be configured, for example, to generate substantially accurate estimates of alignment biases and / or corrected wavefront sensor data based on uncorrected wavefront sensor data, or a combination of uncorrected wavefront sensor data and eye-tracking data as described herein. Thus, embodiments provide more reliable and accurate aberration measurements than conventional systems, for example, by increasing the precision and accuracy of aberration measurements due to reduced errors caused by system aberrations and off-axis or skewed eye aberration measurements. Moreover, the 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 thus included in a particular inspection (e.g., present and / or detected in a given image of an inspected image sequence).

[0034] In other embodiments of this disclosure, the ocular aberration measurement system and method provide techniques for correcting wavefront sensor data that would otherwise be degraded by imaging artifacts associated with the patient's eye. For example, to account for potential errors caused by imaging of vitreous bubbles and / or other image artifacts during eye characterization, the ocular aberration measurement system described herein may decompose wavefront sensor data (e.g., a representation of an interferogram of the eye) into a form that includes a phase component (e.g., a spatial phase component), which can be weighted to reduce or eliminate erroneous portions of the wavefront sensor data.

[0035] In other embodiments of this disclosure, ocular aberration measurement systems and methods provide techniques for monitoring tear film hydration (e.g., dry eye) during aberration measurement examinations to help mitigate the adverse effects of dehydration (e.g., relatively low tear film hydration) on corresponding wavefront sensor data. For example, the ocular aberration measurement systems described herein can provide substantially real-time feedback to clinicians or surgeons to, for example, omit wavefront sensor data degraded by relatively poor tear film hydration, prompt patients to blink, prompt operators or surgeons to administer saline solution, and / or provide other dry eye relief techniques as described herein.

[0036] Figure 1 A block diagram of an eye aberration measurement system 100 according to an embodiment of this disclosure is shown. Figure 1 In the illustrated embodiment, the ocular aberration measurement system 100 can be implemented to provide substantially real-time (e.g., 30Hz update) monitoring of the optical target 102 (e.g., the patient's eye) while continuously compensating for common characterization errors, such as patient motion, system optical aberrations, thermal changes, vibrations, and / or other characterization errors that would otherwise degrade the ocular aberration measurements provided by the ocular aberration measurement system 100.

[0037] 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 the 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 the eye tracker 130, and an OCT beacon 123 for generating an OCT probe beam 124 to illuminate the optical target 102 for the OCT sensor 122. Beam splitters 112-116 provide the probe beam 111 to the optical target 102 and generate associated sensor beams 113, 115, 117 from the optical target 102 (e.g., probe beam 111, probe beam 124, and a portion of the light generated by the LED array 132 and reflected by the optical target 102). Each sensor element of beacon 110, OCT beacon 123, LED array 132, and eye aberration measurement system 100 can be controlled by controller 140 (e.g., via communication links 141-144), and controller 140 can also serve as an interface between the sensor elements of beacon 110, OCT beacon 123, LED array 132, and eye aberration measurement system 100 and other elements of eye aberration measurement system 100, including user interface 146, server 150, distributed server 154, and other modules 148 as shown (e.g., accessed via optional communication links 145, 149, and 155).

[0038] In typical operation, controller 140 initializes one or more of wavefront sensor 120, optional OCT sensor 122, and optional eye tracker 130; controls beacon 110, OCT beacon 123, and / or LED array 132 to illuminate optical target 102; and receives eye aberration measurement output data (e.g., wavefront sensor data, eye tracker data, OCT sensor data) from various sensor elements of eye aberration measurement system 100. As described herein, controller 140 may process the eye aberration measurement output data itself (e.g., to detect or correct alignment errors and / or extract aberration parameters from wavefront sensor data) or may provide the eye aberration measurement output data to server 150 and / or distributed server system 154 (e.g., via network 152) for processing. Controller 140 and / or server 150 may be configured to receive user input (e.g., to control the operation of the ocular aberration measurement system 100) and / or generate user feedback at user interface 146 to display to a user via a display of user interface 146, such as a display view of ocular aberration measurement output data and / or characteristics of the ocular aberration measurement output data 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 the operation and / or characterization of the ocular aberration measurement 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 aberration measurement system 100, detect alignment deviations associated with 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., updates at 30 Hz or higher) 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.

[0039] Beacon 110 can be implemented using a laser source (e.g., generating highly coherent light) and / or a superluminescent diode (e.g., a “SLD” generating relatively low coherent light), which can be controlled by controller 140 to generate a probe beam 111 primarily for wavefront sensor 120. OCT beacon 123 can be implemented using a laser source and / or a superluminescent diode (e.g., generating relatively low coherent light, which is particularly suitable for OCT sensor 122), which can be controlled by controller 140 to generate an OCT probe beam 124 primarily for OCT sensor 122. In various embodiments, the OCT beacon can be integrated with OCT sensor 122, as shown, can be integrated with, for example, beacon 110, and / or can be implemented as its own standalone beacon, similar to beacon 110 (e.g., arranged using a suitable beam splitter). LED array 132 can be implemented using a shaped or patterned LED array, which can be controlled by controller 140 to illuminate target 102 primarily for eye tracker 130. Beam splitters 112-116 can be implemented by any of a plurality of optical components (e.g., thin-film beam splitters, mirror surfaces) configured to aim the probe beam 111 and / or through the optical target 102 and to direct at least a portion of the probe beam 111 and / or the source beam generated by the optical target 102 (e.g., the reflected portion of light emitted from the probe beam 111 or 124 and / or the light emitted from the LED array 132) toward various sensor elements of the ocular aberration measurement system 100 to form sensor bundles 113-117 (e.g., sensor bundles). The optical target 102 can be, for example, a patient's eye, or can be implemented by a one-way (probe beam 111 off while the optical target 102 generates its own illumination) or two-way (e.g., normal operation with the probe beam 111 on) model target, for example, as described herein.

[0040] 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., the optical wavefront of sensor beam 117 generated by at least one reflection from optical target 102 via probe beam 111), and wavefront sensor 120 can be configured to provide associated wavefront sensor data. For example, wavefront sensor 120 can be implemented as a Shack-Hartmann wavefront sensor, a phase-shifting schlieren wavefront sensor, a wavefront curvature sensor, a pyramid wavefront sensor, a common-path interferometer, a polygonal shearing interferometer, a Ronchi tester, a shearing interferometer, and / or any one or combination of wavefront sensors that can be configured for ophthalmic use. Wavefront sensor data provided by wavefront sensor 120 can be represented in various formats, including Zernike coefficients, such as Fourier, cosine, or Hartley transforms, or Taylor polynomials in cylindrical or Cartesian coordinates, or as interferograms.

[0041] OCT sensor 122 can be implemented as any one or a combination of devices or device architectures configured to capture two-dimensional and three-dimensional images at micron and / or submicron resolution from an optically scattering medium (such as optical target 102) using relatively low coherence light and low coherence interferometry, and is configured to provide associated OCT sensor data. For example, OCT sensor 122 can be implemented as any one or a combination of OCT sensor architectures that can be configured for ophthalmic use. Eye tracker 130 can be implemented as any one or a combination of devices or device architectures configured to track the orientation and / or position and / or features of optical target 102 (e.g., retina, pupil, iris, cornea, lens), including conventional eye trackers or fundus cameras, and is configured to provide associated eye tracker data. In some embodiments, eye tracker 130 can be configured to capture images of one or more types of Purkinje reflections associated with target 102.

[0042] The controller 140 can be implemented as any suitable logic device (e.g., a processing device, microcontroller, processor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), memory storage device, memory reader, or other device or combination thereof) 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 aberration measurement system 100 and / or its components. Such software instructions can also implement methods for processing sensor signals, determining sensor information, providing user feedback (e.g., via user interface 146), querying device operating parameters, selecting device operating parameters, or performing any of the various operations described herein (e.g., operations performed by the logic devices of the various devices of the aberration measurement system 100).

[0043] Furthermore, a machine-readable medium may be provided for storing non-transitory instructions to be loaded into and executed 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 connection to means of the eye aberration measurement system 100. For example, the controller 140 may be adapted to store, for example, sensor signals, sensor information, complex analysis parameters, calibration parameters, calibration point sets, training data, reference data, and / or other operating parameters over time, and to provide such stored data to other elements of the eye aberration measurement system 100. In some embodiments, the controller 140 may be integrated with a user interface 146.

[0044] User interface 146 may be implemented as one or more of a display, touchscreen, keyboard, mouse, joystick, knob, virtual reality headset, and / or any other device capable of accepting user input and / or providing feedback to the user. In various embodiments, user interface 146 may be adapted to provide user input to other devices of the eye aberration measurement system 100 (such as controller 140). User interface 146 may also be implemented with one or more logic devices adapted to execute instructions, such as software instructions, to implement any of the various processes and / or methods described herein. For example, user interface 146 may be adapted to establish communication links, 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.

