Ophthalmic device, control method therefor, program, and recording medium

Abstract

An ophthalmic device according to an embodiment collects first, second, and third data sets by using repetitive scanning on each of first, second, and third cross sections cutting through fundus blood vessels. The ophthalmic device: generates first and second intensity image sets from the first and second data sets, respectively; uses image compositing processing on each of the first and second intensity image sets to generate first and second composite intensity images; analyzes each of the first and second composite intensity images to determine first and second feature positions corresponding to fundus blood vessel feature positions; and generates fundus blood vessel orientation information in the third cross section on the basis of the first and second feature positions. Further, the ophthalmic device generates phase change information representing a change over time in phase information in the third data set, and generates hemodynamic information on the basis of the orientation information and the phase change information.

Description

Ophthalmic apparatus, its control method, program, and recording medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 557,720, entitled "OPHTHALMIC OPTICAL COHERENCE TOMOGRAPHY," filed February 26, 2024, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure relates to an ophthalmic apparatus, a control method thereof, a program, and a recording medium.

[0003] Various imaging modalities are used in ophthalmology practice, including fundus cameras, scanning laser ophthalmoscopy (SLO), slit lamp microscopes, and optical coherence tomography (OCT). OCT can be used for both structural and functional imaging.

[0004] Structural imaging using OCT is a technique for representing the spatial distribution of OCT signal intensity, which varies depending on the structure of a test object, as an image. Images generated by this technique are called OCT intensity images or simply intensity images.

[0005] OCT blood flow measurement is one of the functional imaging techniques using OCT. OCT blood flow measurement is a Doppler measurement that uses OCT to determine blood flow dynamics, and is also called Doppler OCT. OCT blood flow measurement is a technique that repeatedly scans the cross section of a blood vessel with OCT measurement light to collect a data set, and then determines the Doppler signal due to blood flow from the difference in this data set. It also determines the angle (Doppler angle) between the blood vessel and the OCT measurement light, thereby determining the magnitude of retinal blood flow velocity. Furthermore, the blood flow volume can be calculated by multiplying the obtained blood flow velocity by the cross-sectional area of ​​the blood vessel. In the field of ophthalmology, OCT blood flow measurement is typically applied to blood vessels in the fundus, particularly retinal blood vessels. However, OCT blood flow measurement of choroidal blood vessels has also been reported.

[0006] U.S. Pat. No. 1,1980,419 U.S. Pat. No. 8,175,685 U.S. Pat. No. 1,1944,382

[0007] An object of the present disclosure is to improve fundus hemodynamic measurement using Doppler OCT.

[0008] Some embodiments provide an ophthalmic apparatus for measuring hemodynamics in fundus blood vessels of a test eye using optical coherence tomography scanning. The ophthalmic apparatus includes a scanning unit, an intensity image generating unit, an image combining unit, a feature position determining unit, an orientation information generating unit, a phase change information generating unit, and a hemodynamic information generating unit. The scanning unit applies a first repeated scan to a first cross-section across the fundus blood vessels to collect a first data set, applies a second repeated scan to a second cross-section across the fundus blood vessels to collect a second data set, and applies a third repeated scan to a third cross-section across the fundus blood vessels to collect a third data set. The intensity image generating unit generates a first intensity image set based on the first data set and a second intensity image set based on the second data set. The image synthesis unit applies image synthesis processing to the first intensity image set to generate a first synthetic intensity image, and applies image synthesis processing to the second intensity image set to generate a second synthetic intensity image. The feature position determination unit analyzes the first synthetic intensity image to determine first feature positions corresponding to feature positions of the fundus blood vessels, and analyzes the second synthetic intensity image to determine second feature positions corresponding to the feature positions of the fundus blood vessels. The orientation information generation unit generates orientation information indicating the orientation of the fundus blood vessels in the third cross section based on the first feature positions and the second feature positions. The phase change information generation unit generates phase change information representing time-series changes in phase information in the third data set. The hemodynamic information generation unit generates hemodynamic information based on the orientation information and the phase change information.

[0009] According to some embodiments, it is possible to improve fundus hemodynamic measurements using Doppler OCT.

[0010] FIG. 1 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 2 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 3 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 4 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 5 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 6 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 7 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 8 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 9 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 10 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 11 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 12 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 13 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment.

[0011] Several non-limiting embodiments of the present disclosure will be described. In the present disclosure, embodiments of an ophthalmic device (e.g., an ophthalmic blood flow measuring device, an ophthalmic imaging device, etc.), embodiments of a method for controlling an ophthalmic device, embodiments of a program, and embodiments of a recording medium will be described. However, the categories of embodiments of the present disclosure are not limited to these.

[0012] Embodiments according to the present disclosure can be employed to solve problems that arise in fundus hemodynamic measurement using Doppler OCT (OCT blood flow measurement). There are various problems in fundus hemodynamic measurement using Doppler OCT.

[0013] One objective of some embodiments of the present disclosure is to improve the quality of the Doppler angle estimation process. The phase signal obtained in fundus hemodynamic measurement has a good contrast mechanism. However, when measurement is performed during the diastolic phase, when pulsation is relatively weak, the intensity of the detected signal may be low, resulting in poor measurement quality. When measuring veins, which have weaker pulsation than arteries, or when measurement is performed at an unfavorable Doppler angle, the intensity of the detected signal may also be reduced, resulting in poor measurement quality. Some embodiments provide a high-quality Doppler angle estimation method, thereby improving the quality of fundus hemodynamic measurement.

[0014] Those skilled in the art will appreciate from this disclosure that the problems that can be addressed using the techniques of this disclosure are not limited to improving the quality of Doppler angle estimation.

[0015] <Embodiments of Ophthalmic Apparatus> Several non-limiting aspects of an ophthalmic apparatus according to an embodiment will be described. The ophthalmic apparatus according to the embodiment has a function of performing OCT blood flow measurement and a function of processing data obtained by the OCT blood flow measurement.

[0016] The ophthalmic device according to the embodiment mainly described in the present disclosure functions as an OCT device capable of performing OCT blood flow measurement (OCT scan and image generation processing). In some other embodiments, the ophthalmic device may not be capable of performing at least a part of the processing of OCT blood flow measurement.

[0017] The OCT method may be any method, for example, spectral domain OCT or swept-source OCT. Spectral domain OCT is a method in which light from a low-coherence light source is split into measurement light and reference light, return light of the measurement light from the test object is superimposed on the reference light to generate interference light, the spectral distribution of the interference light is detected with a spectrometer, and the detected spectral distribution is subjected to processing such as Fourier transform to construct an image. Swept-source OCT is a method in which light from a tunable light source is split into measurement light and reference light, return light of the measurement light from the test object is superimposed on the reference light to generate interference light, the interference light is detected with a photodetector (such as a balanced photodiode), and detection data collected in response to wavelength sweeping and scanning of the measurement light is subjected to processing such as Fourier transform to construct an image. That is, spectral domain OCT is an OCT method that acquires a spectral distribution by spatial division, while swept-source OCT is an OCT method that acquires a spectral distribution by time division. It should be noted that other OCT methods, such as time domain OCT, may also be used.

[0018] The ophthalmic device according to the embodiment mainly described in the present disclosure has a function as a fundus camera capable of photographing the fundus of the eye. In some other embodiments, the ophthalmic device may have a function as any ophthalmic imaging modality, such as an SLO, a slit lamp microscope, or a surgical microscope, in addition to or instead of the function as a fundus camera.

[0019] In this disclosure, unless otherwise specified, no distinction is made between "image data" and "images" that are visual information based on the image data. Furthermore, unless otherwise specified, no distinction is made between a site or tissue of the subject's eye and its image (image data).

[0020] An ophthalmologic apparatus according to some exemplary embodiments may not have a fundus imaging function. Such an ophthalmologic apparatus may have a function of acquiring a front image of the fundus from a storage device or a recording medium. A typical example of a storage device is a medical image archiving system (medical image filing system). A typical example of a recording medium is a hard disk drive or an optical disk.

[0021] At least a portion of the functionality of elements of embodiments of the present disclosure is implemented using circuitry or processing circuitry. The circuitry or processing circuitry may be a general-purpose processor, a special-purpose processor, an integrated circuit, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), a Field Programmable Gate Array (FPGA)), or a combination of these devices configured and / or programmed to perform at least some of the disclosed functions. Array), conventional circuitry, and any combination thereof. A processor is considered to be processing circuitry or circuitry, including transistors and / or other circuitry. In this disclosure, circuitry, unit, means, or similar terms refers to hardware that performs at least a portion of the disclosed functions or hardware that is programmed to perform at least a portion of the disclosed functions. The hardware may be hardware disclosed herein or known hardware that is programmed and / or configured to perform at least a portion of the described functions. In the case of a processor, where the hardware can be considered to be a type of circuitry, circuitry, unit, means, or similar terms refers to a combination of hardware and software, and the software is used to configure the hardware and / or processor.

[0022] The configuration of an exemplary ophthalmic apparatus is shown in Figures 1 to 4. The ophthalmic apparatus 1 of this example includes a fundus camera unit 2, an OCT unit 100, and an arithmetic and control unit 200. The fundus camera unit 2 is provided with elements of a fundus camera capable of photographing the fundus and the anterior segment, and elements of an OCT scanner. The OCT unit 100 is provided with elements of an OCT scanner. The arithmetic and control unit 200 includes one or more processors configured to perform various processes (such as calculation, analysis, and control).

[0023] The fundus camera unit 2 will now be described. The fundus camera unit 2 includes an optical system for photographing the fundus Ef (and the anterior segment) of the subject's eye E. The digital image acquired by the fundus camera unit 2 is typically a front image. The fundus camera unit 2 can acquire observation images by video capture using near-infrared fixed light as illumination light, and can acquire photographed images by capture using visible flash light as illumination light, for example.

[0024] The fundus camera unit 2 includes an illumination optical system 10 and an imaging optical system 30. The illumination optical system 10 irradiates illumination light onto the subject's eye E. The imaging optical system 30 detects return light of the illumination light irradiated onto the subject's eye E. In other words, the imaging optical system 30 photographs the subject's eye E illuminated by the illumination light. OCT measurement light provided from the OCT unit 100 is guided to the subject's eye E through an optical path within the fundus camera unit 2. Return light of the OCT measurement light applied to the subject's eye E is guided to the OCT unit 100 through an optical path within the fundus camera unit 2.

[0025] The observation illumination light output from the observation light source 11 of the illumination optical system 10 is reflected by the concave mirror 12, passes through the condenser lens 13, and passes through the visible cut filter 14 to become near-infrared light. It is then focused near the imaging light source 15, reflected by the mirror 16, and passed through the relay lens system 17, the relay lens 18, the aperture 19, and the relay lens system 20 to be guided to the perforated mirror 21. It is reflected by the mirror portion around the central hole of the perforated mirror 21, passes through the dichroic mirror 46, is refracted by the objective lens 22, and is projected onto the subject's eye E (fundus Ef). The return light of the observation illumination light projected onto the subject's eye E is refracted by the objective lens 22, passes through the dichroic mirror 46, passes through the central hole of the perforated mirror 21, passes through the dichroic mirror 55, passes through the photographing focusing lens 31, is reflected by the mirror 32, passes through the half mirror 33A, is reflected by the dichroic mirror 33, and is imaged on the light-receiving surface of the image sensor 35 by the imaging lens 34. The image sensor 35 detects the return light at regular time intervals (frame rate). The focus of the photographing optical system 30 is adjusted according to the photographing region.

[0026] The imaging illumination light output from the imaging light source 15 is projected onto the fundus oculi Ef along the same path as the observation illumination light. The return light of the imaging illumination light from the subject's eye E is guided to the dichroic mirror 33 along the same path as the return light of the observation illumination light, passes through the dichroic mirror 33, is reflected by a mirror 36, and is imaged by an imaging lens 37 on the light-receiving surface of an image sensor 38.

[0027] The liquid crystal display (LCD) 39 displays a fixation target (fixation target image) for guiding and fixing the line of sight. The light beam output from the liquid crystal display 39 is reflected by the half mirror 33A, reflected by the mirror 32, passes through the photographing focusing lens 31 and the dichroic mirror 55, passes through the central hole of the perforated mirror 21, passes through the dichroic mirror 46, is refracted by the objective lens 22, and is projected onto the fundus Ef. This allows the subject to visually recognize the fixation target.

[0028] The alignment optical system 50 generates an alignment index for aligning the ophthalmic apparatus 1 with respect to the subject's eye E. Alignment light output from a light-emitting diode (LED) 51 passes through an aperture 52, an aperture 53, and a relay lens 54, is reflected by a dichroic mirror 55, passes through the central hole of the perforated mirror 21, transmits through the dichroic mirror 46, and is projected onto the subject's eye E via the objective lens 22. The return light of the alignment light from the subject's eye E is guided to the image sensor 35 via the same path as the return light of the observation illumination light. Manual alignment or automatic alignment can be performed by referring to the received light image (alignment index image).

[0029] The focusing optical system 60 generates a split index used for focus adjustment of the subject's eye E. The focusing optical system 60 moves along the optical path (illumination optical path) of the illumination optical system 10 in conjunction with movement of the photographing focusing lens 31 along the optical path (photography optical path) of the photographing optical system 30. The reflecting rod 67 is inserted into and removed from the illumination optical path. When performing focus adjustment, the reflective surface of the reflecting rod 67 is tilted relative to the illumination optical path. The focusing light output from the LED 61 passes through the relay lens 62, is split into two beams by the split index plate 63, passes through the two-hole diaphragm 64, is reflected by the mirror 65, is once imaged and reflected by the condenser lens 66 on the reflective surface of the reflecting rod 67, passes through the relay lens 20, is reflected by the perforated mirror 21, passes through the dichroic mirror 46, and is projected onto the subject's eye E via the objective lens 22. The returning light of the focusing light from the subject's eye E is guided to the image sensor 35 along the same path as the returning light of the alignment light. By referring to the received light image (split target image), manual focusing or autofocusing can be performed.