[0045] In some embodiments, the user interface 146 may be adapted to accept user input, for example, to establish a communication link (e.g., to server 150 and / or distributed server system 154), select specific parameters for the operation of the aberration measurement system 100, select a method for processing sensor data, adjust the position and / or orientation of the articulated model target, and / or otherwise facilitate the operation of the aberration measurement system 100 and devices within the aberration measurement system 100. Once the user interface 146 has accepted 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 of graphs or maps including such data and / or parameters. In some embodiments, the user interface 146 may be adapted to accept user input to, for example, modify control loop or process parameters of controller 140, or control loop or process parameters of any other element of the aberration measurement system 100.

[0046] Other modules 148 may include any one or a combination of sensors and / or devices configured to facilitate operation of the aberration measurement system 100. For example, 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 aberration measurement system 100; a humidity sensor configured to measure ambient humidity around the aberration measurement system 100; a vibration sensor configured to measure vibration amplitude and / or presence associated with operation of the aberration measurement system 100; a patient sensor configured to measure the posture, motion, or other characterization of the patient supplying the optical target 102; and / or other sensors capable of providing sensor data that facilitates operation of the aberration measurement system 100 and / or corrects for common systematic errors typical in the operation of the aberration measurement system 100. In additional embodiments, other modules 148 may include an additional illumination 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 reflections associated with the target 102.

[0047] For example, server 150 may be implemented similarly to controller 140 and may include various elements of a personal computer or server computer for storing, processing, and / or otherwise manipulating relatively large datasets associated with one or more patients, such as relatively large training datasets as described herein, to train neural networks or implement other types of machine learning. For example, distributed server system 154 may be implemented as a distributed combination of multiple embodiments of controller 140 and / or server 150 and may include networking and storage devices, as well as the ability to facilitate the storage, processing, and / or other manipulation of relatively large datasets (including relatively large training datasets as described herein) to train neural networks or implement other types of machine learning in a distributed manner. Network 152 may be implemented as one or more of wired and / or wireless networks, local area networks, wide area networks, the Internet, cellular networks, and / or according to other network protocols and / or topologies.

[0048] Figures 2A to 2B The illustration shows a block diagram of aberration measurement characterization targets 202A-B for an eye aberration measurement system 100 according to an embodiment of this disclosure. Figure 2A In the illustrated embodiment, the aberration measurement characterization target 202A can be implemented as a one-way model target configured to characterize optical aberrations associated with the eye aberration measurement system 100. For example... 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 via a lens system 262 and is oriented 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 may be configured to generate a diverging spherical wavefront with controllable convergence power, a converging spherical wavefront with controllable convergence power, or a plane wave with zero power. The lens system 262 is coupled to a linear motion actuator 264 via a mounting base 265, which allows the lens system 262 to move along its optical axis to change the defocus aberration of the single-pass model target 202A to generate multiple reference interferograms and corresponding wavefront sensor data (e.g., provided by wavefront sensor 120). For example, such reference interferograms and / or corresponding wavefront sensor data may be aggregated and stored as... Figure 3 The aberration meter model 360 is used to correct system aberrations associated with the eye aberration measurement system 100 as described herein.

[0049] In some embodiments, the lens system 262 may be implemented as a trackable lens from the National Institute of Standards and Technology (NIST), and the linear motion actuator 264 may be implemented as a relatively high-precision actuator stage configured to position the lens system 262 at a set of locations spaced apart from the source 260 to generate a source beam 211 with known and predefined defocus powers (e.g., defocus aberrations), such as -12 to 6 diopters or 0 to ±5 diopters, for example, with a step size of 5.0D or at a higher resolution step size, based on the range of defocus aberrations typically experienced by a patient as monitored by the eye aberration measurement system 100. The resulting aberrometer model 360 can be used to compensate for various systemic aberrations, including those attributable to shot noise, thermal variations, and vibrations.

[0050] like Figure 2B As shown, the aberration measurement characterization target / two-way model target 202B includes an interchangeable eye aberration model 270, which is releasably 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 change the position and / or orientation of the interchangeable eye aberration model 270 to generate multiple selected (e.g., known) alignment biases (e.g., relative to the optical axis of the eye aberration measurement system 100) and corresponding multiple sets of wavefront sensor data. For example, such alignment biases and corresponding wavefront sensor data can 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.

[0051] In some embodiments, the interchangeable eye aberration model 270 can form one element of a set of interchangeable eye aberration models, each model forming a precise amount of predefined eye aberration (e.g., represented by precise and predefined Zernike coefficient amplitudes, such as the amplitude of a Zernike expansion to the 6th order expressed in micrometers). In one embodiment, such an interchangeable eye aberration model can be cut using transparent poly(methyl methacrylate) (PMMA) on a contact lens lathe. More generally, such an interchangeable eye aberration model can be measured by a third-party profilometer with traceability to NIST for ground truth comparison with measurements performed by the eye aberration measurement system 100. In various embodiments, the 6DOF motion actuator 272 can be configured to provide micrometer-resolution positioning of the interchangeable eye aberration model 270 along the x, y, and z axes, and microradian orientation of the interchangeable eye aberration model 270 about the θy (rolling), θx (oblique), and θz (torsion) directions, as shown in representative coordinate systems 280A-B.

[0052] In general operation, each interchangeable ocular aberration model 270 in this group (e.g., a group of 10 or more interchangeable models) can be sequentially mounted to a 6DOF motion actuator 272, and the controller 140 can be configured to control the 6DOF motion actuator 272 to position or orient the interchangeable ocular aberration model 270 at a set of relative positions and / or orientations (e.g., relative to the optical axis of the ocular aberration measurement system 100) within a range of alignment deviations typically experienced by the patient as monitored by the ocular aberration measurement system 100. In one embodiment, the alignment deviation group may include approximately 40,000 different alignment deviations. In various embodiments, combining the alignment deviation group with a corresponding set of wavefront sensor data (e.g., provided by wavefront sensor 120) can form a supervised data set (e.g., eye model 370), which 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 may also be measured by the eye tracker 130. More generally, the combination of characterizations performed by aberration measurement characterizing targets 202A-B can be used to compensate for or correct both systematic aberrations and errors in the wavefront sensor data caused by misalignment of optical target 102.

[0053] Figure 3 A block diagram of an eye aberration measurement system 300 according to an embodiment of this disclosure is shown. Figure 3 In the illustrated embodiment, the eye aberration measurement system 300 can be configured to generate a complex analysis engine 350 and / or a compact analysis engine 340 using characterization data generated by the eye aberration measurement system 100 through aberration measurement characterization of targets 202A-B. These engines can be used during operation of the eye aberration measurement system 100 to provide substantially real-time monitoring and user feedback (e.g., updates at 30 Hz or higher) of optical aberration measurements of optical targets 102, while continuously compensating for common characterization errors as described herein.

[0054] like Figure 3As shown, the eye aberration measurement system 300 is similar to the eye aberration measurement system 100, but has additional details regarding the various data structures and executable program instructions used in the operation of the eye aberration measurement system 100 or 300. For example, the controller 140 is shown to be implemented with a compact analysis engine 340, and the server 150 and the distributed server system 154 are each shown to be implemented with or storing one or more of the following: an aberration meter model 360, an eye model 370, training data 392, a supervised learning engine 390, a complex analysis / neural network engine 350, and a compact analysis engine 340. Dashed lines generally indicate optional storage and / or implementation of particular elements, but in various embodiments, each of the controller 140, server 150, and distributed server system 154 may implement or store any identified elements and / or additional elements as described herein.

[0055] Generally, the aberration meter model 360 can be generated by aggregating sensor data associated with the use of the single-pass model target 202A to characterize the eye aberration measurement system 100, and 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. Training data 392 can be generated by combining the aberration meter model 360 and / or the eye model 370 and / or by generating and aggregating simulated training data sets, as described in this paper. Figure 6 The elements described herein. Supervised learning engine 350 can be implemented as a static learning engine and / or a learning engine generated according to a program configured to generate complex analysis engine 350 using training data 392 (e.g., a genetic algorithm that can update the learning engine). For example, 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. Compact analysis engine 340 can be implemented as a compact form of complex analysis engine 350, such as a relatively low-resource but high-performance form more suitable for execution by controller 140, and thus can be implemented as a deep neural network and / or other complex analysis methodologies. In a particular embodiment, compact analysis engine 340 can be implemented as a neural network with fewer hidden layers and / or neurons / layers than complex analysis engine 350.

[0056] Figure 4 The illustration shows a flowchart of a process 400 characterizing an eye aberration measurement system 100 and / or 300 according to an embodiment of this disclosure. It should be understood that any step, substep, subprocess, or block of process 400 may differ from... Figure 4The illustrated embodiments are performed in the order or arrangement shown. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, 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 a subsequent part of the corresponding process. While process 400 is a reference, Figures 1 to 3 The system, process, control loop, and image described herein may be used to describe a process 400, but process 400 may be performed by other systems that are different from those systems, processes, control loops, and images and include different selections of, for example, electronic devices, sensors, components, moving structures, and / or moving structure attributes.

[0057] In block 402, an aberration meter model associated with the eye aberration measurement system is generated. For example, controller 140, server 150, and / or distributed server system 154 may be configured to control source 260 of a single-pass model target 202A (arranged as an optical target 102 monitored by the eye aberration measurement system 100) to generate source beam 211 via lens system 262 to illuminate wavefront sensor 120 and / or other elements of the eye aberration measurement system 100. For example, controller 140 may be configured to vary the defocus aberration of single-pass model target 202A according to a plurality of selected defocus powers to generate a plurality of wavefront sensor data sets provided by wavefront sensor 120. Controller 140, server 150, and / or distributed server system 154 may be configured to determine system aberrations associated with the eye aberration measurement system 100 based at least in part on the plurality of wavefront sensor data sets provided by wavefront sensor 120. System aberrations and / or associated wavefront sensor data sets can be stored (e.g., on server 150 and / or distributed server system 154) as aberration meter model 360.

[0058] In block 404, an eye model associated with the aberration measurement system is generated. For example, controller 140, server 150, and / or distributed server system 154 may be configured to control beacon 110 of aberration measurement system 100 to generate probe beam 111 to illuminate two-way model target 202B (an optical target 102 arranged to be monitored by aberration measurement system 100), thereby sequentially illuminating (e.g., via reflection from probe beam 111) one or more of the wavefront sensor 120, eye tracker 130, OCT sensor 122, and / or other elements of aberration measurement system 100. For example, controller 140 may be configured to change the position and / or orientation of interchangeable aberration model 270 of two-way model target 202B relative to the optical axis 111 of aberration measurement system 100 based on a plurality of selected alignment deviations to generate a plurality of corresponding wavefront sensor data sets provided by wavefront sensor 120. Multiple selected alignment biases and / or corresponding sets of multiple wavefront sensor data can be stored (e.g., on server 150 and / or distributed server system 154) as eye model 370. Similar techniques can be used to combine eye-tracking data from eye tracker 130 and OCT sensor data from OCT sensor 122 into eye model 370.

[0059] In block 406, a complex analytics engine is determined. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine complex analytics engine 350 based at least in part on aberration model 360 generated in block 402 and / or eye model 370 generated in block 404. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to form a deep neural network 600, which includes an input layer 620, an output layer 640, and at least one hidden layer 630-639 connecting the input layer 620 and the output layer 640, each layer including multiple neurons. Controller 140, server 150, and / or distributed server system 154 may be configured to train at least one trainable weighting matrix W associated with each neuron of the input, output, and hidden layers of neural network 600 via supervised learning engine 390, using alignment biases of eye model 370 as ground truth output data and corresponding wavefront sensor data sets of eye model 370 as training input data as described herein. The resulting deep neural network may be stored and used as complex analysis engine 350. In other embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to generate, at least in part, multiple corrected wavefront sensor data sets corresponding to multiple selected alignment biases of eye model 370 based on system aberrations associated with the eye aberration measurement system in aberrometer model 360, then form neural network 600 and train one or more complex analysis parameters of neural network 600 using supervised learning engine 390 to determine complex analysis engine 350 as described herein.

[0060] In box 408, a compact analysis engine is generated. For example, controller 140, server 150, and / or 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 connecting the input layer 620 and the output layer 640), and to generate a weighted 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. During generation, the compact analysis engine 340 may be stored or otherwise integrated with or implemented by controller 140, which may use the compact analysis engine 340 to generate substantially real-time (e.g., 30 frames / second) user feedback (e.g., display views including various graphics) and reliable and accurate monitoring of eye alignment deviations, eye aberrations, and / or other characteristics of the optical target 102 as described herein.

[0061] Figure 5The illustration shows a flowchart of process 500 for operating the eye aberration measurement system 100 and / or 300 according to an embodiment of this disclosure. It should be understood that any step, substep, subprocess, or block of process 500 may differ from... Figure 5 The illustrated embodiments are performed in the order or arrangement shown. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, 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 a subsequent part of the corresponding process. While process 500 is for reference only... Figures 1 to 3 The system, process, control loop, and image described herein may be used to describe a process 500, but process 500 may be performed by other systems that are different from those systems, processes, control loops, and images and include, for example, different choices of electronic devices, sensors, components, moving structures, and / or moving structure attributes.

[0062] In block 502, eye aberration output is received. For example, controller 140, server 150, and / or distributed server system 154 may be configured to receive eye aberration measurement output data that includes at least wavefront sensor data provided by wavefront sensor 120. More generally, the eye aberration measurement output data may include any one or more of the wavefront sensor data provided by wavefront sensor 120 as described herein, OCT sensor data provided by OCT sensor 122, eye tracker data provided by eye tracker 130, and / or other output data provided by eye aberration measurement system 100.

[0063] In block 504, an estimated eye alignment deviation is determined. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine, at least in part, the estimated eye alignment deviation corresponding to the relative position and / or orientation of an optical target 102 (e.g., a patient's eye) monitored by eye aberration measurement system 100, based at least in part on eye aberration measurement output data received in block 502. In some embodiments, 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 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 controller 140, server 150, and / or distributed server system 154 may be configured to determine the estimated eye alignment deviation by determining a wavefront-estimated eye alignment deviation corresponding to each wavefront sensor measurement.

[0064] For example, in one embodiment, controller 140, server 150, and / or distributed server system 154 may be configured to determine the estimated eye alignment deviation by: determining the corresponding estimated relative position and / or orientation of optical target 102 for each wavefront sensor measurement (e.g., estimated eye alignment of optical target 102); identifying one or more clusters of estimated relative position and / or orientation of optical target at least in part based on one or more preset or adaptive cluster thresholds; determining gaze alignment at least in part based on the centroid of the largest of one or more identified clusters; and determining the estimated eye alignment deviation for each wavefront sensor measurement by at least in part based on the difference between gaze alignment and estimated relative position and / or orientation of optical target corresponding to wavefront sensor measurements (e.g., the difference between gaze alignment and estimated eye alignment of optical target 102 in a time series corresponding to wavefront sensor measurements). In a related embodiment, 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 the estimated eye alignment deviation by: determining the 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 wavefront sensor measurements, omitting subgroups of wavefront sensor measurements; and then determining the corresponding estimated relative position and / or orientation of the optical target 102 based at least in part on the determined corresponding gaze state. For example, this technique can eliminate wavefront sensor measurements acquired when the optical target 102 is detected as (e.g., by the eye tracker 130) not fixed.

[0065] In block 506, corrected eye aberration measurement output data is determined. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine the corrected eye aberration measurement output data based at least in part on the estimated eye alignment deviation determined in block 504 and / or wavefront sensor data received in block 502. In some embodiments, where the wavefront sensor data comprises a time series of wavefront sensor measurements, controller 140, server 150, and / or distributed server system 154 may be configured to determine the corrected eye aberration measurement output data by determining an estimated eye alignment deviation associated with each 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 having an associated estimated eye alignment deviation equal to or less than a preset maximum permissible deviation. This preset maximum permissible deviation may be selected or set by the manufacturer and / or user of the eye aberration measurement system 100.

[0066] In other embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to determine corrected eye aberration measurement output data by: determining an estimated eye alignment deviation associated with each wavefront sensor measurement for each wavefront sensor measurement; determining a corrected wavefront sensor measurement for each wavefront sensor measurement having an associated estimated eye alignment deviation equal to or less than a preset maximum permissible deviation based at least in part on the wavefront sensor measurement and / or the associated estimated eye alignment deviation; and generating an average wavefront sensor measurement based at least in part on the corrected wavefront sensor measurement. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine corrected wavefront sensor measurements by: applying the complex analysis engine 350 or compact analysis engine 340 of the eye aberration measurement system 100 to each wavefront sensor measurement to generate a corresponding wavefront estimated eye alignment deviation and / or corrected wavefront sensor measurement as described herein.

[0067] In block 508, user feedback is generated. For example, controller 140, server 150, and / or distributed server system 154 may be configured to generate user feedback corresponding to the eye aberration measurement output data received in block 502, based at least in part on the estimated eye alignment deviation determined in block 504. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to generate user feedback by determining an eye alignment deviation metric based at least in part on the estimated eye alignment deviation and reporting the eye alignment deviation metric via user interface 146 of eye aberration measurement system 100. In other embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to generate user feedback based at least in part on the estimated eye alignment deviation determined in block 504 and / or the corrected eye 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 an estimated eye alignment deviation determined in block 504, a substantially real-time display view of an eye aberration map based at least in part on corrected eye aberration measurement output data determined in block 504, and / or an auditory and / or visual alarm indicating that at least one estimated eye alignment deviation is greater than a preset maximum permissible deviation as described herein.

[0068] Therefore, embodiments of this disclosure can provide substantially real-time (e.g., 30 frames per second) user feedback (e.g., display views including various graphics) and reliable and accurate monitoring of eye alignment deviation, eye 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 ophthalmological examinations and help provide improved surgical outcomes.