[0030] If the subject's eye E is highly hyperopic, a diopter correction lens 70 (plus lens) is placed in the photographing optical path between the perforated mirror 21 and the dichroic mirror 55. On the other hand, if the subject's eye E is highly myopic, a diopter correction lens 71 (minus lens) is placed.

[0031] The dichroic mirror 46 combines the optical path for imaging by the fundus camera unit 2 with the optical path for OCT (measurement arm). The dichroic mirror 46 reflects light in the wavelength band for OCT and transmits light in the wavelength band for imaging by the fundus camera unit 2. The measurement arm is provided with, in order from the OCT unit 100 side, a collimator lens unit 40, a retroreflector 41, a dispersion compensation member 42, an OCT focusing lens 43, an optical scanner 44, and a relay lens 45. The retroreflector 41 is movable along the optical path of the OCT measurement light incident thereon and is used to correct the optical path length according to the axial length and adjust the interference state. The dispersion compensation member 42 is used for dispersion compensation between the measurement arm and the reference arm. The OCT focusing lens 43 is movable along the measurement arm and is used to adjust the focus of the measurement arm. Focus adjustment of the ophthalmologic apparatus 1 is performed by coordination of movement of the imaging focusing lens 31, movement of the focusing optical system 60, and movement of the OCT focusing lens 43. The optical scanner 44 is positioned at a position substantially conjugate with the pupil of the subject's eye E through alignment, and changes the traveling direction of the OCT measurement light. The optical scanner 44 is, for example, a galvano scanner capable of two-dimensional scanning.

[0032] The OCT unit 100 will now be described. The OCT unit 100 shown in FIG. 2 is equipped with a spectral domain OCT optical system. This OCT optical system includes an interference optical system. This interference optical system splits light from a low-coherence light source (broadband light source) into measurement light LS (OCT measurement light) and reference light LR, and generates interference light LC by superimposing the return light of the measurement light LS projected onto the subject's eye E on the reference light LR. The generated interference light LC is detected by a spectroscope 130. This provides a signal indicating the spectral distribution of the interference light LC. This detection signal is sent to the arithmetic and control unit 200.

[0033] The light source unit 101 outputs broadband low-coherence light L0. The light source unit 101 includes an optical output device such as a superluminescent diode (SLD), an LED, or a semiconductor optical amplifier (SOA). The low-coherence light L0 output from the light source unit 101 is guided by an optical fiber 102 to a polarization controller 103 where its polarization state is adjusted, and then guided by an optical fiber 104 to a fiber coupler 105 where it is split into a measurement light LS and a reference light LR. The measurement light LS is guided by a measurement arm, and the reference light LR is guided by a reference arm.

[0034] The reference light LR is guided by an optical fiber 110 to a collimator 111 and converted into a parallel beam, passes through an optical path length correction member 112 for compensating for the optical distance between the measurement arm and the reference arm, passes through a dispersion compensation member 113 for compensating for dispersion between the measurement arm and the reference arm, and is then guided to a retroreflector 114. The retroreflector 114 is movable along the optical path of the reference light LR incident thereon and is used to correct the optical path length according to the axial length and adjust the interference state. The reference light LR that has passed through the retroreflector 114 passes through the dispersion compensation member 113 and the optical path length correction member 112 and is converted from a parallel beam into a focused beam by a collimator 116, is guided through an optical fiber 117 to a polarization controller 118 for adjusting its polarization state, is guided through an optical fiber 119 to an attenuator 120 for adjusting its light intensity, and reaches a fiber coupler 122 through an optical fiber 121.

[0035] On the other hand, the measurement light LS is guided through the optical fiber 127 to the collimator lens unit 40, where it is converted into a parallel beam, passes through the retroreflector 41, the dispersion compensation member 42, the OCT focusing lens 43, the optical scanner 44, and the relay lens 45, is reflected by the dichroic mirror 46, is refracted by the objective lens 22, and is projected onto the subject's eye E. The measurement light LS is scattered and reflected at various depth positions in the subject's eye E. Return light of the measurement light LS from the subject's eye E travels in the opposite direction through the measurement arm, is guided to the fiber coupler 105, and reaches the fiber coupler 122 via the optical fiber 128.

[0036] The fiber coupler 122 generates interference light LC by superimposing the measurement light LS incident via the optical fiber 128 and the reference light LR incident via the optical fiber 121. The generated interference light LC is guided to the spectrometer 130 via the optical fiber 129. In a non-limiting example, the spectrometer 130 converts the incident interference light LC into a parallel beam using a collimator lens, resolves the parallel beam of interference light LC into multiple spectral components using a diffraction grating, and projects the multiple spectral components generated by the diffraction grating onto an image sensor via a lens. This image sensor is, for example, a line sensor, and detects the multiple spectral components of the interference light LC to generate an electrical signal (detection signal). The generated detection signal contains information about the spectral distribution of the interference light LC and is sent to the arithmetic and control unit 200.

[0037] The OCT unit 100 in FIG. 2 described above employs a spectral domain OCT system. When swept-source OCT is used, the light source unit 101 includes a tunable light source (e.g., a near-infrared tunable laser) that rapidly changes the wavelength of emitted light. Furthermore, in the swept-source OCT optical system, interference light LC, generated by superimposing the measurement light LS and the reference light LR, is split at a predetermined splitting ratio (e.g., 1:1) to generate a pair of interference light beams, which are then detected by a photodetector. The photodetector includes a balanced photodiode. The balanced photodiode includes a pair of photodetectors that respectively detect the pair of interference light beams and outputs the difference between the pair of detection signals obtained by the pair of photodetectors. The photodetector sends this difference signal to a data acquisition system (DAQ). A clock is supplied to the data acquisition system from the light source unit 101. This clock is generated in the light source unit 101 in synchronization with the output timing of each wavelength swept within a predetermined wavelength range by the tunable light source. For example, the light source unit 101 splits light of each output wavelength to generate two split lights, optically delays one of the split lights, and then combines the two split lights, detects the resulting combined light, and generates a clock based on the detection signal. The data collection system samples the detection signal (differential signal) input from the photodetector using the clock provided by the light source unit 101. The data obtained by this sampling is provided for processing such as image generation.

[0038] In the examples shown in FIGS. 1 and 2 , optical path length changing elements (retroreflectors 41 and 114) are provided in both the measurement arm and the reference arm, but only one of them may be provided. Furthermore, the optical path length changing elements are not limited to retroreflectors. For example, the optical path length changing element in the reference arm may be a movable reflecting member (reference mirror). More generally, the ophthalmic device according to the present disclosure includes an element configured to relatively change the measurement arm length and the reference arm length (i.e., an element configured to change the optical path length difference between the measurement arm and the reference arm), and this element can be used to move the coherence gate position.

[0039] The arithmetic and control unit 200 will be described. The arithmetic and control unit 200 executes various processes, such as controlling each component of the ophthalmologic apparatus 1, various calculations, and various analyses. For example, the arithmetic and control unit 200 performs signal processing, such as Fourier transform, on the spectral distribution (interference signal, interferogram) acquired by the spectroscope 130 to calculate a reflection intensity profile of a line (A-line) extending in the depth direction (z direction) at each projection position of the measurement light LS. Furthermore, the arithmetic and control unit 200 generates image data by imaging the reflection intensity profile of each A-line. The arithmetic and control unit 200 may perform the same calculation process as image generation using conventional spectral domain OCT. The arithmetic and control unit 200 includes, for example, a processor, RAM, ROM, a hard disk drive, a communication interface, etc. Various computer programs are stored in the storage device, such as the hard disk drive. The arithmetic and control unit 200 may also include an operation device, an input device, a display device, etc.

[0040] The user interface 240 shown in FIG. 3 will be described. The user interface 240 has a display unit 241 and an operation unit 242. The display unit 241 includes, for example, the display device 3 of FIG. 1. The operation unit 242 includes various operation devices and input devices. The user interface 240 may include a touch panel. In some exemplary embodiments, at least a portion of the user interface is provided as a peripheral device connected to the ophthalmologic apparatus 1.

[0041] The moving mechanism 150 shown in Fig. 3 will be described. The moving mechanism 150 is configured to move the optical system of the ophthalmologic apparatus 1. The moving mechanism 150 moves, for example, at least the fundus camera unit 2 three-dimensionally.

[0042] The data input / output unit 290 shown in FIG. 3 will be described. The data input / output unit 290 inputs data to the ophthalmologic apparatus 1 and outputs data from the ophthalmologic apparatus 1. A non-limiting example of the data input / output unit 290 has a function for communicating with an external device (not shown). The communication unit 290 includes a communication interface according to the connection configuration with the external device. The external device may be, for example, any ophthalmologic apparatus. The external device may also be any information processing device, such as a Hospital Information System (HIS) server, a DICOM (Digital Imaging and Communication in Medicine) server, a doctor's terminal, a mobile terminal, a personal terminal, or a cloud server. Some exemplary examples of the data input / output unit 290 include a device (data reader) that reads information from a recording medium and a device (data writer) that writes information to the recording medium. The data input / output unit 290 may be, but is not limited to, the above.

[0043] The processing system (arithmetic and control system) of the ophthalmologic apparatus 1 will now be described. An example of the configuration of the processing system is shown in Figures 3 and 4. The control unit 210 and the data processing unit 230 are provided in the arithmetic and control unit 200.

[0044] The control unit 210 includes a processor and controls each unit of the ophthalmic apparatus 1. The control unit 210 includes a main control unit 211 and a memory unit 212. The main control unit 211 includes a processor and is configured to control each element of the ophthalmic apparatus 1 (including the elements shown in FIGS. 1 to 3). The main control unit 211 may also be configured to be able to control apparatuses, devices, and systems connected to the ophthalmic apparatus 1. The functions of the main control unit 211 are realized, for example, by cooperation between hardware including circuits and control software. The memory unit 212 stores various types of data. The memory unit 212 includes a storage device such as a hard disk drive or a solid state drive.

[0045] Several controls executed by the main controller 211 will be described. The main controller 211 controls an imaging focusing driver (not shown) to synchronously move the imaging focusing lens 31 and the focus optical system 60. The main controller 211 controls a retroreflector (RR) driver 41A to move the retroreflector 41 of the measurement arm. The main controller 211 controls an OCT focusing driver 43A to move the OCT focusing lens 43 of the measurement arm. The main controller 211 controls the optical scanner 44 to deflect the measurement light LS according to a preset scan pattern. The main controller 211 controls a retroreflector (RR) driver 114A to move the retroreflector 114 of the reference arm. The main controller 211 controls a moving mechanism 150 to move the optical system (e.g., the fundus camera unit 2 and the OCT unit 100).

[0046] The data processing unit 230 performs various types of data processing. For example, the data processing unit 230 applies various types of processing to images (fundus images, anterior segment images, etc.) acquired by the fundus camera unit 2. The data processing unit 230 also applies various types of processing to images acquired using OCT scanning (OCT images). The data processing unit 230 includes a processor. The data processing unit 230 is realized, for example, by cooperation between hardware including circuits and data processing software.

[0047] The data processing unit 230 includes an image generation unit 220. The image generation unit 220 processes data collected by applying an OCT scan to the fundus Ef of the subject's eye E to generate OCT image data. The image generation unit 220 includes a processor. The functions of the image generation unit 220 are realized, for example, by cooperation between hardware including circuits and image generation software.

[0048] The image generating unit 220 is configured to perform a process of generating an OCT intensity image that represents the intensity of the interference signal as visual information, and a process of generating a phase image that represents the phase information of the interference signal as visual information. A non-limiting example of the process of generating the intensity image will be described below. A non-limiting example of the process of generating the phase image will be described later, along with a description of the theoretical aspects of OCT blood flow measurement.

[0049] The image generator 220 generates an intensity image based on the data (interference signal) acquired by the spectrometer 130. Similar to conventional spectral-domain OCT, this intensity image generation process includes signal processing such as A / D conversion, denoising, filtering, and fast Fourier transform (FFT). The fast Fourier transform converts the interference signal acquired by the spectrometer 130 into an A-line profile (a reflection intensity profile along the z-direction). The A-line profile is visualized by applying imaging processing (a process of assigning pixel values ​​to reflection intensity values) to the A-line profile. This results in A-scan image data. By arranging multiple A-scan images according to a scan pattern, a cross-sectional image (e.g., B-scan image data, circle scan image data, etc.) corresponding to the scan pattern is constructed. When another OCT method is used, the cross-sectional image generator 221 performs known processing appropriate to the type of OCT method.

[0050] In some embodiments, the intensity image may be a dataset including a group of A-scan image data obtained by visualizing the reflection intensity profile of multiple A-lines arranged in the area where the OCT scan was performed. In other words, in some embodiments, the intensity image may be a dataset including a group of A-scan image data and their position information (coordinates). In another embodiment, the intensity image may be stack data constructed by embedding multiple B-scan images in a single three-dimensional coordinate system, i.e., a dataset including multiple B-scan images and their position information. In yet another embodiment, the intensity image may be volume data (voxel data) generated by applying a voxelization process to the stack data. Stack data and volume data are non-limiting examples of three-dimensional image data in which pixel coordinates are defined using a three-dimensional coordinate system. The process of generating the three-dimensional image data is performed by the image generation unit 220.

[0051] The image generation unit 220 can process the three-dimensional image data. For example, the image generation unit 220 can generate new image data by applying rendering to the three-dimensional image data. Rendering techniques include volume rendering, surface rendering, multiplanar reconstruction (MPR), maximum intensity projection (MIP), minimum intensity projection (MinIP), and average intensity projection (AIP). The image generation unit 220 can construct projection data by integrating (projecting) the three-dimensional image data in the z direction. The image generation unit 220 can construct a shadowgram by integrating (projecting) a portion of the three-dimensional image data (three-dimensional partial image data) in the z direction. The three-dimensional partial image data is extracted from the three-dimensional image data using any image segmentation method.

[0052] The ophthalmologic apparatus 1 can apply OCT blood flow measurement to the fundus Ef. The theoretical aspects of OCT blood flow measurement will be described below, as well as some non-limiting aspects of OCT blood flow measurement.

[0053] In a non-limiting aspect, blood flow measurement applies two types of scans (main scan and supplemental scan) to the fundus Ef. In the main scan, a region of interest (cross section of interest) that intersects with a blood vessel of interest in the fundus Ef at a position of interest is repeatedly scanned with the measurement light LS to acquire phase image data. On the other hand, in the supplemental scan, a predetermined cross section (supplemental cross section) is scanned with the measurement light LS to estimate the inclination of the blood vessel of interest in the cross section of interest. In a non-limiting aspect, the supplemental cross section may be, for example, a cross section (first supplemental cross section) that intersects with the blood vessel of interest and is located near the cross section of interest. In another non-limiting aspect, the supplemental cross section may be a cross section (second supplemental cross section) that intersects with the cross section of interest and is aligned with the blood vessel of interest. The inclination of the blood vessel of interest is the angle between the measurement light LS projected onto the cross section of interest and the blood vessel of interest, which is the Doppler angle in Doppler OCT.

[0054] An example of the application of the first supplemental cross section is shown in FIG. 5A. In this example, as shown in a fundus image D, one cross section of interest C0 located near the optic disc Da of the fundus oculi Ef and two supplemental cross sections C1 and C2 located nearby are set to intersect with a blood vessel of interest Db. One of the two supplemental cross sections C1 and C2 is located upstream of the blood vessel of interest Db relative to the cross section of interest C0, and the other is located downstream. The cross section of interest C0 and the supplemental cross sections C1 and C2 are oriented, for example, approximately perpendicular to the running direction of the blood vessel of interest Db.

[0055] An example of a case where the second supplemental cross section is applied is shown in FIG. 5B. In this example, a cross section of interest C0 similar to the example shown in FIG. 5A is set so as to be approximately perpendicular to the blood vessel of interest Db, and a supplemental cross section Cp is set so as to be approximately perpendicular to the cross section of interest C0. The supplemental cross section Cp is set along the blood vessel of interest Db. As an example, the supplemental cross section Cp may be set so as to pass through the central axis of the blood vessel of interest Db at the position of the cross section of interest C0.

[0056] It is desirable for the main scan in OCT blood flow measurement to collect data over a period that includes at least one cardiac cycle of the subject's heart. This makes it possible to determine the hemodynamics of blood flow in all cardiac phases. The time period for performing the main scan may be a fixed period that is set in advance, or may be a period set for each subject or each examination. This fixed period has traditionally been set to a period (e.g., 2 seconds) that is sufficiently longer than a standard cardiac cycle. Furthermore, the period set for each subject or each examination has traditionally been determined by referring to data from a biosignal detector such as an electrocardiograph.

[0057] The image generating unit 220 includes a cross-sectional image generating unit 221 and a phase image generating unit 222. The cross-sectional image generating unit 221 includes a processor, and its functions are realized, for example, by cooperation between hardware including a circuit and cross-sectional image generating software. The phase image generating unit 222 includes a processor, and its functions are realized, for example, by cooperation between hardware including a circuit and phase image generating software.

[0058] The cross-sectional image generating unit 221 generates an intensity image based on data collected by an OCT scan of the fundus oculi Ef. The intensity image generating process may be the same as a conventional image generating method in the spectral domain OCT method.

[0059] The cross-sectional image generating unit 221 generates cross-sectional images (main cross-sectional images) representing time-series changes in the morphology of the cross-section of interest based on interference signals obtained by the spectroscope 130 during main scanning of the cross-section of interest of the fundus oculi Ef. As described above, during main scanning, the ophthalmologic apparatus 1 applies repeated scans to the cross-section of interest C0. These repeated scans include multiple B-scans for the cross-section of interest C0. The interference signals sequentially generated by the spectroscope 130 in the multiple B-scans are sequentially input to the cross-sectional image generating unit 221. The cross-sectional image generating unit 221 generates one main cross-sectional image corresponding to the cross-section of interest C0 based on the interference signals corresponding to each B-scan. The cross-sectional image generating unit 221 repeats this process the number of times the B-scan is repeated during main scanning, thereby generating a series of main cross-sectional images in time series. In this way, the cross-sectional image generating unit 221 generates multiple intensity images corresponding to the multiple B-scans based on the data set collected by the repeated scanning of the main scanning. In some exemplary embodiments, the image quality of the main cross-sectional images can be improved by dividing a series of main cross-sectional images obtained by main scanning into multiple groups, and applying image synthesis (e.g., averaging) to the main cross-sectional images included in each group to generate multiple composite images.

[0060] The cross-sectional image generating unit 221 generates a cross-sectional image (supplementary cross-sectional image) representing the morphology of the supplementary cross-section based on an interference signal obtained by the spectroscope 130 during supplementary scanning of the supplementary cross-section of the fundus oculi Ef. The process of generating the supplementary cross-sectional image is performed in the same manner as the process of generating the main cross-sectional image. The supplementary cross-sectional image may be one cross-sectional image or two or more cross-sectional images. In some exemplary embodiments, the image quality of the supplementary cross-sectional image can be improved by scanning the supplementary cross-section multiple times to generate multiple cross-sectional images and applying image synthesis to these cross-sectional images to generate a synthesized image. When the supplementary cross-sections C1 and C2 illustrated in FIG. 5A are applied, the cross-sectional image generating unit 221 generates a supplementary cross-sectional image corresponding to the supplementary cross-section C1 and a supplementary cross-sectional image corresponding to the supplementary cross-section C2. When the supplementary cross-section Cp illustrated in FIG. 5B is applied, the cross-sectional image generating unit 221 generates a supplementary cross-sectional image corresponding to the supplementary cross-section Cp.

[0061] The phase image generating unit 222 generates a phase image representing a time-series change in the phase difference in the cross section of interest based on the interference signal obtained by the spectroscope 130 during the main scan. The interference signal used to generate the phase image may be the same as the interference signal used to generate the principal cross section image by the cross section image generating unit 221. In this case, a natural positional correspondence is defined between the pixels of the principal cross section image and the pixels of the phase image, making it easy to align the principal cross section image and the phase image. In contrast, in some embodiments, the principal cross section image and the phase image may be generated from different interference signals. In this case, for example, a known image registration method can be used to align the principal cross section image and the phase image.

[0062] A non-limiting example of a process for generating a phase image will now be described. The phase image in this example is obtained by calculating the phase difference between adjacent A-line complex signals (i.e., signals corresponding to adjacent scanning points). In other words, the phase image in this example is generated based on the time-series changes in pixel values ​​(brightness values) of the principal cross-sectional image. For any pixel in the principal cross-sectional image, the phase image generating unit 222 creates a graph showing the time-series changes in the brightness value of that pixel. The phase image generating unit 222 calculates the phase difference Δφ between two time points t1 and t2 (t2 = t1 + Δt) that are separated by a predetermined time interval Δt in this graph. This phase difference Δφ is then defined as the phase difference Δφ(t1) at time point t1 (or more generally, any time point between time points t1 and t2). By performing this series of processes for each of a number of preset time points, the time-series changes in the phase difference at that pixel can be obtained. Note that the time-series changes in the phase difference can be obtained by making the time interval Δt sufficiently small to ensure phase correlation. For this reason, the scanning (main scanning) of the measuring light LS executes oversampling in which the time interval Δt is set to a value smaller than the time corresponding to the resolution of the cross-sectional image.

[0063] A phase image is an image obtained by visually representing the phase difference value of each pixel at each time point (imaging process). This imaging process includes, for example, a process of representing the phase difference value using predetermined display parameters (e.g., display color, brightness, etc.). Some imaging processes can use different display colors to indicate an increase in phase over time and a decrease in phase over time. For example, an increase in phase over time can be represented by red, and a decrease can be represented by blue. Furthermore, some imaging processes can represent the magnitude of phase change (phase change amount) as the intensity of the display color. Some imaging processes described herein enable visualization of the direction and magnitude of blood flow. A phase image is generated by performing such imaging process on each pixel.

[0064] The data processing unit 230 includes, as exemplary elements for obtaining hemodynamic information, a vascular region specifying unit 231 and a hemodynamic information generating unit 232. The hemodynamic information generating unit 232 may include a Doppler angle calculating unit 233, a blood flow velocity calculating unit 234, a vascular diameter calculating unit 235, and a blood flow amount calculating unit 236.

[0065] The vascular region specifying unit 231 includes, for example, a processor operable according to a vascular region specifying program. The hemodynamic information generating unit 232 includes, for example, a processor operable according to a hemodynamic information generating program. The Doppler angle calculating unit 233 includes, for example, a processor operable according to a Doppler angle calculation program. The blood flow velocity calculating unit 234 includes, for example, a processor operable according to a blood flow velocity calculation program. The blood vessel diameter calculating unit 235 includes, for example, a processor operable according to a blood vessel diameter calculation program. The blood flow volume calculating unit 236 includes, for example, a processor operable according to a blood flow volume calculation program.

[0066] The vascular region identifying unit 231 analyzes an OCT image of the fundus and identifies an image region (vascular region) corresponding to a blood vessel in the OCT image. The vascular region identifying unit 231 also analyzes a front image of the fundus (e.g., an observed image or a photographed image acquired by the fundus camera unit 2) and identifies an image region (vascular region) corresponding to a blood vessel in the front image. The vascular region identifying process performed by the vascular region identifying unit 231 may be image processing using any image segmentation, and is performed, for example, by analyzing pixel values ​​in the target image (e.g., threshold processing). In some embodiments, the vascular region identifying unit 231 identifies a vascular region corresponding to the blood vessel of interest Db from each of the principal cross-sectional image, the supplementary cross-sectional image, and the phase image.

[0067] In some cases, the principal and supplementary cross-sectional images have sufficient resolution to be analyzed in the vascular region identification process, while the phase images do not have sufficient resolution to identify the boundaries of the vascular regions. Even in such cases, since hemodynamic information is generated based on the phase images, it is necessary to identify the vascular regions in the phase images with high accuracy. For this purpose, for example, the following process can be adopted.

[0068] When the principal cross-sectional image and the phase image are generated based on the same interference signal, the natural positional correspondence relationship (described above) defined between the pixels of the principal cross-sectional image and the pixels of the phase image can be utilized. For example, the vascular region identifying unit 231 can perform a process of analyzing the principal cross-sectional image to identify a vascular region and a process of identifying an image region in the phase image corresponding to the vascular region in the principal cross-sectional image based on the positional correspondence relationship. The image region in the phase image is adopted as the vascular region in the phase image. This allows the vascular region in the phase image to be determined with high accuracy. When the principal cross-sectional image and the phase image are generated based on mutually different interference signals, the vascular region in the phase image can be determined by utilizing the result of image registration (described above) between the principal cross-sectional image and the phase image instead of the natural positional correspondence relationship.

[0069] The hemodynamic information generating unit 232 generates information indicating the hemodynamics of the blood flow in the fundus blood vessels (hemodynamic information). The hemodynamic information may be information on any parameter (hemodynamic parameter) indicating the fundus hemodynamics. Although the present disclosure describes blood velocity and blood volume, the hemodynamic parameters are not limited to these.

[0070] The hemodynamic information generator 232 generates hemodynamic information regarding the interested blood vessel Db. As described above, the hemodynamic information generator 232 in some embodiments includes a Doppler angle calculator 233, a blood flow velocity calculator 234, a blood vessel diameter calculator 235, and a blood flow amount calculator 236.

[0071] The Doppler angle calculation unit 233 calculates an estimated value of the tilt of the blood vessel of interest based on data of the supplementary cross section (cross-sectional data, supplementary cross-sectional image) collected by the supplementary scan. The calculated value may be, for example, a value based on a measurement value of the tilt of the blood vessel of interest on the cross section of interest, or an approximate value thereof. As described above, the tilt of the blood vessel of interest is a parameter equivalent to the Doppler angle in Doppler OCT. That is, the Doppler angle is the angle between the incident direction of the measurement light LS in the main scan on the cross section of interest and the direction of the axis of the blood vessel of interest (i.e., the tilt of the blood vessel of interest), and therefore the tilt of the blood vessel of interest is equivalent to the Doppler angle.

[0072] An example of actually measuring the gradient value of the blood vessel of interest will be described (first example of gradient estimation). When the supplementary cross sections C1 and C2 shown in Fig. 5A are applied, the Doppler angle calculation unit 233 can calculate the gradient of the blood vessel of interest Db on the cross section of interest C0 based on the positional relationship between the cross section of interest C0, the supplementary cross sections C1, and the supplementary cross sections C2, and the vascular region identification result obtained by the vascular region identification unit 231.

[0073] A method for calculating the gradient of the blood vessel of interest Db will be described with reference to FIG. 6A . The symbols G0, G1, and G2 respectively denote the principal cross-sectional image at the cross-section of interest C0, the supplementary cross-section image at the supplementary cross-section C1, and the supplementary cross-section image at the supplementary cross-section C2. The symbols V0, V1, and V2 respectively denote the vascular region in the principal cross-sectional image G0, the vascular region in the supplementary cross-section image G1, and the vascular region in the supplementary cross-section image G2. The z-coordinate axis shown in FIG. 6A substantially coincides with the incident direction of the measurement light LS. The distance between the principal cross-sectional image G0 (cross-section of interest C0) and the supplementary cross-sectional image G1 (supplementary cross-section C1) is denoted by d, and the distance between the principal cross-sectional image G0 (cross-section of interest C0) and the supplementary cross-sectional image G2 (supplementary cross-section C2) is also denoted by d. The distance between adjacent cross-sectional images, i.e., the distance between adjacent cross-sections, is called the inter-section distance.

[0074] The Doppler angle calculation unit 233 can calculate the gradient A of the blood vessel of interest Db in the cross section of interest C0 based on the positional relationship between the three vascular regions V0, V1, and V2. This positional relationship can be determined, for example, by connecting the three vascular regions V0, V1, and V2. As a specific example, the Doppler angle calculation unit 233 can identify the characteristic positions of each of the three vascular regions V0, V1, and V2 and connect these characteristic positions. This characteristic position may be, for example, one of the center position, the center of gravity position, the top (the position with the smallest z-coordinate value), and the bottom (the position with the largest z-coordinate value). The characteristic positions may be connected by any method, such as connecting them with a line segment or an approximation curve (such as a spline curve or a Bezier curve).