[0069] Figure 6 A diagram illustrating a multilayer or “deep” neural network (DNN) 600 according to an embodiment of this disclosure is shown. In some embodiments, the neural network 600 may represent a neural network for implementing each of one or more models and / or analysis engines described in conjunction with system 100 and / or 300. The neural network 600 uses an input layer 620 to process input data 610. In various embodiments, as described herein, the 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. In some embodiments, the input layer 620 may include a plurality of neurons or nodes for conditioning the input data 610 by scaling, biasing, filtering, range limiting, and / or otherwise conditioning it 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., where the input data 610 has been appropriately scaled, biased, filtered, range limited, and / or otherwise conditioning). Each neuron in input layer 620 generates an output that is provided to the neurons / nodes in hidden layer 630. Hidden layer 630 includes multiple neurons / nodes that process the output from input layer 620. In some embodiments, each neuron in hidden layer 630 generates an output that is then propagated through one or more additional hidden layers (ending in hidden layer 639). 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 hidden layer 639 is fed to output layer 640. In various embodiments, output layer 640 includes one or more neurons / nodes that can be used to modulate the output from hidden layer 639 by scaling, biasing, filtering, range limiting, and / or otherwise adjusting to form output data 650. In alternative embodiments, neural network 600 can 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 other various neural network architectures or complex analytical methodologies, including K-nearest neighbor (k-NN) databases for classification and / or regression.

[0070] In some embodiments, each of the input layer 620, hidden layers 630-639, and / or output layer 640 includes one or more neurons. In one embodiment, each of the input layer 620, hidden layers 630-639, and / or output layer 640 may include the same or different numbers of neurons. In a particular embodiment, the neural network 600 may include approximately six layers in total, each with up to 2,000-4,000 neurons. In various embodiments, each neuron in such constituent neurons may be configured to receive a combination of its input x (e.g., a weighted sum generated using a trainable weighted 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 modified linear unit activation function, or an activation function with an upper and / or lower bound, any or a combination of the log-sigmoid function, the 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 a particular embodiment, corresponding to a regression application, only the neurons of the output layer 640 can be configured to apply this linear activation function to generate their respective outputs.

[0071] In various embodiments, the neural network 600 may be trained using supervised learning (e.g., implemented as a supervised learning engine 390), for example by systematically providing the neural network 600 with selected sets of training data (e.g., training data 392), each set of training data comprising a set of input training data and a corresponding set of ground truth (e.g., expected) output data (e.g., a combination of aberration model 360 and eye model 370), and then determining and / or otherwise comparing the differences between the resulting output data 650 (e.g., 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 may 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 may be provided to the neural network 600 as feedback using one or more backpropagation techniques (including, for example, stochastic gradient descent and / or other backpropagation techniques). In one or more embodiments, a relatively large set of selected training data can be presented to the neural network 600 multiple times until the total loss function (e.g., mean squared error based on the difference between each set of training data) converges to or below a preset maximum allowable loss threshold.

[0072] In additional 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 techniques to aid in training complex analysis parameters of the neural network 600 as described herein (e.g., and generating a complex analysis engine 350). For example, the supervised learning engine 390 may 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 to perform supervised learning at least in part based on the simulated training data sets. Each of the simulated input training data and ground truth data sets may be generated by modifying the input training data (e.g., adjusting aberration parameters and / or alignment biases associated with the simulated two-way model objective) and interpolating non-simulated ground truth data to generate the corresponding simulated ground truth data. In various embodiments, the supervised learning engine 390 may 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 to train the neural network 600 based on one or millions of such simulated training data sets.

[0073] In various embodiments, the eye aberration measurement system 100 and / or 300 can be configured as a phase-based eye aberration measurement system. In such embodiments, one of the processing steps of this phase-based eye aberration measurement system generates a wrapped phase array as part of a representation of the wavefront sensor data provided by the wavefront sensor 120. Typically, for example, the wrapped phase array has real values ​​limited to the range of [-π to π] or [0 to 2π] as a natural result of calculating the phase from complex numbers using the arctangent 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 characteristics.

[0074] To generate an accurate final set of wavefront aberration values ​​(e.g., Zernike polynomial coefficients), the resulting expanded 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 expanded phase array can include various "singularities" or discontinuities and is not continuous, which leads to errors in the final calculated aberration fit. Typically, this aberration fit is a Zernike polynomial expansion, but other functional aberration features are also possible. Local singularity errors (e.g., points of discontinuity in the expanded phase) can cause errors in the overall final aberration characterization due to how the Zernike polynomial coefficients are fitted to the wavefront sensor data in the scaled, expanded phase array. Some type of repair can be applied to attempt to recover the true phase, but this repair process is 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 wavefront sensor data represented by an expanded phase array, since the Zernike polynomial coefficients fit the data in the least-squares error sense. The weights of each row in the linear equation system are chosen to reduce or eliminate the effect of each singularity.

[0075] In one embodiment, the weights can be chosen as follows: Let d be the nearest distance from a point in the unfolded phase array to the identified singularity. The weight of this 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 Dl; and [(d-DO) / (D1-D0)]^p if the distance d is between DO and Dl. In other words, the weight of each point is: 0 for elements whose distance to any identified singularity is less than or equal to a threshold distance D0; 1 for elements whose distance to all identified singularities is greater than or equal to a threshold distance D1; and a function of the nearest distance, the threshold, and the scaling parameter p for all elements whose distance to the identified singularity is between the two threshold distances. Formulation:

[0076]

[0077] 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 P is the pupil diameter being characterized / reconstructed. In various embodiments, this weighted fitting reduces the variability of phase-based eye aberration measurements caused by singularities in wavefront sensor data without introducing new errors, as described herein. This, in turn, improves the precision and accuracy of the resulting eye aberration measurement output data (e.g., characterization of aberrations in the eye).

[0078] Figure 7A The illustration shows a flowchart of a process 700 for reducing errors caused by imaging artifacts in an eye aberration measurement system according to an embodiment of this disclosure. Figure 7A In the illustrated embodiment, the initial wavefront sensor data in the form of an interferogram 710 resembling a human eye (e.g., comprising multiple generally concentric stripes / patterns 712) can be decomposed (e.g., step 702) into a set of at least one wrapping phase component / array 724 (φ). i The packaged phase wavefront sensor data 720 may include additional components (e.g., amplitude / scaling array 722 (ρ)). i This allows the wrapped phase wavefront sensor data 720 to form a discrete representation of the interferogram 710, which can be analogous to the discrete-space Fourier transform of the interferogram 710. The wrapped phase wavefront sensor data 720 can be transformed (e.g., step 704) to include at least the unfolded phase array 734 (φ i The unfolded phase wavefront sensor data 730 is obtained by identifying waves with an amplitude of approximately 2π (or multiples thereof) and / or appearing in the wrapped phase array 724 (φ). i The wrapped phase array 724 (φ) near the artificial numerical boundaries (e.g., +-π or 0 and 2π) i The discontinuities in the array (e.g., between adjacent elements) will be addressed, and the phase array 724 (φ) will be wrapped. i Add 2π to or subtract 2π from the elements of ) to eliminate discontinuities and form an expanded phase array 734 (φ i ').

[0079] Once the unfolded phase wavefront sensor data 730 is determined, singularities or discontinuities attributable to image artifacts (e.g., related to the vitreous bubble 714 imaged as part of the interferogram 710) can be identified in the unfolded phase wavefront sensor data 730, for example by identifying unfolded phase array 734 (φ) with an amplitude greater than (e.g., provided by the user or manufacturer of the eye aberration measurement systems 100 and / or 300) a predetermined singularity threshold. i Any remaining discontinuities in the interferogram 710 and / or in the unfolded phase array 734 (φ) can be considered. This predetermined singularity threshold can be selected to take into account various types of image noise in the interferogram 710 and / or in the unfolded phase array 734 (φ) i The expected slope between adjacent elements in ').

[0080] Once this discontinuity is identified (e.g., identified as an unfolded phase array 734 (φ) iThe distance between any element of the expanded phase wavefront sensor data 730 and such discontinuity can be determined, and the fitting weight of each element of the expanded phase wavefront sensor data 730 can be determined, for example, by using a linear system of weighted normal equations of wi(1) as described herein. In some embodiments, such weights can be calculated only for the nearest singularity (e.g., as a value of d), or such weights can be calculated for each detected singularity, and the resulting weight sets are combined (e.g., by multiplication) to determine the fitting weight of that element of the expanded phase wavefront sensor data 730. The aberration characterization fit 740 (e.g., aberration measurement output data) can be based on a fitting function (e.g., a selected aberration characterization function, such as a Zernike polynomial expansion of a selected order), the determined weights w i The phase wavefront sensor data 730 is used to determine (step 706), and an eye aberration map 750 can be generated (step 708) to visualize the aberration characterization fitting 740 and identify the spatial regions 752-754 of the examined eye that deviate from the ideal eye.