[0075] Furthermore, the Doppler angle calculation unit 233 calculates the gradient A of the blood vessel of interest Db in the cross section of interest C0 based on a connecting line connecting the characteristic positions identified from the three vascular regions V0, V1, and V2. If the connecting line is a line segment, the Doppler angle calculation unit 233 can calculate the gradient A based on the gradient of a first line segment connecting the characteristic position of the cross section of interest C0 to the characteristic position of the supplementary cross section C1 and the gradient of a second line segment connecting the characteristic position of the cross section of interest C0 to the characteristic position of the supplementary cross section C2. A non-limiting example of this calculation process may be calculating the average gradient of the two line segments. If the connecting line is an approximated curve, the Doppler angle calculation unit 233 can calculate the gradient A as the gradient of the approximated curve at the position where the approximated curve intersects with the cross section of interest C0. In the Doppler angle calculation process, the inter-section distance d is used, for example, when embedding the cross-sectional images G0 to G2 in an xyz coordinate system to calculate the connecting line.

[0076] In the above example, the vascular region in three cross sections is considered. In some embodiments, the gradient may be calculated by considering two cross sections. As a non-limiting example, the gradient A of the blood vessel Db of interest in the cross section C0 of interest may be calculated as the gradient of the first line segment or the gradient of the second line segment. Alternatively, the gradient A of the blood vessel Db of interest in the cross section C0 of interest may be calculated based on two supplementary cross-sectional images G1 and G2.

[0077] An example of calculating an approximate value of the gradient of the blood vessel of interest (second example of gradient estimation) will be described below. When the supplementary cross section Cp shown in FIG. 5B is applied, the Doppler angle calculation unit 233 can analyze the supplementary cross section image corresponding to the supplementary cross section Cp to calculate an approximate value of the gradient of the blood vessel of interest Db on the cross section C0 of interest.

[0078] A method for approximating the gradient of the blood vessel of interest Db will be described with reference to Fig. 6B. The symbol Gp denotes a supplemental cross-sectional image at the supplemental cross-section Cp. The symbol A denotes the gradient of the blood vessel of interest Db at the cross-section of interest C0, similar to the example shown in Fig. 6A.

[0079] In this example, the Doppler angle calculation unit 233 can analyze the supplemental cross-sectional image Gp to identify an image region corresponding to a predetermined tissue of the fundus oculi Ef. For example, the Doppler angle calculation unit 233 can identify an image region (internal limiting membrane region) M corresponding to the internal limiting membrane (ILM), which is a superficial tissue of the retina. To identify the image region, for example, a known image segmentation method is used.

[0080] It is known that the internal limiting membrane and the fundus blood vessels are approximately parallel to each other. The Doppler angle calculation unit 233 calculates the gradient A of the internal limiting membrane region M on the cross section C0 of interest. ａｐｐ The gradient A of the inner limiting membrane region M in the cross section C0 of interest is calculated. ａｐｐ is used as an approximation of the gradient A of the blood vessel Db of interest in the cross section C0 of interest.

[0081] 6A and 6B is a vector representing the direction of the blood vessel of interest Db, and its value may be defined arbitrarily. In some non-limiting examples, the value of the gradient A can be defined as the angle (Doppler angle) formed by the gradient (vector) A and the z-axis. Similarly, the gradient A shown in FIG. ａｐｐ is a vector representing the direction of the inner limiting membrane region M, and its value may be defined arbitrarily. For example, the gradient (vector) A ａｐｐ The angle between the z-axis and the Doppler angle is the gradient A. ａｐｐ Here, the orientation of the z-axis substantially coincides with the incident direction of the measurement light LS.

[0082] As a third example of estimating the gradient of the blood vessel of interest, the Doppler angle calculation unit 233 can analyze the supplementary cross-sectional image Gp shown in FIG. 6B to identify an image region corresponding to the blood vessel of interest Db and determine the gradient of the image region at a position corresponding to the cross section of interest C0. In this case, the Doppler angle calculation unit 233 can, for example, perform a curve approximation on the boundary or central axis of the image region corresponding to the blood vessel of interest Db and determine the gradient of the approximated curve at a position corresponding to the cross section of interest C0. It is also possible to apply a similar curve approximation to an image region corresponding to a specific tissue of the fundus oculi Ef described above (for example, the internal limiting membrane region M).

[0083] The processing performed by the Doppler angle calculation unit 233 is not limited to the above example, and may be any processing that can obtain an estimated value of the inclination of the blood vessel Db of interest (e.g., the inclination value of the blood vessel Db itself, its approximate value, etc.) based on cross-sectional data collected by applying an OCT scan to a cross section of the fundus Ef.

[0084] The blood flow velocity calculation unit 234 calculates the blood flow velocity of blood flowing through the blood vessel Db at the cross section C0 of interest based on information on the time-series change in phase difference obtained as a phase image. The calculated information may be the value of the blood flow velocity at a specific time point (blood flow velocity value) or the time-series change in the blood flow velocity value (blood flow velocity change information). The blood flow velocity value may be a value at a specific cardiac phase selected from the cardiac cycle (e.g., the R-wave phase). The period for which the blood flow velocity change information is defined may be the entire period during which the main scan is applied to the cross section C0 of interest, or may be a selected portion of that period.

[0085] When the blood flow velocity change information is obtained, the blood flow velocity calculation unit 234 may calculate a statistical value of the blood flow velocity during the measurement period. This statistical value may be, for example, any of the mean value, standard deviation, variance, median, mode, maximum value, minimum value, local maximum value, and local minimum value. However, it is not limited to these. Furthermore, when the blood flow velocity change information is obtained, the change in the blood flow velocity can be visualized to generate visual information (e.g., a graph, a histogram, etc.).

[0086] The blood flow velocity calculation unit 234 calculates the blood flow velocity using the Doppler OCT technique. At this time, the gradient A (or its approximate value A) of the blood vessel Db of interest in the cross section C0 calculated by the Doppler angle calculation unit 233 is used. ａｐｐ Specifically, the blood flow velocity calculation unit 234 can use the following formula: Δf=[2nv cos θ] / λ.

[0087] Here, Δf indicates the Doppler shift experienced by the scattered light of the measurement light LS; n indicates the refractive index of the medium; v indicates the flow velocity (blood flow velocity) of the medium; θ indicates the angle between the incident direction of the measurement light LS and the flow vector of the medium; and λ indicates the central wavelength of the measurement light LS.

[0088] In some embodiments, n and λ are known, Δf is obtained from the time series of the phase difference, and θ is the Doppler angle (slope A or approximate value A ａｐｐ The blood flow velocity calculation unit 234 calculates the blood flow velocity v by substituting the medium refractive index n, the central wavelength λ of the measurement light LS, the Doppler shift Δf, and the Doppler angle θ into the above equation: v = [λΔf] / [2n cos θ]. Note that the method for calculating the blood flow velocity is not limited to the method described here, and any method that can be employed in Doppler OCT may be used.

[0089] The blood vessel diameter calculation unit 235 calculates the diameter of the blood vessel Db of interest in the cross section C0 of interest. Examples of this calculation method include a first calculation method using a frontal fundus image and a second calculation method using a cross section image.

[0090] When the first calculation method is applied, an image of the area of ​​the fundus Ef including the position of the cross section of interest C0 is captured in advance. The resulting frontal fundus image may be, for example, a frame of an observed image, a captured image (color image, fluorescent contrast image), or an OCT angiography image (motion contrast image).

[0091] The blood vessel diameter calculation unit 235 sets the scale of the front fundus image based on various factors that determine the relationship between the scale in the image and the scale in real space, such as the imaging angle of view (imaging magnification, scan dimension), working distance, information on the ocular optical system, etc. This scale, for example, corresponds the interval between adjacent pixels (pixel pitch) to the scale in real space (e.g., pixel pitch = 10 micrometers). The blood vessel diameter calculation unit 235 can calculate the diameter of the blood vessel Db of interest in the cross section C0 of interest, i.e., the diameter of the blood vessel region V0, based on the scale set for the front fundus image and the pixels in the blood vessel region V0.

[0092] The second calculation method will be described. In the second calculation method, a cross-sectional image of the cross-section of interest C0 is typically used. This cross-sectional image may be a principal cross-sectional image or another cross-sectional image. The scale of the cross-sectional image is determined based on the measurement conditions of the OCT, etc. In some embodiments, the cross-section of interest C0 is scanned as shown in FIG. 5A or 5B. The length of the cross-section of interest C0 is determined based on various factors that determine the relationship between the scale on the image and the scale in real space, such as the scan dimension, working distance, and information about the ocular optical system. The blood vessel diameter calculation unit 235 can calculate the diameter of the blood vessel of interest Db in the cross-section of interest C0 by performing a process of calculating the pixel pitch based on the length of the cross-section of interest C0 and a process similar to that of the first calculation method.

[0093] The blood flow rate calculation unit 236 calculates the blood flow rate in the blood vessel of interest Db based on the blood flow velocity calculated by the blood flow velocity calculation unit 234 and the blood vessel diameter calculated by the blood vessel diameter calculation unit 235. An example of this process will be described below. It is assumed that the blood flow in the blood vessel is a Hagen-Poiseuille flow. Furthermore, the blood vessel diameter is represented by w, and the maximum value of the blood flow velocity is represented by Vm. In this case, the blood flow rate Q is expressed by the following equation: Q = [πw ２ Vm] / 8.

[0094] The blood flow calculation unit 236 calculates the blood flow Q by substituting the blood vessel diameter value w calculated by the blood vessel diameter calculation unit 235 and the maximum value Vm based on the blood flow velocity value calculated by the blood flow velocity calculation unit 234 into this formula.

[0095] The types of parameters calculated by the hemodynamic information generating unit 232 are not limited to the several parameters described above. For example, the hemodynamic information generating unit 232 can calculate parameters obtained by relative measurement in addition to or instead of parameters obtained by absolute measurement, such as blood flow velocity and blood flow rate. Some non-limiting examples of hemodynamic parameters that can be used in this embodiment will be described below.

[0096] Even when an inaccurate Doppler angle value is obtained, it is possible to extract and analyze a profile from an image showing the inside of a blood vessel, or to extract the shape of a pulse wave curve (a waveform derived from the heartbeat) from the time course of an image showing the inside of a blood vessel. Several studies have demonstrated the usefulness of the relative values ​​obtained in this way. It is also possible to extract specific characteristic parameters from the waveform of a pulse wave curve. The extracted characteristic parameters can be used for hemodynamic evaluation, disease assessment, etc.

[0097] The flow of blood within a blood vessel can be considered essentially laminar, with the flow velocity decreasing as the blood approaches the vessel wall due to the frictional drag from the vessel wall, and reaching its maximum at the center of the vessel. Laminar flow is a parabolic flow, and the diastolic and systolic waveforms in the cardiac cycle can be read from the pulse wave. Therefore, the characteristics of these waveforms can be determined. For example, parameters relating to deviations from a specific waveform (such as the presence, degree, and frequency of deviations) can be determined.

[0098] The blood flow velocity in veins is not always constant, but exhibits slight variations (pulsations). Parameters that indicate these minute pulsations (absolute velocity parameters, relative velocity parameters, etc.) can be calculated.

[0099] <Non-limiting aspects of the ophthalmic device> Several non-limiting aspects realized by applying the ophthalmic device 1 having the hardware aspects, software aspects, and functional aspects described above will be described. In the following description, matters related to the ophthalmic device 1 will be referenced and used as appropriate. Any matter related to the ophthalmic device 1 can be at least partially combined with each aspect. Two or more aspects can be at least partially combined.

[0100] 7 shows the configuration of an ophthalmic apparatus 1000 according to one non-limiting embodiment. The ophthalmic apparatus 1000 includes a scanning unit 1010, a scan control unit 1020, an intensity image generating unit 1030, an image combining unit 1040, a feature position determining unit 1050, a direction information generating unit 1060, a phase change information generating unit 1070, and a hemodynamic information generating unit 1080.

[0101] 8 shows a non-limiting example of the configuration of the feature location determiner 1050. The feature location determiner 1050 of this example includes a vascular region identifier 1051, a target region extractor 1052, a local contrast enhancer 1053, an average intensity profile generator 1054, a Gaussian filter 1055, and an average intensity profile analyzer 1056. Each of the elements 1051 to 1056 of the feature location determiner 1050 of this example is optional. In some embodiments, the feature location determiner 1050 may not include any of the elements 1051 to 1056, and / or any of the elements 1051 to 1056 may be replaced with another element.

[0102] The scanning unit 1010 is configured to collect data by applying an OCT scan to the fundus oculi Ef of the subject's eye E. The scanning unit 1010 operates under the control of a scan control unit 1020. The ophthalmologic apparatus 1 of a non-limiting aspect can realize the function of the scanning unit 1010 using the fundus camera unit 2 and the OCT unit 100.

[0103] The scanning unit 1010 applies three types of scans to a pre-specified blood vessel of interest. First, the scanning unit 1010 applies a first repeated scan to a first cross-section that traverses the blood vessel of interest to collect a first data set. Second, the scanning unit 1010 applies a second repeated scan to a second cross-section that traverses the blood vessel of interest to collect a second data set. Third, the scanning unit 1010 applies a third repeated scan to a third cross-section that traverses the blood vessel of interest to collect a third data set.

[0104] The first and second cross sections are different from each other. The third cross section may be different from both the first and second cross sections, or may be the same as one of the first and second cross sections. The first to third cross sections are positioned to cross the blood vessel of interest. The first to third cross sections may be oriented perpendicular to the axis of the blood vessel of interest, or may be oriented non-perpendicular to the axis of the blood vessel of interest (i.e., may be tilted with respect to the axis of the blood vessel of interest). Each of the first to third cross sections includes the entire cross section of the blood vessel of interest. The supplementary cross sections C1 and C2 in FIG. 5A described above correspond to the first and second cross sections, and the cross section of interest C0 corresponds to the third cross section.