[0081] In various embodiments, the interferogram 710 and / or the eye aberration map 750 may be formed as at least a portion of a display view (e.g., display view 718 and / or 758) presented on the display of the user interface 146 and shown to the user as user feedback regarding the operation of the eye aberration measurement system 100 and / or 300. In general operation, the diameter 716 of the interferogram 710 and the diameter 756 of the eye aberration map 750 may be configured to approximately represent or correspond to the pupil diameter of the eye being examined.

[0082] Figure 7B The illustration shows a flowchart of process 700B for reducing errors caused by imaging artifacts in an eye aberration measurement system according to an embodiment of this disclosure, similar to... Figure 7A The process is 700. In... Figure 7B In the illustrated embodiment, the initial wavefront sensor data in the form of an interferogram / fringe pattern 710B can be decomposed (e.g., steps 702B and 703B-1 and 703B-2) into a set of wrap-around phase wavefront sensor data 724B and 726B, which includes at least the wrap-around phase components / array (e.g., similar to...). Figure 7A The packaged phase components / array 724 (φ) iSpecifically, the interferogram 710B can be Fourier transformed (e.g., step 702B) to produce transformed wavefront sensor data 720B, which may include multiple spatial peaks having corresponding spatial structure neighborhoods 723B-1, 723B-2. In some embodiments, the spatial structure neighborhoods 723B-1, 723B-2 may 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), as shown, for example. In some embodiments, each spatial structure neighborhood 723B-1, 723B-2 may be processed individually to determine corresponding wrapping phase wavefront sensor data sets 724B and 726B, as shown. Figure 7B In the specific embodiment shown, the spatial structure neighborhood 723B-1 is processed into a first wrapping phase dW / dx array and the spatial structure neighborhood 723B-1 is processed into a second wrapping phase dW / dy array.

[0083] The unfolded phase wavefront sensor data 724B and 726B can be converted (e.g., steps 704B-1 and 704B-2) into unfolded phase wavefront sensor data 734B and 736B, which includes at least one unfolded phase array (e.g., similar to the wrapped phase component / array 734(φ) in FIG7). i Once the unfolded phase wavefront sensor data 734B and 736B are determined, singularities or discontinuities 764 (e.g., image artifacts attributed to interferogram 710B) can be identified in the unfolded phase wavefront sensor data 734B and 736B to generate processed unfolded phase wavefront sensor data 744B and 746B. Once such discontinuities 764 are identified (e.g., as elements of the corresponding unfolded phase array), the distance between any element of the unfolded phase wavefront sensor data 734B and 736B and such discontinuities can be determined, for example, by means of w as described herein. i Equation (1) determines the fitting weight for each element of the expanded phase wavefront sensor data 734B, 736B. In particular, for example, the processed expanded phase wavefront sensor data 744B, 746B includes a darker line 764 indicating singularities in the processed expanded phase wavefront sensor data 744B, 746B and a halo 766 about a discontinuity 764 indicating a local weight different from 1.

[0084] Aberration characterization fitting (e.g., aberration measurement output data) can be based on a fitting function (e.g., a selected aberration characterization function, such as a Zernike polynomial expansion of a selected order or order range) and determined weights w. iThe processed unfolded phase wavefront sensor data 744B, 746B (e.g., in the form of a processed first wrapped phase dW / dx array and a second wrapped phase dW / dy array) are used to determine the eye aberration map 750B, which 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 color map gradients according to a selected palette). Figure 7B An uncorrected eye aberration map 790B (e.g., showing significantly different spatial gradients and aberration distributions) is provided directly from the expanded phase wavefront sensor data 734B, 736B without weighting according to equation (2) for comparison.

[0085] In various embodiments, the interferogram 710B and / or the eye aberration map 750B may be formed as at least a portion of a display view (e.g., display views 718B and / or 758B) presented and displayed to the user on the display of the user interface 146, as user feedback regarding the operation of the eye aberration measurement system 100 and / or 300, either together with or separately from any of the intermediate display views, such as display views 728B, 729B, 738B, 739B, and / or 798B as shown. In general operation, the diameter 716B of the interferogram 710B and the diameter 756B of the eye aberration map 750B may be configured to approximately represent or correspond to the pupil diameter of the eye being examined.

[0086] Figure 7C The illustration shows a flowchart of a process 700C for reducing errors caused by imaging artifacts in an eye aberration measurement system according to an embodiment of this disclosure, including... Figure 7A Process 700 and / or Figure 7B The process 700B comprises several parts. In particular, process 700C illustrates a spatially enlarged version of the technique used in steps 705B-1 and 705B-2 to determine and apply weights to the unfolded phase wavefront sensor data 734B, 736B based on discontinuities 764. For example, the unfolded phase wavefront sensor data 734C is shown as including the discontinuity profile 764C of the unfolded phase wavefront sensor data 734C and the nearest discontinuity point 766C to elements / points 744-1, 744-2, 744-3 (e.g., points used to fit the Zernike polynomial expansion). Figure 7CThe diagram also illustrates circle 740C-0 (which includes / distinguishes all points whose distance from the nearest discontinuity 766C of the discontinuity profile 764C is less than or equal to a threshold distance / radius D0) and circle 740C-1 (which does not include / distinguish all points whose distance from the nearest discontinuity 766C of the discontinuity profile 764C is greater than or equal to a threshold distance D1). Line 746C may be perpendicular to the discontinuity profile 764C at the nearest discontinuity 766C and pass through elements / points 744-1, 744-2, 744-3 of the unfolded phase wavefront sensor data 734C. Therefore, according to equation (1) of this paper, the weight of element 744-1 will be 0 (e.g., corresponding to discontinuity 764), the weight of element 744-2 will be between 0 and 1 (e.g., corresponding to halo 766), and the weight of element 744-3 will be 1 (e.g., the remainder of the unfolded phase wavefront sensor data 736B).

[0087] In various embodiments, process 700C may form at least a portion of a display view (e.g., display view 738C) presented and shown to the user on the display of user interface 146 as user feedback regarding the operation of the eye aberration measurement system 100 and / or 300, either together with or separate from any of the other display views described herein. In general operation, diameter 716C may be configured to approximately represent or correspond to the pupil diameter of the eye being examined.

[0088] Figure 8 The illustration shows a flowchart of process 800 for operating an eye aberration measurement system 100 and / or 300 according to an embodiment of this disclosure, reducing errors caused by imaging artifacts. It should be understood that any step, substep, subprocess, or block of process 800 may differ from... Figure 8 The illustrated embodiments are performed in the order or arrangement shown. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, 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 a subsequent part of the corresponding process. While process 800 is a reference, Figure 1 The system, process, control loop, and image described to FIG7 may be used to describe the process 800, but the process 800 may be performed by other systems that are different from those systems, processes, control loops, and images and include different choices of, for example, electronic devices, sensors, components, moving structures, and / or moving structure attributes.

[0089] In block 802, eye aberration output is received. For example, controller 140, server 150, and / or distributed server system 154 may be configured to receive eye aberration measurement output data that includes at least wavefront sensor data provided by wavefront sensor 120. More generally, eye aberration measurement output data may include any one or more of the wavefront sensor data provided by wavefront sensor 120 as described herein, OCT sensor data provided by OCT sensor 122, eye tracker data provided by eye tracker 130, and / or other output data provided by eye aberration measurement system 100. For example, wavefront sensor data provided by wavefront sensor 120 may include an interferogram 710 of the eye being examined, and / or may include wrapped phase wavefront sensor data 720 or unfolded phase wavefront sensor data 730, depending, for example, on the capabilities and / or configuration of wavefront sensor 120.

[0090] In block 804, the eye aberration measurement output data is decomposed. For example, controller 140, server 150, and / or distributed server system 154 can be configured to decompose the wavefront sensor data received in block 802 into at least wrapper phase components / array 724, for example, by generating a discrete Fourier transform of the interferogram 710 as described herein. For example, in embodiments where wavefront sensor 120 directly provides wrapper phase wavefront sensor data 720, block 804 can be omitted or incorporated into block 802.

[0091] In block 806, singularities in the eye aberration measurement output data are identified. For example, controller 140, server 150, and / or distributed server system 154 may be configured to identify singularities caused by imaging artifacts in the unfolded phase wavefront sensor data corresponding to the eye aberration measurement output data received in block 802. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to determine the unfolded phase array 734 and / or the unfolded 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 to identify discontinuities caused by imaging artifacts in the unfolded phase array 734 as described herein. In various embodiments, the controller 140, server 150, and / or distributed server system 154 may be configured to identify singularities by, for example, by identifying discontinuities in the unfolded phase component 734 of the wrapped phase wavefront sensor data 730 having an amplitude greater than a predetermined singularity threshold, or by using other discontinuity detection techniques on discrete data, including, for example, spline fitting and element offset techniques and / or numerical derivative and thresholding techniques.

[0092] In block 808, corrected ocular aberration measurement output data is determined. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine the corrected ocular aberration measurement 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 the singularities in the received ocular aberration measurement output data identified in block 806. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to determine a fitting weight for each element of the unfolded phase wavefront sensor data 730 and determine the corrected ocular aberration measurement output data by at least in part based on the fitting weights as described herein and the aberration measurement fit determined by the unfolded phase wavefront sensor data 730.

[0093] In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to be based on w as described herein. i Equation (1) determines the fitting weight for each element of the expanded phase wavefront sensor data 730, wherein the values ​​of the parameters used in Equation (1) are at least partially based on the pupil diameter of the eye being examined, such that the fitting weights themselves are at least partially based on the pupil diameter. In related embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to additionally employ any of the methods described herein to determine corrected ocular aberration measurement output data to compensate for, as described herein with respect to processes 400 and 500 and Figures 4 to 6 The aforementioned system aberrations and / or eye alignment deviations.

[0094] In block 810, user feedback is generated. For example, controller 140, server 150, and / or distributed server system 154 can be configured to generate user feedback corresponding to the ocular aberration measurement output data received in block 802, such as a display view of interferogram 710, and / or generate user feedback corresponding to the corrected ocular aberration measurement output data determined in block 808, such as a display view of ocular aberration map 750. In various embodiments, controller 140, server 150, and / or distributed server system 154 can be configured to use the methods described herein. Figures 4 to 6 Any of the aforementioned processes are used to generate user feedback. In various embodiments, such user feedback may include a substantially real-time display view of wavefront sensor data received in block 802, decomposed eye aberration measurement output data determined in block 804, singularities identified in block 806, corrected eye aberration measurement output data determined in block 808, and / or a corresponding eye aberration map 750.

[0095] Therefore, embodiments of this disclosure can provide substantially real-time (e.g., 30 frames per second) user feedback (e.g., display views 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 ophthalmological examinations and help provide improved surgical outcomes.

[0096] As tear film hydration deteriorates during examination / surgery, aberration measurements can be negatively impacted in terms of accuracy and repeatability. This may be due to the lack of a basic saline solution for the surgeon during intraoperative measurements or the lack of regular blinking by the patient during clinical examination. In various embodiments, ocular aberration measurement systems 100 and / or 300 can be configured to monitor at least one, typically two, available imaging resources during the preview and acquisition phases of the measurement to quantify the decline in hydration, and to provide, for example, various related mitigation measures as described herein.

[0097] The first imaging resource in this imaging resource includes any imaging system of an ocular aberration measurement system that can be used to generate Purkinje images of reflections from the surface of the eye being examined by known and repeatable structures. For example, the eye tracker 130 of ocular aberration measurement systems 100 and / or 300, a dedicated Purkinje imaging system (e.g., other module 148), and / or other imaging systems can be configured to generate and / or capture first Purkinje reflection (P1) images of one or more LED reflections generated by reflections from the outer surface of target 102 (e.g., the outer surface of the cornea of ​​a human eye) by LED array 132. In some embodiments, such first Purkinje reflection images of LED reflections can be processed by thresholding into a binary morphological representation that provides analysis for, for example, pixel connectivity and / or equivalent diameter and / or area of ​​each reflecting LED. These reflection characterization parameters can be quantified and monitored over a series of image or video frames to determine the tear film hydration state of the imaging approximately in real time.

[0098] The second imaging resource in this imaging resource includes a wavefront sensor 120, which can be configured to generate wavefront sensor data associated with the eye being examined. As described herein, this wavefront sensor data can include interferograms of fringe lines / patterns, and the Talbot Moiré fringe contrast associated with those interferograms typically decreases with loss of hydration. This tear film hydration degradation can be quantified within the Fourier transform domain of the interferogram. For example, the amplitude and shape of the neighborhood near the dominant frequency peak in the Fourier domain (e.g., Figure 7BThe spatial structural neighborhoods (723B-1, 723B-2) can represent the derivative of the wavefront. These neighborhoods can be directly affected when the tear film is dehydrated, and the associated tear film hydration state can be quantified and monitored using various amplitude and size image analysis methods as described in this paper.

[0099] In various embodiments, the focus and the Talbot Moiré camera (e.g., eye tracker 130 and wavefront sensor 120) can be synchronized; thus, image analysis of separate image / video streams can overlap. For example, the amplitude of the Fourier peak and the quality of LED reflections (e.g., reflection characterization parameters) can be monitored to provide physicians / technicians with substantially real-time feedback on tear film hydration status. For example, during surgery or clinical examination, the immediate tear film hydration status can be communicated to the clinician (e.g., via one or more display views). At the end of the examination, a more comprehensive tear film hydration map and / or graph can be generated, showing the evolution of relatively dry areas as a function of time.

[0100] Figure 9 The illustration shows a flowchart of a tear film hydration process 900 for a system for monitoring ocular aberration measurement according to an embodiment of this disclosure. Figure 9 In the illustrated embodiment, wavefront sensor data in the form of a first interferogram 910 in the human eye (e.g., comprising a plurality of generally concentric stripes / patterns 912) can be acquired / captured at a first time T1, including wavefront sensor data associated with one or more monitoring regions 914, including a specific monitoring region 915. In some embodiments, the shape, pattern, and / or distribution of the monitoring regions 914 may substantially correspond to the reflections from the target 102 by the individual LEDs in the LED array 132 as described herein. In other embodiments, the monitoring regions 914 may be selected to cover at least half, three-quarters, or all of the surface of the target 102, and the monitoring regions may or may not overlap each other. After a predetermined time period, wavefront sensor data in the form of a second interferogram 920 in the same eye can be acquired / captured at a second time T2, wherein the stripes / patterns 922 of the interferogram 920 have relatively reduced contrast compared to the stripes / patterns 912 of the interferogram 910. The Fourier transform of one or more monitoring regions 914 can be determined (step 904) and plotted as a graph 935, for example, to show the absolute and / or relative tear film hydration status of target 102 based on wavefront sensor data.

[0101] For example, in one embodiment, graph 935 illustrates the Fourier transform of wavefront sensor data in image data form corresponding to monitoring region 915, where transform profile 932 corresponds to the Fourier transform of image data captured at time T1, and transform profile 933 corresponds to the Fourier transform of image data captured at time T2. Various transform features of the transform profiles can indicate the absolute or relative tear film hydration state of target 102, such as the peak amplitude, full width at half maximum (FWHM), area, and / or other transform features of transform profiles 932 and 933, each of which can be monitored and / or stored. In an alternative embodiment, graph 935 can be a two-dimensional spatial graph, such as similar to... Figure 7B The two-dimensional spatial diagram shown in the display view 728B, and the transformation characteristics of the spatial peak and the corresponding spatial structure neighborhoods 723B-1 and 723B-2 can be monitored and stored.

[0102] Within approximately the same time period, Purkinje image data in the form of a first Purkinje reflection (P1) image 950 (e.g., including one or more reflections of a known structure) can be acquired / captured at approximately a first time T1, including Purkinje image data associated with one or more reflections 952 (e.g., including a specific reflection 955) of the LEDs of the LED array 132. In some embodiments, the shape, pattern, and / or distribution of the individual LEDs in the LED array 132 can be selected to cover at least half, three-quarters, or all of the surface of the target 102, and / or match the monitoring area 914, and such monitored structural reflections may generally not overlap with each other. After a predetermined time period, Purkinje image data in the form of a second Purkinje (P1) image 960 in the form of a human eye image of the same eye can be acquired / captured at a second time T2, wherein the reflection 962 of the Purkinje (P1) image 960 has relatively poor pixel connectivity and / or is relatively small or indistinguishable from the background compared to the reflection 952 of the Purkinje (P1) image 950. Such reflection characterization parameters corresponding to each of reflections 952 and 962 (and / or the mean, median and / or other statistical combinations and / or measurements of the reflection characterization parameters) can be determined and plotted as a graph 975, for example, to show the absolute and / or relative tear film hydration status of target 102 based on Purkinje image data.

[0103] For example, in one embodiment, Figure 975 illustrates reflection characterization parameters of Purkinje image data corresponding to reflection / monitoring region 955, wherein reflection characterization parameter value 972 corresponds to the estimated diameter of reflection 955 as captured at time T1, and wherein reflection characterization parameter value 973 corresponds to the estimated diameter of reflection 955 as captured at time T2. Various reflection characterization parameters can indicate the absolute or relative tear film hydration state of target 102, such as pixel connectivity, estimated diameter, estimated area, and / or other reflection characterization parameters of reflections 952, 962, each of which can be monitored and / or stored.