[0105] The first and second iterative scans correspond to the aforementioned supplementary scans. The first and second cross sections correspond to supplementary cross sections. In some embodiments, the first iterative scan performs a predetermined number of OCT scans (B-scans) on the first cross section, and the second iterative scan performs a predetermined number of OCT scans (B-scans) on the second cross section. The number of scans in the first iterative scan and the number of scans in the second iterative scan may be the same as or different from each other.

[0106] The third repeated scan corresponds to the main scan described above. The third cross section corresponds to the cross section of interest. In some embodiments, the third repeated scan involves repeatedly performing OCT scans (B-scans) on the third cross section over a predetermined period. The scan execution period is typically set to a length equal to or longer than one cardiac cycle. The scan execution period may be a predetermined length (e.g., 2 seconds) or may be determined in real time based on a signal from a biological signal detector such as an electrocardiograph or a signal obtained in the third repeated scan.

[0107] In some embodiments, when the third cross section is the same cross section as the first cross section, the first data set may be extracted from a third data set obtained in the third scan. The third data set includes a number of B-scan data corresponding to the number of B-scans repeated over a predetermined period in the third repeated scan. In this embodiment, a number of B-scan data corresponding to the number of scan repetitions in the second repeated scan is selected from the third data set. This makes it possible to make the number of B-scan data included in the first data set equal to the number of B-scan data included in the second data set when the third cross section is the same cross section as the first cross section. Note that this embodiment is premised on the number of B-scan data included in the third data set being equal to or greater than the number of B-scan data included in the second data set. Similar processing can also be performed when the third cross section is the same cross section as the second cross section. Note that in some embodiments, the number of B-scan data included in the first data set does not have to be equal to the number of B-scan data included in the second data set.

[0108] The blood vessel of interest is designated in advance. The designation of the blood vessel of interest and the designation of the first to third cross sections are performed manually or automatically. Manual designation is performed by combining the display of an image of the fundus oculi Ef using a user interface (the above-mentioned user interface 240) with a position designation operation on this displayed image. Examples of displayed images include a frontal fundus image, a rendering image of a three-dimensional fundus image, an OCT angiography image, and a vascular map, which will be described later.

[0109] The automatic designation is performed by a measurement position designation unit (the aforementioned data processing unit 230), not shown. In some aspects, the ophthalmologic device 1000 acquires in advance information (a vascular map) representing the state of blood vessels in the fundus oculi Ef. The vascular map represents, for example, the distribution of blood vessels (an image of the fundus vascular network) and / or the distribution of indices indicating the direction of the blood vessels (Doppler angle, its desirability, etc.). The vascular map is generated, for example, from a 3D OCT intensity image (stack data, volume data) of the fundus oculi Ef and / or an OCT angiography image. The measurement position designation unit designates a blood vessel of interest based on the vascular map and further designates first to third cross sections that intersect the blood vessel of interest. The criteria (selection criteria) for selecting the blood vessel and cross sections include the aforementioned indices indicating the direction of the blood vessel (Doppler angle, its desirability, etc.). In some aspects, the preferred value of the Doppler angle in OCT blood flow measurement is approximately 80 degrees, and in actual measurement, the blood vessel and cross section of interest are searched for within a range of 77 degrees to 83 degrees. Although measurement is possible even when the Doppler angle is as small as 75 degrees, a problem of phase wrapping is likely to occur. Therefore, it is considered desirable to set the lower limit of the target range to approximately 77 degrees. Furthermore, a problem of a decrease in the strength of the detected Doppler signal occurs when the Doppler angle is as large as 85 degrees. Therefore, it is considered desirable to set the upper limit of the target range to approximately 83 degrees. Note that this target range is a non-limiting example, and other target ranges may be adopted. Taking into account the actual situation regarding the Doppler angle, a suitable range of the Doppler angle in OCT blood flow measurement is typically set to 77 degrees to 83 degrees. This range is referred to as the Doppler angle allowable range. The measurement position designation unit of this example designates the blood vessel of interest and the first to third cross sections by comparing the Doppler angle at each position of each blood vessel displayed on the vascular map of the fundus oculi Ef with the Doppler angle allowable range. In some aspects, the selection criteria may include another indicator in addition to or instead of the indicator indicating the blood vessel orientation. Other examples of indices include the type of blood vessel (eg, artery, vein), size (vessel diameter), tortuosity, and location (eg, relative to the optic disc).The measurement position designation unit may determine the blood vessel of interest and the first to third cross sections by considering two or more types of indices stepwise or in parallel.

[0110] The scan control unit 1020 is configured to control the scan unit 1010. For example, the scan control unit 1020 causes the scan unit 1010 to perform the first to third iterative scans described above.

[0111] The scan control unit 1020 is realized by cooperation between hardware including a circuit and scan control software. The scan control unit 1020 has a timing function. The ophthalmologic apparatus 1 of a non-limiting embodiment can realize the functions of the scan control unit 1020 using the control unit 210 (main control unit 211).

[0112] The intensity image generating unit 1030 is configured to generate intensity images based on data collected from the fundus Ef by the scanning unit 1010. The intensity image generating unit 1030 generates a first set of intensity images based on a first data set collected from a first cross section by a first repeated scan. Furthermore, the intensity image generating unit 1030 generates a second set of intensity images based on a second data set collected from a second cross section by a second repeated scan.

[0113] In some embodiments, the first intensity image set includes a number of B-scan images equal to the number of B-scan repetitions in the first repeat scan, and the second intensity image set includes a number of B-scan images equal to the number of B-scan repetitions in the second repeat scan.

[0114] The intensity image generating unit 1030 is realized by cooperation between hardware including a circuit and intensity image generating software. The ophthalmologic apparatus 1 of a non-limiting aspect can realize the function of the intensity image generating unit 1030 by using the data processing unit 230 (the cross-sectional image generating unit 221 in the image generating unit 220).

[0115] The image synthesis unit 1040 is configured to apply an image synthesis process to the plurality of OCT intensity images to generate a synthetic intensity image. The image synthesis unit 1040 applies the image synthesis process to a first set of intensity images generated from a first data set to generate a first synthetic intensity image. Further, the image synthesis unit 1040 applies the image synthesis process to a second set of intensity images generated from a second data set to generate a second synthetic intensity image. The image synthesis unit 1040 may perform registration between the plurality of images in preparation for the image synthesis process.

[0116] Image synthesis is an image processing technique that generates one image from two or more images. In some embodiments, the image synthesis performed by the image synthesis unit 1040 includes image averaging (arithmetic average synthesis) for noise reduction. The image averaging calculates the arithmetic average of corresponding pixel values ​​between multiple images. An image obtained by applying the image averaging to a data set obtained by repeated scanning is called an average intensity image. In this example, the image synthesis unit 1040 applies the image averaging process as an image synthesis process to a first set of intensity images to generate a first average intensity image as a first synthesized intensity image. Furthermore, the image synthesis unit 1040 applies the image averaging process as an image synthesis process to a second set of intensity images to generate a second average intensity image as a second synthesized intensity image.

[0117] The image synthesis unit 1040 is realized by cooperation between hardware including a circuit and image synthesis software. The ophthalmologic apparatus 1 of a non-limiting aspect can realize the function of the image synthesis unit 1040 by using the data processing unit 230.

[0118] The feature position determination unit 1050 is configured to analyze an OCT intensity image of the fundus oculi Ef and determine a position (feature position) in the OCT intensity image corresponding to the feature position of the blood vessel of interest. The feature position determination unit 1050 analyzes a first composite intensity image generated from a first intensity image set to determine a first feature position corresponding to the feature position of the blood vessel of interest. Furthermore, the feature position determination unit 1050 analyzes a second composite intensity image generated from a second intensity image set to determine a second feature position corresponding to the feature position of the blood vessel of interest.

[0119] The feature position detected from the intensity image by the feature position determination unit 1050 may be any position in the cross-sectional image (vascular region) of the blood vessel of interest depicted in the intensity image. For example, the feature position may be any of the center position, center of gravity position, upper end position, lower end position, right end position, and left end position. The type of feature position to be detected is predetermined. Two or more types of feature positions may be detected in stages or in parallel. As an example of staged detection, detection of a first type of feature position may be performed first, and if this fails, detection of a second type of feature position may be performed.

[0120] The following describes some non-limiting examples of the processing performed by the feature position determining unit 1050. Hereinafter, the first and second composite intensity images may be collectively referred to as composite intensity images.

[0121] FIG. 9 shows two images 1100L and 1100R. The left image 1100L is an OCT intensity image (B-scan image) depicting a cross section of a fundus blood vessel (vascular region). The right image 1100R is the left image 1100L to which explanatory figures 1110, 1121, and 1122 have been added. The figure 1110 indicates the vascular region (its outline) depicted in the left image 1100L. As can be seen from the left image 1100L, two approximately elliptical images arranged vertically are depicted within the vascular region depicted by the figure 1110. In the present disclosure, the two approximately elliptical images are referred to as snowman-shaped regions (or hourglass-shaped regions). The snowman-shaped region is one of the characteristics of fundus blood vessels depicted in an OCT intensity image. In some non-limiting embodiments, the snowball region is used to determine the feature position of the cross-sectional image of the blood vessel of interest (blood vessel region). Some specific examples thereof will be described below.

[0122] In some embodiments, the feature position determination unit 1050 (vascular region identification unit 1051) identifies a vascular region in the composite intensity image corresponding to the blood vessel of interest. This process may be performed using any segmentation method. Alternatively, the vascular region may be identified by analyzing the composite intensity image to detect a snowball-shaped region. A phase image may be generated from the intensity image set that is the basis of the composite intensity image, and the phase image may be analyzed to detect a blood vessel candidate region. A region in the composite intensity image corresponding to the blood vessel candidate region may be defined as the vascular region. Alternatively, the vascular region may be searched for using a region in the composite intensity image that corresponds to the blood vessel candidate region in the phase image as a guide. Furthermore, the feature position determination unit 1050 determines the feature position based on the snowball-shaped region in the vascular region identified from the composite intensity image. According to this embodiment, the feature position determination unit 1050 identifies a vascular region (first vascular region) in the first composite intensity image that corresponds to the blood vessel of interest, and determines a feature position (first feature position) in the first vascular region based on the snowball-shaped region (first snowball-shaped region) in the first vascular region. Similarly, the feature position determination unit 1050 identifies a vascular region (second vascular region) in the second composite intensity image that corresponds to the blood vessel of interest, and determines a feature position (second feature position) in the second vascular region based on a snowman-shaped region (second snowman-shaped region) in this second vascular region.

[0123] In some embodiments, the feature location determination unit 1050 (target region extraction unit 1052) extracts a region (target region) including at least a portion of the snowman-shaped region from the composite intensity image. The feature location determination unit 1050 analyzes the extracted target region to determine the feature location. According to this embodiment, the feature location determination unit 1050 extracts a target region (first target region) including at least a portion of the first snowman-shaped region in the first composite intensity image from the first composite intensity image, and analyzes the first target region to determine a feature location (first feature location) in the first blood vessel region. Similarly, the feature location determination unit 1050 extracts a target region (second target region) including at least a portion of the second snowman-shaped region in the second composite intensity image from the second composite intensity image, and analyzes the second target region to determine a feature location (second feature location) in the second blood vessel region.

[0124] In some embodiments, the target region extracted from the composite intensity image may be a strip-shaped region extending in the A-scan direction. The A-scan direction can be expressed as the depth direction in the composite intensity image as a B-scan image, the z direction shown in FIGS. 1 and 6A , the axial direction in an OCT scan, the direction of incidence of the OCT measurement light, etc. The A-scan direction can also be expressed as the arrangement direction of two approximately elliptical images forming the snowman-shaped region, the direction along the craniocaudal axis of the snowman-shaped region, etc. The craniocaudal axis is one of the body axes in an animal and is also called the main axis, long axis, etc. In this example, the strip-shaped region is a rectangular region, and its longitudinal direction is oriented in the A-scan direction and its transverse direction is oriented in a direction perpendicular to the A-scan direction (the transverse direction, the B-scan direction, or the direction defined by a vector in the x-y plane). According to this aspect, the feature position determination unit 1050 extracts a first strip-shaped region from the first composite intensity image, the first strip-shaped region including at least a part of the first snowman-shaped region in the first composite intensity image, and analyzes the first strip-shaped region to determine a first feature position. Similarly, the feature position determination unit 1050 extracts a second strip-shaped region from the second composite intensity image, the second strip-shaped region including at least a part of the second snowman-shaped region in the second composite intensity image, and analyzes the second strip-shaped region to determine a second feature position.

[0125] In some embodiments, the dimension of the strip-shaped region in the transverse direction (the width of the strip-shaped region) is set to be equal to or less than the dimension of the blood vessel region in the transverse direction. Preferably, the dimension of the strip-shaped region in the transverse direction is set to be half the dimension of the blood vessel region. The range of dimensions shown here was found as a result of analyzing, evaluating, and examining a large number of images. According to this embodiment, the dimension (width) of the first strip-shaped region extracted by the feature location determination unit 1050 from the first composite intensity image is equal to or less than the dimension of the first blood vessel region in the transverse direction of the first cross-section, preferably half the dimension of the first blood vessel region. Similarly, the dimension (width) of the second strip-shaped region extracted by the feature location determination unit 1050 from the second composite intensity image is equal to or less than the dimension of the second blood vessel region in the transverse direction of the second cross-section, preferably half the dimension of the second blood vessel region.