[0104] In various embodiments, interferograms 910, 920, Purkinje (P1) images 950, 960, and / or charts 935, 975 may be formed as at least a portion of a display view (e.g., display views 918, 928, 938, 958, 968, and / or 778) presented on the display of user interface 146 and shown to the user as user feedback regarding the operation of eye aberration measurement systems 100 and / or 300, as illustrated. Furthermore, Figure 9 The process 900 can be used to generate time series of images, charts, tear film hydration states, and / or associated display views, having more than two component images and / or states in the time series, including a stream of such images and / or states. In general operation, the diameter 916 of the interferogram 910 and the diameter 956 of the Purkinje (P1) image 950 can be configured to approximately represent or correspond to the pupil diameter of the eye being examined.

[0105] Figure 10 The illustration shows a display view 100 (e.g., including one or more display views 1018 and / or 1028) for monitoring tear film hydration in an ocular aberration measurement system according to an embodiment of this disclosure. Figure 10In the illustrated embodiment, the display view 1018 includes a tear film hydration map 1010 with one or more dehydration portion indicators 1012. In some embodiments, the tear film hydration map 1010 can be presented according to a palette and / or grayscale gradient configured to indicate the absolute and / or relative tear film hydration of the eye being examined. For example, an absolute measurement could be based on ideal eye tear film hydration, such as a known tear film thickness typically present after a series of typical blinks. A relative measurement could be based on an initial tear film hydration measured at the start of the examination (e.g., T1) or when the patient indicates that their eyes feel adequately hydrated, and all subsequent tear film hydration measurements can be determined relative to that initial measurement. In various embodiments, the dehydration portion indicator 1012 may be a graphic overlay configured to identify portions of the target 102 and / or the tear film hydration map 1010, such as portions where tear film hydration is below absolute measure or below expected after a specific time (e.g., T2-T1), for example, based on an ideal eye model with typical dehydration rates.

[0106] A selection box 1014 is also illustrated in display view 1018. In some embodiments, for example, user interface 146 may be configured to accept user input that identifies the boundaries of selection box 1014, and ocular aberration system 100 may be configured to generate display view 1028, which includes a hydration time evolution chart 1024 configured to illustrate the time evolution of tear film hydration associated with the region of tear film hydration map 1010 defined by selection box 1014. For example, in Figure 10 In the illustrated embodiment, the hydration time evolution chart 1024 includes a hydration evolution profile 1022 that indicates the absolute or relative hydration of a portion of the tear film hydration map 1010 defined by selection box 1014, where point 1026 indicates the end of the current time or a particular examination sequence. In embodiments without selection box 1014, the hydration time evolution chart 1024 may correspond to the average, mean, or other statistical measure of the entire tear film hydration map 1010. In various embodiments, selection box 1014 may have different shapes, for example, or may correspond to a selection of one or more of the dehydration portion indicators 1012 as described herein.

[0107] Figure 11 The illustration shows a flowchart of process 1100 for operating an ocular aberration measurement system 100 and / or 300 with tear film hydration monitoring according to an embodiment of this disclosure. It should be understood that any step, sub-step, sub-process, or block of process 1100 may be different from... Figure 11The illustrated embodiments are performed in the order or arrangement shown. For example, in other embodiments, one or more blocks may be omitted from or added to the process. Furthermore, 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 a subsequent part of the corresponding process. While process 1100 is for reference only... Figure 1-10 The system, process, control loop, and image described herein may be used to describe the process 1100, but the process 1100 may be performed by other systems that are different from those systems, processes, control loops, and images and include, for example, different choices of electronic devices, sensors, components, moving structures, and / or moving structure attributes.

[0108] In block 1102, ocular aberration output is received. For example, controller 140, server 150, and / or distributed server system 154 may be configured to receive ocular aberration measurement output data, which includes at least wavefront sensor data provided by wavefront sensor 120 and / or Purkinje image data provided by the imaging system of ocular aberration measurement system 100 (e.g., eye tracker 130, other modules 148). More generally, the ocular aberration measurement output data may include any one or more of the 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 measurement system 100 as described herein. The wavefront sensor data provided by wavefront sensor 120 may include, for example, an interferogram 910 of the eye being examined, and the Purkinje image data provided by the imaging system of ocular aberration measurement system 100 may include, for example, a first Purkinje reflection (P1) image 950 of the eye being examined.

[0109] In block 1104, the tear film hydration state is determined based on Purkinje image data. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine the tear film hydration state corresponding to target 102 based at least in part on the Purkinje image data received in block 1102. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to determine the tear film hydration state by: determining a threshold-based binary first Purkinje reflection (P1) image (e.g., a binary morphological representation of the P1 image where reflective pixels = 1 and non-reflective pixels = 0) based at least in part on the captured P1 image 950, and then determining one or more reflection characterization parameters (e.g., reflective pixel connectivity, estimated diameter, estimated area) corresponding to the tear film hydration state of target 102 based at least in part on the threshold-based binary P1 image. Determining such a threshold-based binary P1 image may include determining the pixel intensity or luminance of each pixel in the P1 image 950, assigning a pixel value of 1 to pixels with intensity or luminance equal to or higher than the threshold intensity or luminance, and assigning a pixel value of 0 to pixels with intensity or luminance lower than the threshold intensity or luminance. In some embodiments, the threshold may be predetermined (e.g., by user selection) or may be adaptive (e.g., based on the average, median, or other statistical measure of the intensity and / or luminance of pixels in the P1 image 950).

[0110] In block 1106, tear film hydration status is determined based on wavefront sensor data. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine the tear film hydration status corresponding to target 102 based at least in part on the wavefront sensor data received in block 1102. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to determine the tear film hydration status by determining the Fourier transform of the wavefront sensor data associated with the monitored area of ​​the wavefront sensor data, and by determining one or more transform features based at least in part on the Fourier transform of the wavefront sensor data. In various embodiments, for example, transform features may include one or more stripe contrast measures, such as peak amplitude, neighborhood diameter, and / or area, and / or one or more contour features as described herein, such as peak amplitude, full width at half maximum (FWHM), area, and / or other characterizations of the transform contour.

[0111] In block 1108, the synchronous tear film hydration state is determined. For example, controller 140, server 150, and / or distributed server system 154 may be configured to determine the synchronous tear film hydration state corresponding to target 102 based at least in part on the tear film hydration state determined in blocks 1104 and 1106. In some embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to determine the synchronous tear film hydration state by controlling wavefront sensor 120 and eye tracker 130 (e.g., or another imaging sensor of system 100) to image target 102 at approximately the same time (e.g., T1, T2) and / or at approximately the same rate (e.g., captures / second). In various embodiments, the controller 140, server 150, and / or distributed server system 154 may be configured to determine the synchronous tear film hydration state by combining the tear film hydration state based on Purkinje image data with the tear film hydration state based on wavefront sensor data, or by determining the average, median, and / or other statistical combinations of the tear film hydration state based on Purkinje image data and the tear film hydration state based on wavefront sensor data.

[0112] In block 1110, user feedback is generated. For example, controller 140, server 150, and / or distributed server system 154 may be configured to generate user feedback corresponding to the tear film hydration state determined in blocks 1104, 1106, and / or 1108, such as interferogram 910, first Purkinje reflection (P1) image 950, and / or a graph or map display view of the time evolution or sequence of the determined tear film hydration state, such as a tear film hydration map 1010 and / or a tear film hydration chart 1024, based at least in part on any one or combination of the determined tear film hydration state and / or ocular aberration measurement output data received in block 1102. In various embodiments, controller 140, server 150, and / or distributed server system 154 may be configured to use the information described herein. Figures 4 to 8 Any of the processes described above are used to generate user feedback.

[0113] In additional embodiments, for example, controller 140, server 150, and / or distributed server system 154 may be configured to perform various operations based at least in part on the tear film hydration status determined in blocks 1104, 1106, and / or 1008 and / or based on or in combination with user feedback generated in block 1110. For example, controller 140, server 150, and / or distributed server system 154 may be configured to detect one or more tear film hydration states determined in blocks 1104, 1106, and / or 1008, such as dehydration partial indicator 1012, tear film hydration map 1010, and / or hydration time evolution chart 1024 indicating that the tear film hydration level is below a minimum aberration measurement hydration threshold, and wavefront sensor data and / or aberration measurements of the examination sequence associated with tear film hydration levels below the minimum aberration measurement hydration threshold are omitted. In other embodiments, the aberration measurement system 100 and / or 300 may be configured to, for example, prompt (e.g., via visible or audible user feedback) the patient to blink or prompt the operator to provide saline solution when any portion of the target 102 is detected to be below a minimum aberration measurement hydration threshold or when a preset percentage (e.g., 10%) of the target 102 is detected to be below the minimum aberration measurement hydration threshold. In a further embodiment, the aberration measurement system 100 and / or 300 may be configured to project air (e.g., causing the patient to blink) or saline solution (to moisten the target 102) onto the target 102 when any portion of the target 102 or a preset percentage of the target 102 is detected to be below the minimum aberration measurement hydration threshold.

[0114] Therefore, embodiments of this disclosure can provide substantially real-time (e.g., 30 frames per second) user feedback (e.g., display views including various graphics) and reliable and accurate monitoring of ocular aberrations of the optical target 102 and / or other features affecting such ocular aberration characterization, as described herein. Such embodiments can be used to assist in various types of clinical and intraoperative ophthalmological examinations and help provide improved surgical outcomes.

[0115] Where applicable, the various embodiments provided in this disclosure may be implemented using hardware, software, or a combination of hardware and software. Similarly, where applicable, the various hardware and / or software components described herein may be combined into composite components comprising software, hardware, and / or both, without departing from the spirit of this disclosure. Where applicable, the various hardware and / or software components described herein may be divided into sub-components comprising software, hardware, or both, without departing from the spirit of this disclosure. Furthermore, where applicable, it is contemplated that a software component may be implemented as a hardware component, and vice versa.

[0116] Software according to this 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 conceivable that the software identified herein may 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 may be changed, combined into compound steps, and / or divided into sub-steps to provide the features described herein.

[0117] The embodiments described above are illustrative and do not limit the invention. It should also be understood that many modifications and variations are possible based on the principles of the invention. Therefore, the scope of the invention is defined 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 eye aberration measurement system; as well as A logic device configured to communicate with the wavefront sensor, wherein the logic device is configured to: Receive at least eye aberration measurement output data including wavefront sensor data provided by the wavefront sensor; and The tear film hydration status associated with the optical target is determined at least in part based on the received ocular aberration measurement output data; Determine when a portion of the optical target, or when a preset proportion of the optical target, is detected as below a predetermined minimum aberration measurement hydration threshold; and A projection device for projecting salt water onto an optical target when a portion of the optical target or a predetermined proportion of the optical target is detected to be below a predetermined minimum aberration measurement hydration threshold.

2. The eye aberration measurement system as described in claim 1, wherein, The tear film hydration state includes tear film hydration state based on wavefront sensor data, and wherein determining the tear film hydration state includes: Determine the Fourier transform of the received wavefront sensor data; and The tear film hydration state based on the wavefront sensor data is determined at least in part based on one or more transform features corresponding to a determined Fourier transform of the received wavefront sensor data, wherein the one or more transform features include at least one of the following: peak amplitude, full width at half maximum (FWHM), area, spatial peak amplitude, and / or spatial structure neighborhood area.

3. The eye aberration measurement system as described in claim 1, wherein, The ocular aberration measurement output data includes Purkinje image data provided by the imaging system of the ocular aberration measurement system, the tear film hydration status includes the tear film hydration status based on the Purkinje image data, and determining the tear film hydration status includes: The threshold-based binary first Purkinje reflection P1 image is determined at least in part based on the received Purkinje image data; and One or more reflection characterization parameters are determined at least in part based on the threshold-based binary P1 image.

4. The eye aberration measurement system of claim 3, further comprising the imaging system of the eye aberration measurement system, wherein: The imaging system includes an eye tracker; and The one or more reflection characterization parameters include at least one of the following in the received Purkinje image data: pixel connectivity, estimated diameter, and / or estimated area of ​​at least one reflection.

5. The eye aberration measurement system as described in claim 3, wherein, The tear film hydration state includes a synchronous tear film hydration state, and wherein determining the tear film hydration state includes: Controlling the wavefront sensor and imaging sensor of the eye aberration measurement system to image the optical target at approximately the same time and / or at the same rate; and The tear film hydration state based on Purkinje image data, which is determined at least in part based on Purkinje image data provided by the imaging system, and the tear film hydration state based on wavefront sensor data, which is determined at least in part based on received wavefront sensor data, are combined by determining the average, median, and / or other statistical combinations of the tear film hydration state based on Purkinje image data and the tear film hydration state based on wavefront sensor data.

6. The eye aberration measurement system as described in claim 5, wherein: The received Purkinje image data includes one or more reflections corresponding to LEDs in an array of light-emitting diodes (LEDs) configured to illuminate the optical target.

7. The eye aberration measurement system of claim 1, further comprising: Display device, and The logic device is configured to generate user feedback on the display device, at least in part, based on the received aberration measurement output data and / or the determined tear film hydration state, corresponding to the determined tear film hydration state.

8. The eye aberration measurement system as described in claim 7, wherein, Generating user feedback corresponding to the determined tear film hydration state includes: A display view is generated, the display view including a tear film hydration map corresponding to the determined tear film hydration state and including at least one dehydration portion indicator and / or a hydration time evolution chart including at least one hydration evolution profile.

9. The eye aberration measurement system as described in claim 8, wherein, The logic device is configured as follows: Receive user input, the user input being a selection box selection boundary indicating a selected area of ​​the tear film hydration map, wherein the at least one hydration evolution profile corresponds to the temporal evolution of tear film hydration associated with the selected area of ​​the tear film hydration map.

10. The eye aberration measurement system as claimed in claim 1, wherein, The logic device is configured as follows: The tear film hydration status determined by the test indicates that the tear film hydration level is below the minimum aberration measurement hydration threshold; as well as Wavefront sensor data that is associated with the detected tear film hydration level below the minimum aberration measurement hydration threshold is omitted from the received wavefront sensor data.

11. The eye aberration measurement system as claimed in claim 1, wherein, The logic device is configured as follows: The tear film hydration status determined by the test indicates that the tear film hydration level is below the minimum aberration measurement hydration threshold; and The eye aberration measurement system prompts the patient to blink.

12. A method for measuring eye aberrations, comprising: Receive 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; The tear film hydration status associated with the optical target is determined at least in part based on the received ocular aberration measurement output data; and Determine when a portion of the optical target, or when a preset proportion of the optical target, is detected as below a predetermined minimum aberration measurement hydration threshold; and When the portion of the optical target or the optical target at the preset ratio is detected by the projection device to be below the predetermined minimum aberration measurement hydration threshold, salt water is projected onto the optical target.

13. The method of claim 12, wherein, The tear film hydration state includes tear film hydration state based on wavefront sensor data, and wherein determining the tear film hydration state includes: Determine the Fourier transform of the received wavefront sensor data; and The tear film hydration state based on the wavefront sensor data is determined at least in part based on one or more transform features corresponding to a determined Fourier transform of the received wavefront sensor data, wherein the one or more transform features include at least one of the following: peak amplitude, full width at half maximum (FWHM), area, spatial peak amplitude, and / or spatial structure neighborhood area.

14. The method of claim 12, wherein, The ocular aberration measurement output data includes Purkinje image data provided by the imaging system of the ocular aberration measurement system, the tear film hydration status includes the tear film hydration status based on the Purkinje image data, and determining the tear film hydration status includes: The threshold-based binary first Purkinje reflection P1 image is determined at least in part based on the received Purkinje image data; and One or more reflection characterization parameters are determined at least in part based on the threshold-based binary P1 image.

15. The method of claim 14, wherein: The imaging system includes an eye tracker; and The one or more reflection characterization parameters include at least one of the following in the received Purkinje image data: pixel connectivity, estimated diameter, and / or estimated area of ​​at least one reflection.

16. The method of claim 14, wherein, The tear film hydration state includes a synchronous tear film hydration state, and wherein determining the tear film hydration state includes: Controlling the wavefront sensor and imaging sensor of the eye aberration measurement system to image the optical target at approximately the same time and / or at the same rate; and The tear film hydration state based on Purkinje image data, which is determined at least in part based on Purkinje image data provided by the imaging system, and the tear film hydration state based on wavefront sensor data, which is determined at least in part based on received wavefront sensor data, are combined by determining the average, median, and / or other statistical combinations of the tear film hydration state based on Purkinje image data and the tear film hydration state based on wavefront sensor data.

17. The method of claim 16, wherein: The received Purkinje image data includes one or more reflections corresponding to LEDs in an array of light-emitting diodes (LEDs) configured to illuminate the optical target.

18. The method of claim 12, further comprising: User feedback corresponding to the determined tear film hydration state is generated on the display device, based at least in part on the received aberration measurement output data and / or the determined tear film hydration state.

19. The method of claim 18, wherein, Generating user feedback corresponding to the determined tear film hydration state includes: A display view is generated, the display view including a tear film hydration map corresponding to the determined tear film hydration state and including at least one dehydration portion indicator and / or a hydration time evolution chart including at least one hydration evolution profile.

20. The method of claim 19, further comprising: Receive user input, the user input being a selection box selection boundary indicating a selected area of ​​the tear film hydration map, wherein the at least one hydration evolution profile corresponds to the temporal evolution of tear film hydration associated with the selected area of ​​the tear film hydration map.

21. The method of claim 12, further comprising: The tear film hydration status determined by the test indicates that the tear film hydration level is below the minimum aberration measurement hydration threshold; as well as Wavefront sensor data that is associated with the detected tear film hydration level below the minimum aberration measurement hydration threshold is omitted from the received wavefront sensor data.

22. The method of claim 12, further comprising: The tear film hydration status determined by the test indicates that the tear film hydration level is below the minimum aberration measurement hydration threshold; as well as The eye aberration measurement system prompts the patient to blink.

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