[0126] FIG. 10 shows an exemplary strip-shaped region 1230 set in a composite intensity image 1200. The vertical direction of the composite intensity image 1200 is the A-scan direction, and the horizontal direction is the transverse direction. The strip-shaped region 1230 is set based on a blood vessel region 1210 including a snowman-shaped region consisting of two approximately elliptical images 1221 and 1222. In the A-scan direction, the strip-shaped region 1230 extends from the upper end to the lower end of the composite intensity image 1200. That is, the upper end of the strip-shaped region 1230 coincides with the upper end of the composite intensity image 1200, and the lower end of the strip-shaped region 1230 coincides with the lower end of the composite intensity image 1200. Furthermore, the dimension (width) of the strip-shaped region 1230 in the transverse direction is set to half the dimension of the blood vessel region 1210 in the transverse direction.

[0127] In some embodiments, the feature location determination unit 1050 (local contrast enhancement unit 1053) applies local contrast enhancement to the strip-shaped regions extracted from the composite intensity image. Objects generated as a result are referred to as enhanced strip-shaped regions. Non-limiting examples of local contrast enhancement include adaptive histogram equalization, contrast-limited adaptive histogram equalization (CLAHE), multi-peak histogram equalization (MPHE), and multi-objective beta-optimized histogram equalization (MBOBHE). By applying local contrast enhancement, local contrast is enhanced without substantially amplifying noise. This improves the visibility of the snowman-shaped region. According to this embodiment, the feature location determination unit 1050 applies local contrast enhancement to a first strip-shaped region in the first composite intensity image to generate a first enhanced strip-shaped region, and applies local contrast enhancement to a second strip-shaped region in the second composite intensity image to generate a second enhanced strip-shaped region.

[0128] In some embodiments, the feature location determination unit 1050 (average intensity profile generation unit 1054) applies intensity averaging processing in the transverse direction of a cross section of the blood vessel of interest to a strip-shaped region extracted from the composite intensity image. The object generated thereby is referred to as an average intensity profile. In a preferred embodiment, the feature location determination unit 1050 generates an average intensity profile by applying intensity averaging processing to an enhanced strip-shaped region generated using local contrast enhancement. The average intensity profile is data reflecting the distribution of brightness (intensity) in a snowball-shaped region. According to this embodiment, the feature location determination unit 1050 applies intensity averaging processing in the transverse direction of a first cross section of the blood vessel of interest to a first strip-shaped region to generate a first average intensity profile, and applies intensity averaging processing in the transverse direction of a second cross section of the blood vessel of interest to a second strip-shaped region to generate a second average intensity profile.

[0129] 11 shows a composite intensity image 1300 and an average intensity profile 1350. A blood vessel region 1310 is depicted in the composite intensity image 1300. A snowman-shaped region consisting of two approximately elliptical images 1321 and 1322 is depicted within the blood vessel region 1310. The width of the strip-shaped region 1330 is half the width of the blood vessel region 1310.

[0130] The feature position determination unit 1050 (average intensity profile generation unit 1054) averages the pixel values ​​(brightness values, intensity) of the strip-shaped region 1330 in a direction perpendicular to the z direction. That is, the feature position determination unit 1050 adds up the values ​​of multiple pixels having the same z coordinate in the strip-shaped region 1330 (i.e., the values ​​of multiple pixels arranged in a direction perpendicular to the z direction) and divides this sum by the number of those multiple pixel values. By performing this averaging operation for each z coordinate, an average intensity profile 1350 is generated. The average intensity profile 1350 exhibits a hump 1351 corresponding to the approximately elliptical image 1321 and a hump 1352 corresponding to the approximately elliptical image 1322. Between the two humps 1351 and 1352, there is a valley 1353 corresponding to the contact point of the two approximately elliptical images 1321 and 1322 (or the point between the two approximately elliptical images 1321 and 1322). The z coordinate of the valley 1353 is z ｃ It is expressed as:

[0131] In some embodiments, the feature location determiner 1050 (Gaussian filter unit 1055) applies a Gaussian filter to the average intensity profile. The data generated thereby is referred to as a filtered profile. The Gaussian filter in this embodiment is a one-dimensional Gaussian filter. The Gaussian filter reduces noise in the average intensity profile. According to this embodiment, the feature location determiner 1050 applies a Gaussian filter to a first average intensity profile to generate a first filtered profile, and applies a Gaussian filter to a second average intensity profile to generate a second filtered profile.

[0132] In some embodiments, the feature position determination unit 1050 (average intensity profile analysis unit 1056) analyzes the average intensity profile (filtered profile) to determine feature positions in the blood vessel region. The feature position determination unit 1050 determines, as feature positions, minimum points located between two maximum points in the filtered profile corresponding to two partial regions (two approximately elliptical images) of the snowman-shaped region in the composite intensity image. When processing the average intensity profile 1350 in FIG. 11 , the feature position determination unit 1050 identifies the vertices (two maximum points) of two humps 1351 and 1352 in the average intensity profile 1350, and identifies the valley (minimum point) located between these two vertices. The position of this valley (z coordinate z ｃ ) is taken as the feature location.

[0133] The configuration and operation of the feature position determining unit 1050 have been described above with reference to Fig. 8. These are non-limiting examples. The feature position determining unit 1050 may take any of several forms described below.

[0134] In some embodiments, the feature location determination unit 1050 applies a first cross-sectional intensity averaging process to a first strip-like region of the blood vessel of interest to generate a first average intensity profile, and determines a first feature location based on the first average intensity profile. Further, the feature location determination unit 1050 applies a second cross-sectional intensity averaging process to a second strip-like region of the blood vessel of interest to generate a second average intensity profile, and determines a second feature location based on the second average intensity profile. This embodiment provides an example of a feature location determination process when local contrast enhancement and a Gaussian filter are not used.

[0135] In some embodiments, the feature location determination unit 1050 applies local contrast enhancement to a first strip-shaped region to generate a first enhanced strip-shaped region, applies intensity averaging processing across the first cross-section to the first enhanced strip-shaped region to generate a first average intensity profile, and determines a first feature location based on the first average intensity profile. Furthermore, the feature location determination unit 1050 applies local contrast enhancement to a second strip-shaped region to generate a second enhanced strip-shaped region, applies intensity averaging processing across the second cross-section to the second enhanced strip-shaped region to generate a second average intensity profile, and determines a second feature location based on the second average intensity profile. This embodiment provides an example of a feature location determination process when a Gaussian filter is not used.

[0136] The process of determining the first characteristic position of the blood vessel of interest (vascular region) from the first composite intensity image and the process of determining the second characteristic position of the blood vessel of interest (vascular region) from the second composite intensity image may be performed using the same procedure or different procedures.

[0137] The feature position determining unit 1050 is realized by cooperation between hardware including a circuit and feature position determining software. The ophthalmologic apparatus 1 of a non-limiting aspect can realize the function of the feature position determining unit 1050 by using the data processing unit 230.

[0138] The orientation information generating unit 1060 is configured to generate orientation information indicating the orientation of the blood vessel of interest in the third cross section based on the characteristic position of the blood vessel of interest in the first cross section (first characteristic position) and the characteristic position of the blood vessel of interest in the second cross section (second characteristic position).

[0139] The first and second cross sections are two supplementary cross sections. The third cross section is a cross section of interest. The orientation information generation unit 1060 calculates the orientation (Doppler angle) of the blood vessel of interest in the cross section of interest, for example, in the same manner as the processing described using the Doppler angle calculation unit 233 and FIGS. 6A and 6B. Here, the pair of the first and second feature positions corresponds to two feature positions (e.g., center positions) in the two blood vessel regions V1 and V2 shown in FIG. 6A.

[0140] The orientation information generating unit 1060 is realized by cooperation between hardware including a circuit and orientation information generating software. The ophthalmologic apparatus 1 of a non-limiting aspect can realize the function of the orientation information generating unit 1060 by using the data processing unit 230 (Doppler angle calculation unit 233).

[0141] The phase change information generating unit 1070 is configured to generate phase change information representing changes in phase information in data collected from the fundus oculi Ef by the scanning unit 1010, based on the data. The phase change information generating unit 1070 generates phase change information representing time-series changes in phase information in a third data set collected from a third cross section by a third repeated scan. The phase change information generating unit 1070 generates a phase image from the third data set, for example, in the same manner as the processing described using the phase image generating unit 222. The phase image is a non-limiting example of phase change information.

[0142] The operation of the vascular region identifying unit 1051 and the operation of the phase change information generating unit 1070 can be combined. For example, a first data set acquired by applying a first repeated scan to a first cross section and a second data set acquired by applying a second repeated scan to a second cross section are input to the phase change information generating unit 1070. The phase change information generating unit 1070 generates first phase change information representing a time series change in phase information in the first data set, and generates second phase change information representing a time series change in phase information in the second data set. The first and second phase change information are input to the vascular region identifying unit 1051. The vascular region identifying unit 1051 identifies a first vascular region in a first composite intensity image generated from the first data set based on the first phase change information. Furthermore, the vascular region identifying unit 1051 identifies a second vascular region in a second composite intensity image generated from the second data set based on the second phase change information. The areas where the phase change information shows large changes are due to the blood flow within the blood vessels. This can be used to estimate the blood vessel region. There is a natural positional correspondence (coordinate correspondence) between the phase change information and the composite intensity image generated from the same data set.

[0143] The phase change information generating unit 1070 is realized by cooperation between hardware including a circuit and phase change information generating software. The ophthalmologic apparatus 1 of a non-limiting aspect can realize the function of the phase change information generating unit 1070 by using the data processing unit 230 (the phase image generating unit 222 in the image generating unit 220).

[0144] The hemodynamic information generating unit 1080 is configured to generate hemodynamic information representing the hemodynamics of blood flowing through a blood vessel of interest at a cross section of interest based on the orientation information generated by the orientation information generating unit 1060 and the phase change information generated by the phase change information generating unit 1070.

[0145] For example, the hemodynamic information generating unit 1080 can calculate the blood flow velocity based on the Doppler angle and the phase image. Furthermore, the hemodynamic information generating unit 1080 can calculate the diameter (vascular diameter) of the blood vessel of interest in the cross section of interest and calculate the blood flow volume based on this blood vessel diameter and the blood flow velocity. The blood flow velocity and blood flow volume are non-limiting examples of hemodynamic information. The hemodynamic information generating unit 1080 may be capable of calculating other hemodynamic information.

[0146] The hemodynamic information generating unit 1080 is realized by cooperation between hardware including a circuit and hemodynamic information generating software. The ophthalmologic apparatus 1 of a non-limiting embodiment can realize the function of the hemodynamic information generating unit 1080 by using the data processing unit 230 (the blood flow velocity calculating unit 234, the blood vessel diameter calculating unit 235, the blood flow rate calculating unit 236, etc.).

[0147] A non-limiting example of the operation of the ophthalmologic apparatus 1000 will be described. The processing content of each step in the example of the operation is non-limiting and may be modified as desired. Furthermore, the order of the steps in the example of the operation is non-limiting and may be modified as desired.

[0148] Fig. 12 shows an example of an operation of the ophthalmologic apparatus 1000. Fig. 13 shows an example of a sub-step of one step (step S5) of this example of operation. The blood vessel of interest, the cross section of interest (third cross section), and the supplementary cross sections (first and second cross sections) are specified before step S1 of this example of operation. The third cross section may be located, for example, between the first cross section and the second cross section, or may be the same cross section as the first cross section or the second cross section. Alternatively, the third cross section may be located outside the range bounded by the first cross section and the second cross section.

[0149] First, in step S1, the scan unit 1010, under the control of the scan control unit 1020, applies a first repeated scan to a first cross section to acquire a first data set, and applies a second repeated scan to a second cross section to acquire a second data set. The acquired first and second data sets are sent to the intensity image generation unit 1030.

[0150] In step S2, the scan unit 1010 applies a third repeated scan to a third slice to acquire a third data set under the control of the scan control unit 1020. The acquired third data set is sent to the phase change information generation unit 1070.

[0151] In step S3, the intensity image generator 1030 generates a first set of intensity images from the first data set acquired from the first cross section in step S1, and generates a second set of intensity images from the second data set acquired from the second cross section. The generated first and second sets of intensity images are sent to the image combiner 1040.

[0152] In step S4, the image synthesis unit 1040 generates a first synthesis intensity image from the first set of intensity images and a second synthesis intensity image from the second set of intensity images. The generated first and second synthesis intensity images are sent to the feature location determination unit 1050.

[0153] In step S5, the feature position determining unit 1050 determines a first feature position of the blood vessel of interest in the first cross section based on the first composite intensity image, and determines a second feature position of the blood vessel of interest in the second cross section based on the second composite intensity image. Information on the determined first and second feature positions is sent to the orientation information generating unit 1060.

[0154] The process of step S5 is performed, for example, according to the procedure shown in Fig. 13, which will be described below. This procedure is a non-limiting example. In this example, the first and second composite intensity images generated in step S4 are sent to a vascular region identifying unit 1051 of the feature position determining unit 1050.

[0155] In step S11, the vascular region identifying unit 1051 identifies a first vascular region from the first composite intensity image and a second vascular region from the second composite intensity image. Information on the identified first and second vascular regions is sent to the target region extraction unit 1052.

[0156] In step S12, the target region extraction unit 1052 extracts a first target region (first strip-shaped region) from the first composite intensity image based on the first blood vessel region, and extracts a second target region (second strip-shaped region) from the second composite intensity image based on the second blood vessel region. Information on the extracted first and second strip-shaped regions is sent to the local contrast enhancement unit 1053.

[0157] In step S13, the local contrast enhancement unit 1053 applies local contrast enhancement to the first strip-shaped region to generate a first enhanced strip-shaped region, and applies local contrast enhancement to the second strip-shaped region to generate a second enhanced strip-shaped region. The generated first and second enhanced strip-shaped regions are sent to the average intensity profile generation unit 1054.

[0158] In step S14, the average intensity profile generator 1054 generates a first average intensity profile from the first enhanced strip-shaped region and a second average intensity profile from the second enhanced strip-shaped region. The generated first and second enhanced strip-shaped regions are sent to the Gaussian filter unit 1055.

[0159] In step S15, the Gaussian filter unit 1055 applies a Gaussian filter to the first enhanced strip-shaped region to generate a first filtered profile, and applies a Gaussian filter to the second enhanced strip-shaped region to generate a second filtered profile, and the generated first and second filtered profiles are sent to the average intensity profile analysis unit 1056.

[0160] In step S16, the average intensity profile analysis unit 1056 determines a first characteristic position of the blood vessel of interest in the first cross section based on the first filtering profile, and determines a second characteristic position of the blood vessel of interest in the second cross section based on the second filtering profile.

[0161] 13, which is a non-limiting example of step S5, is now complete. Information on the first and second feature positions determined in step S16 is sent to the orientation information generation unit 1060. The processing proceeds to step S6.

[0162] In step S6, the orientation information generating unit 1060 calculates the Doppler angle of the blood vessel of interest in the third cross section (cross section of interest) based on the first characteristic position of the blood vessel of interest in the first cross section (supplementary cross section) and the second characteristic position of the blood vessel of interest in the second cross section (supplementary cross section). Information on the calculated Doppler angle is sent to the hemodynamic information generating unit 1080.

[0163] In step S7, the phase change information generating unit 1070 generates phase change information representing time-series changes in the phase information in the third data set from the third data set acquired from the third cross section in step S2. The generated phase change information is sent to the hemodynamic information generating unit 1080.

[0164] In step S8, the hemodynamic information generating unit 1080 generates hemodynamic information representing the hemodynamics of the blood vessel of interest in the third cross section (cross section of interest) based on the Doppler angle of the blood vessel of interest in the third cross section (cross section of interest) obtained in step S6 and the phase change information in the third cross section generated in step S7 (end).

[0165] The type of hemodynamic information generated in step S8 may be any type, such as blood flow velocity, blood volume, pulse wave curve, characteristic parameters of the waveform of the pulse wave curve, parameters relating to deviation of the waveform of the pulse wave curve from a specific waveform, etc.

[0166] The ophthalmologic apparatus 1000 capable of executing the processing procedure according to this operational example provides a novel method for estimating the orientation information (Doppler angle) of fundus blood vessels. This method determines the characteristic positions of the blood vessels of interest in two cross sections from two composite intensity images obtained by repeated scanning of the two cross sections and image synthesis. This makes it possible to improve the quality (e.g., accuracy and precision) of the process for determining the characteristic positions of the blood vessels of interest. Therefore, the ophthalmologic apparatus 1000 according to this embodiment contributes to improving the quality of the process for estimating the Doppler angle, thereby contributing to improving the quality of fundus hemodynamic measurements.

[0167] Some non-limiting examples of the operation of the ophthalmic apparatus 1000 will be described below.

[0168] Generally, OCT blood flow measurement involves repeated scanning over a predetermined period (e.g., 2 seconds) to acquire phase change information. Therefore, the influence of eye fixation blur cannot be ignored. Various methods for detecting fixation blur are known, including a method using an observation image of the anterior segment of the eye, a method using an observation image of the fundus, and a method using OCT images. Some ophthalmic devices may be configured to automatically repeat the measurement if a certain level of fixation blur is detected. In the case of small fixation blur, software registration can be applied to the collected image set to compensate for the misalignment between images caused by fixation blur during the scan.

[0169] Conventional ophthalmic OCT devices typically perform eye movement monitoring for fixation blur detection using an imaging device that uses a CCD camera or CMOS camera, such as the fundus camera unit 2. Therefore, the imaging rate for eye movement monitoring is lower than the repetition rate of OCT scans. In addition to this difference in sampling rate, there is also a computational delay involved in detecting positional deviations in the z direction using OCT and in the x and y directions using the imaging device. Therefore, conventional ophthalmic devices are unable to detect momentary, significant fixation blur, potentially resulting in frame loss. As a result, fixation blur causes heartbeat signal loss, disrupting the continuity of the heartbeat signal.

[0170] To address this issue, it is necessary to detect missing frames and missing heartbeat signals due to fixation blur through post-processing and perform appropriate interpolation. For this purpose, OCT intensity signals (OCT intensity image sets) or real-time biosignals (signals from an electrocardiograph or oximeter) can be used. The OCT intensity signals used may be, for example, scattering variations extracted from OCT speckle signals. Because the biosignals used contain time-series information about heartbeats, higher quality interpolation is possible compared to interpolation based on the waveform of the heartbeat signal.

[0171] In the present disclosure, non-limiting examples of techniques for reducing noise while preserving waveform and image features include a technique using AI denoising using artificial intelligence technology and a technique using an edge-preserving noise filter such as a non-local means filter. However, the denoising techniques used in the present disclosure may also be other signal processing techniques, filtering techniques, machine learning algorithms, etc. that provide equivalent effects. For example, techniques that perform smoothing processing while preserving waveform and image features, such as noise reduction methods using wavelet transforms, adaptive filtering, and other image processing algorithms, may be used.

[0172] 14 shows the configuration of an ophthalmic apparatus 1500 according to one non-limiting embodiment. The ophthalmic apparatus 1500 includes a denoising unit 1090 instead of the image synthesis unit 1040 of the ophthalmic apparatus 1000 shown in FIG. 7. Unless otherwise specified, the scanning unit 1010, the scan control unit 1020, the intensity image generating unit 1030, the feature position determining unit 1050, the orientation information generating unit 1060, the phase change information generating unit 1070, and the hemodynamic information generating unit 1080 have the same configurations and functions as the corresponding elements of the ophthalmic apparatus 1000. Furthermore, any feature related to the ophthalmic apparatus 1000 can be combined with the ophthalmic apparatus 1500 of this embodiment.

[0173] The ophthalmic apparatus 1000 in Fig. 7 performs noise reduction by combining intensity image sets obtained by repeated scans on the first and second cross sections (supplementary cross sections). In contrast, the ophthalmic apparatus 1500 of this embodiment performs noise reduction by performing denoising processing using a machine learning model on intensity images obtained by applying any type of scan (one scan, or two or more scans) to the first and second cross sections (supplementary cross sections). In other respects, the ophthalmic apparatus 1500 may be similar to the ophthalmic apparatus 1000 in Fig. 7.

[0174] The denoising unit 1090 has a denoising model constructed using machine learning, and uses this denoising model to reduce noise in the OCT intensity image. The image generated in this way is called a denoising intensity image.

[0175] The machine learning technique for constructing the denoising model may be any suitable technique, for example, supervised learning, semi-supervised learning, reinforcement learning, active learning, or at least a partial combination of two or more of these techniques.

[0176] The machine learning for constructing the denoising model may be performed using training data. The training data may include, for example, a set of pairs of pre-denosed images and denoised images generated by applying denoising processing to the pre-denosed images. The pre-denosed images may be OCT intensity images or other types of images. The machine learning model (denosed model) constructed using this training data is configured to receive the OCT intensity images as input and output a denoised intensity image. The denoising unit 1090 inputs the OCT intensity images to the denoising model and obtains the corresponding denoised intensity image output from the denoising model.

[0177] A non-limiting example of the operation of the ophthalmologic apparatus 1500 will be described. The processing content of each step in the example of the operation is non-limiting and may be modified as desired. Furthermore, the order of the steps in the example of the operation is non-limiting and may be modified as desired.

[0178] Fig. 15 shows one operation example of the ophthalmologic apparatus 1500. Unless otherwise specified, matters related to the operation example in Fig. 12 can be applied to this operation example. Also, step S25 of this operation example can be executed according to the operation example in Fig. 13. The blood vessel of interest, the cross section of interest (third cross section), and the supplementary cross sections (first and second cross sections) are specified before step S21 of this operation example.

[0179] First, in step S21, the scanning unit 1010, under the control of the scan control unit 1020, applies a first scan to a first cross section to acquire first data, and applies a second scan to a second cross section to acquire second data. The acquired first and second data are sent to the intensity image generating unit 1030. The first scan may be one B-scan, and the second scan may be one B-scan.

[0180] In step S22, the scan unit 1010 applies a third repeat scan to the third cross section to acquire a third data set under the control of the scan control unit 1020. The acquired third data set is sent to the phase change information generation unit 1070.

[0181] In step S23, the intensity image generator 1030 generates a first intensity image from the first data collected from the first cross section in step S21, and generates a second intensity image from the second data collected from the second cross section. The generated first and second intensity images are sent to the image combiner 1040.

[0182] In step S24, the denoising unit 1090 generates a first denoised intensity image from the first intensity image and a second denoised intensity image from the second intensity image using the denoising model. The generated first and second denoised intensity images are sent to the feature position determining unit 1050.

[0183] In step S25, the feature position determining unit 1050 determines a first feature position of the blood vessel of interest in the first cross section based on the first denoised intensity image, and determines a second feature position of the blood vessel of interest in the second cross section based on the second denoised intensity image. Information on the determined first and second feature positions is sent to the orientation information generating unit 1060.

[0184] In step S26, the orientation information generating unit 1060 calculates the Doppler angle of the blood vessel of interest in the third cross section (cross section of interest) based on the first characteristic position of the blood vessel of interest in the first cross section (supplementary cross section) and the second characteristic position of the blood vessel of interest in the second cross section (supplementary cross section). Information on the calculated Doppler angle is sent to the hemodynamic information generating unit 1080.

[0185] In step S27, the phase change information generating unit 1070 generates, from the third data set acquired from the third cross section in step S22, phase change information representing time-series changes in the phase information in the third data set. The generated phase change information is sent to the hemodynamic information generating unit 1080.

[0186] In step S28, the hemodynamic information generating unit 1080 generates hemodynamic information representing the hemodynamics of the blood vessel of interest in the third cross section (cross section of interest) based on the Doppler angle of the blood vessel of interest in the third cross section (cross section of interest) calculated in step S26 and the phase change information in the third cross section generated in step S27 (end).

[0187] The ophthalmologic apparatus 1500 capable of executing the processing procedure according to this operational example provides a novel method for estimating the orientation information (Doppler angle) of fundus blood vessels. This method determines the characteristic positions of the blood vessels of interest in two cross sections from two denoised intensity images obtained using scans of the two cross sections and a denoising model. This makes it possible to improve the quality (e.g., accuracy and precision) of the process for determining the characteristic positions of the blood vessels of interest. Therefore, the ophthalmologic apparatus 1500 according to this embodiment contributes to improving the quality of the process for estimating the Doppler angle, thereby contributing to improving the quality of fundus hemodynamic measurements.

[0188] Other Embodiments It will be understood by those skilled in the art that the present disclosure also provides embodiments in categories other than ophthalmic devices. For example, the present disclosure may provide an embodiment of a method for controlling an ophthalmic device, an embodiment of a method for controlling an ophthalmic information processing device, an embodiment of a program for causing a computer to execute each step of any of the methods, and an embodiment of a computer-readable non-transitory recording medium on which any of the programs is recorded. The recording medium may take any form. For example, the recording medium may be any of a magnetic disk, an optical disk, a magneto-optical disk, and a semiconductor memory.

[0189] Some embodiments are a method for controlling an ophthalmic apparatus having a scanning unit and a processor that applies OCT scans to fundus blood vessels of a test eye. The method causes the processor to execute first scanning control, second scanning control, third scanning control, intensity image generation processing, image synthesis processing, feature location processing, orientation information generation processing, phase change information generation processing, and hemodynamic information generation processing. The first scanning control controls the scanning unit to apply a first repeated scan to a first cross-section across the fundus blood vessels to collect a first data set. The second scanning control controls the scanning unit to apply a second repeated scan to a second cross-section across the fundus blood vessels to collect a second data set. The third scanning control controls the scanning unit to apply a third repeated scan to a third cross-section across the fundus blood vessels to collect a third data set. The intensity image generation processing generates a first intensity image set based on the first data set and a second intensity image set based on the second data set. The image synthesis process generates a first synthesized intensity image from the first intensity image set, and generates a second synthesized intensity image from the second intensity image set. The feature position determination process analyzes the first synthesized intensity image to determine first feature positions corresponding to feature positions of the fundus blood vessels, and analyzes the second synthesized intensity image to determine second feature positions corresponding to the feature positions of the fundus blood vessels. The orientation information generation process generates orientation information indicating the orientation of the fundus blood vessels in the third cross section based on the first feature positions and the second feature positions. The phase change information generation process generates phase change information indicating time-series changes in phase information in the third data set. The hemodynamic information generation process generates hemodynamic information based on the orientation information and the phase change information.

[0190] It is possible to create a program that causes an ophthalmic device including a computer to execute this method, and it is also possible to create a computer-readable non-transitory recording medium on which such a program is recorded.

[0191] Some embodiments are a method for controlling an ophthalmic apparatus having a scanning unit and a processor that applies OCT scans to fundus blood vessels of a test eye. The method causes the processor to execute first scan control, second scan control, third scan control, intensity image generation processing, denoising processing, feature location processing, orientation information generation processing, phase change information generation processing, and hemodynamic information generation processing. The first scan control controls the scanning unit to apply a first scan to a first cross-section across the fundus blood vessels to collect first data. The second scan control controls the scanning unit to apply a second scan to a second cross-section across the fundus blood vessels to collect second data. The third scan control controls the scanning unit to apply repeated scans to a third cross-section across the fundus blood vessels to collect data sets. The intensity image generation processing generates a first intensity image based on the first data and generates a second intensity image based on the second data. The denoising process generates a first denoised intensity image from the first intensity image using a denoising model constructed using machine learning, and generates a second denoised intensity image from the second intensity image using the denoising model. The feature position determination process analyzes the first denoised intensity image to determine first feature positions corresponding to feature positions of fundus blood vessels, and analyzes the second denoised intensity image to determine second feature positions corresponding to the feature positions of the fundus blood vessels. The orientation information generation process generates orientation information indicating the orientation of the fundus blood vessels in the third cross section based on the first and second feature positions. The phase change information generation process generates phase change information representing time-series changes in phase information in a dataset collected according to the third scan control. The hemodynamic information generation process generates hemodynamic information based on the orientation information and the phase change information.

[0192] It is possible to create a program that causes an ophthalmic device including a computer to execute this method, and it is also possible to create a computer-readable non-transitory recording medium on which such a program is recorded.

[0193] Any of the items described in this disclosure may be combined with the methods, programs, and recording media described herein.

[0194] Although several embodiments according to the present disclosure have been described above with reference to the drawings, these are non-limiting examples, and various configurations other than those described above may also be adopted.

[0195] In addition, in the flowcharts used in the above description, multiple steps (processes) are described in order. However, the order of the steps performed in each embodiment is not limited to the order described. In each embodiment, the order of the steps shown in the drawings can be changed to the extent that the content is not affected. Furthermore, the above embodiments can be at least partially combined to the extent that the content is not contradictory.

[0196] Some or all of the above embodiments can be described as follows: However, the embodiments according to the present disclosure are not limited to the following supplementary notes.

[0197] [1] An ophthalmologic apparatus for measuring blood flow dynamics in the fundus blood vessels of a subject's eye using optical coherence tomography scanning, comprising: a scanning unit that applies a first iterative scan to a first cross section that traverses the fundus blood vessels to collect a first data set, applies a second iterative scan to a second cross section that traverses the fundus blood vessels to collect a second data set, and applies a third iterative scan to a third cross section that traverses the fundus blood vessels to collect a third data set; an intensity image generating unit that generates a first intensity image set based on the first data set and generates a second intensity image set based on the second data set; and an image combining unit that applies an image combining process to the first intensity image set to generate a first combined intensity image and applies an image combining process to the second intensity image set to generate a second combined intensity image. an orientation information generating unit that generates orientation information indicating an orientation of the fundus blood vessel in the third cross section based on the first and second feature positions; a phase change information generating unit that generates phase change information indicating a time series change in phase information in the third data set; and a hemodynamic information generating unit that generates hemodynamic information based on the orientation information and the phase change information.

[0198] [2] The ophthalmologic device of claim 1, wherein the feature position determination unit: identifies a first vascular region in the first composite intensity image corresponding to the fundus blood vessels, and determines the first feature position based on a first snowball-shaped region in the first vascular region; and identifies a second vascular region in the second composite intensity image corresponding to the fundus blood vessels, and determines the second feature position based on a second snowball-shaped region in the second vascular region.

[0199] [3] The ophthalmologic apparatus of claim 2, wherein the feature position determination unit: extracts a first target region including at least a portion of the first snowman-shaped region from the first composite intensity image, analyzes the first target region to determine the first feature position; and extracts a second target region including at least a portion of the second snowman-shaped region from the second composite intensity image, and analyzes the second target region to determine the second feature position.

[0200] [4] The ophthalmologic apparatus according to claim 3, wherein the first target area is a first strip-shaped area extending in the A-scan direction in the first repeated scan, and the second target area is a second strip-shaped area extending in the A-scan direction in the second repeated scan.

[0201] [5] The ophthalmic device of item 3 above, wherein the first target area is a first strip-shaped area extending in the craniocaudal axis direction of the first snowman-shaped area, and the second target area is a second strip-shaped area extending in the craniocaudal axis direction of the second snowman-shaped area.

[0202] [6] The ophthalmologic device of claim 4 or 5, wherein the feature position determination unit applies intensity averaging processing to the first strip-shaped region in the transverse direction of the first cross section of the fundus blood vessels to generate a first average intensity profile, and determines the first feature position based on the first average intensity profile; and applies intensity averaging processing to the second strip-shaped region in the transverse direction of the second cross section of the fundus blood vessels to generate a second average intensity profile, and determines the second feature position based on the second average intensity profile.

[0203] [7] The ophthalmologic device of claim 6, wherein the feature position determination unit applies local contrast enhancement to the first strip-shaped region to generate a first enhanced strip-shaped region, applies the intensity averaging process in the transverse direction of the first cross section to the first enhanced strip-shaped region to generate a first average intensity profile, and determines the first feature position based on the first average intensity profile, applies local contrast enhancement to the second strip-shaped region to generate a second enhanced strip-shaped region, applies the intensity averaging process in the transverse direction of the second cross section to the second enhanced strip-shaped region to generate a second average intensity profile, and determines the second feature position based on the second average intensity profile.

[0204] [8] The ophthalmologic apparatus according to claim 6 or 7, wherein the feature position determination unit applies a Gaussian filter to the first average intensity profile to generate a first filtered profile, and determines the first feature position based on the first filtered profile; and applies a Gaussian filter to the second average intensity profile to generate a second filtered profile, and determines the second feature position based on the second filtered profile.

[0205] [9] An ophthalmologic device according to any one of claims 6 to 8, wherein the first snowman-shaped region has two partial regions arranged in the A-scan direction in the first repeated scan, the second snowman-shaped region has two partial regions arranged in the A-scan direction in the second repeated scan, and the feature position determination unit determines, as the first feature position, a minimum point located between two maximum points in the first average intensity profile corresponding to the two partial regions of the first snowman-shaped region, and determines, as the second feature position, a minimum point located between two maximum points in the second average intensity profile corresponding to the two partial regions of the second snowman-shaped region.

[0206]

[10] An ophthalmic device according to any one of 4 to 9 above, wherein the dimension of the first strip-shaped region in the transverse direction of the first cross section relative to the fundus blood vessels is equal to or less than the dimension of the first blood vessel region in the transverse direction, and preferably is half the dimension of the first blood vessel region; and the dimension of the second strip-shaped region in the transverse direction of the second cross section relative to the fundus blood vessels is equal to or less than the dimension of the second blood vessel region in the transverse direction, and preferably is half the dimension of the second blood vessel region.

[0207]

[11] Any of the ophthalmologic devices described above in 2 to 10, wherein the phase change information generating unit generates first phase change information representing a time series change in phase information in the first data set and second phase change information representing a time series change in phase information in the second data set, and the feature position determining unit identifies the first vascular region based on the first phase change information, and identifies the second vascular region based on the second phase change information.

[0208]

[12] The ophthalmologic device according to any one of 1 to 11 above, wherein the image synthesis unit applies an image averaging process as the image synthesis process to the first intensity image set to generate a first average intensity image as the first synthesized intensity image, and applies an image averaging process as the image synthesis process to the second intensity image set to generate a second average intensity image as the second synthesized intensity image.

[0209]

[13] An ophthalmologic apparatus for measuring blood flow dynamics in fundus blood vessels of an examinee's eye using optical coherence tomography scanning, comprising: a scanning unit that applies a first scan to a first cross section across the fundus blood vessels to collect first data, applies a second scan to a second cross section across the fundus blood vessels to collect second data, and applies repeated scans to a third cross section across the fundus blood vessels to collect a data set; an intensity image generating unit that generates a first intensity image based on the first data and a second intensity image based on the second data; and a denoising unit that has a denoising model constructed using machine learning, and generates a first denoising intensity image from the first intensity image using the denoising model, and generates a second denoising intensity image from the second intensity image using the denoising model. an orientation information generating unit that generates orientation information indicating an orientation of the fundus blood vessel in the third cross section based on the first and second feature positions; a phase change information generating unit that generates phase change information that indicates a time series change in phase information in the data set; and a hemodynamic information generating unit that generates hemodynamic information based on the orientation information and the phase change information.

[0210]

[14] A method for controlling an ophthalmologic apparatus having a scanning unit and a processor that applies an optical coherence tomography scan to fundus blood vessels of an eye to be examined, comprising: a first scanning control that controls the scanning unit to apply a first iterative scan to a first cross section that traverses the fundus blood vessels to collect a first data set; a second scanning control that controls the scanning unit to apply a second iterative scan to a second cross section that traverses the fundus blood vessels to collect a second data set; a third scanning control that controls the scanning unit to apply a third iterative scan to a third cross section that traverses the fundus blood vessels to collect a third data set; an intensity image generation process that generates a first intensity image set based on the first data set and a second intensity image set based on the second data set; and an image synthesis process that generates a first composite intensity image from the first intensity image set and a second composite intensity image from the second intensity image set. a feature position determination process for analyzing the first composite intensity image to determine a first feature position corresponding to the feature position of the fundus blood vessel and for analyzing the second composite intensity image to determine a second feature position corresponding to the feature position of the fundus blood vessel; an orientation information generation process for generating orientation information indicating the orientation of the fundus blood vessel in the third cross section based on the first feature position and the second feature position; a phase change information generation process for generating phase change information indicating a time series change in phase information in the third data set; and a hemodynamic information generation process for generating hemodynamic information based on the orientation information and the phase change information.

[0211]

[15] A method for controlling an ophthalmologic apparatus having a scanning unit and a processor that applies an optical coherence tomography scan to fundus blood vessels of an eye to be examined, comprising: a first scanning control that controls the scanning unit to apply a first scan to a first cross section that traverses the fundus blood vessels to collect first data; a second scanning control that controls the scanning unit to apply a second scan to a second cross section that traverses the fundus blood vessels to collect second data; a third scanning control that controls the scanning unit to apply repeated scans to a third cross section that traverses the fundus blood vessels to collect a data set; an intensity image generation process that generates a first intensity image based on the first data and generates a second intensity image based on the second data; and a denoising process that generates a first denoised intensity image from the first intensity image using a denoising model constructed using machine learning and generates a second denoised intensity image from the second intensity image using the denoising model. a feature position determination process for analyzing the first denoised intensity image to determine a first feature position corresponding to a feature position of the fundus blood vessel and for analyzing the second denoised intensity image to determine a second feature position corresponding to the feature position of the fundus blood vessel; an orientation information generation process for generating orientation information indicating an orientation of the fundus blood vessel in the third cross section based on the first feature position and the second feature position; a phase change information generation process for generating phase change information representing a time series change in phase information in the data set; and a hemodynamic information generation process for generating hemodynamic information based on the orientation information and the phase change information.

[0212]

[16] A program for causing a computer to execute the method of 14 or 15 above.

[0213]

[17] A computer-readable non-transitory recording medium on which the program of 16 above is recorded.

[0214] The present disclosure is merely an example of how to implement the present invention, and those who intend to implement the present invention can make any modifications (omissions, substitutions, additions, etc.) within the scope of the gist of the present invention.

[0215] REFERENCE SIGNS LIST 1000 Ophthalmic apparatus 1010 Scan unit 1020 Scan control unit 1030 Intensity image generation unit 1040 Image synthesis unit 1050 Feature position determination unit 1060 Orientation information generation unit 1070 Phase change information generation unit 1080 Hemodynamic information generation unit 1090 Denoising unit

Claims

1. An ophthalmologic apparatus for measuring blood flow dynamics in the fundus blood vessels of a subject's eye using optical coherence tomography scanning, comprising: a scanning unit that applies a first repeated scan to a first cross section that traverses the fundus blood vessels to collect a first data set, applies a second repeated scan to a second cross section that traverses the fundus blood vessels to collect a second data set, and applies a third repeated scan to a third cross section that traverses the fundus blood vessels to collect a third data set; an intensity image generating unit that generates a first intensity image set based on the first data set and generates a second intensity image set based on the second data set; and an image combining unit that applies an image combining process to the first intensity image set to generate a first combined intensity image and applies an image combining process to the second intensity image set to generate a second combined intensity image. an orientation information generating unit that generates orientation information indicating an orientation of the fundus blood vessel in the third cross section based on the first and second feature positions; a phase change information generating unit that generates phase change information indicating a time series change in phase information in the third data set; and a hemodynamic information generating unit that generates hemodynamic information based on the orientation information and the phase change information.

2. The ophthalmologic device of claim 1, wherein the feature position determination unit: identifies a first vascular region in the first composite intensity image corresponding to the fundus blood vessels, and determines the first feature position based on a first snowball-shaped region in the first vascular region; and identifies a second vascular region in the second composite intensity image corresponding to the fundus blood vessels, and determines the second feature position based on a second snowball-shaped region in the second vascular region.

3. The ophthalmologic device of claim 2, wherein the feature position determination unit: extracts a first target region including at least a portion of the first snowman-shaped region from the first composite intensity image, analyzes the first target region to determine the first feature position; and extracts a second target region including at least a portion of the second snowman-shaped region from the second composite intensity image, and analyzes the second target region to determine the second feature position.

4. An ophthalmic device according to claim 3, wherein the first target area is a first strip-shaped area extending in the A-scan direction in the first repeated scan, and the second target area is a second strip-shaped area extending in the A-scan direction in the second repeated scan.

5. An ophthalmic device according to claim 3, wherein the first target area is a first strip-shaped area extending in the craniocaudal axis direction of the first snowman-shaped area, and the second target area is a second strip-shaped area extending in the craniocaudal axis direction of the second snowman-shaped area.

6. An ophthalmologic device according to claim 4 or 5, wherein the feature position determination unit applies intensity averaging processing to the first strip-shaped region in the transverse direction of the first cross-section of the fundus blood vessels to generate a first average intensity profile, and determines the first feature position based on the first average intensity profile, and applies intensity averaging processing to the second strip-shaped region in the transverse direction of the second cross-section of the fundus blood vessels to generate a second average intensity profile, and determines the second feature position based on the second average intensity profile.

7. An ophthalmic device according to claim 6, wherein the feature position determination unit applies local contrast enhancement to the first strip-shaped region to generate a first enhanced strip-shaped region, applies the intensity averaging process in the transverse direction of the first cross section to the first enhanced strip-shaped region to generate the first average intensity profile, and determines the first feature position based on the first average intensity profile, applies local contrast enhancement to the second strip-shaped region to generate a second enhanced strip-shaped region, applies the intensity averaging process in the transverse direction of the second cross section to the second enhanced strip-shaped region to generate the second average intensity profile, and determines the second feature position based on the second average intensity profile.

8. The ophthalmologic apparatus of claim 6 or 7, wherein the feature position determination unit applies a Gaussian filter to the first average intensity profile to generate a first filtered profile, and determines the first feature position based on the first filtered profile, and applies a Gaussian filter to the second average intensity profile to generate a second filtered profile, and determines the second feature position based on the second filtered profile.

9. An ophthalmic device according to any one of claims 6 to 8, wherein the first snowman-shaped region has two partial regions arranged in the A-scan direction in the first repeated scan, the second snowman-shaped region has two partial regions arranged in the A-scan direction in the second repeated scan, and the feature position determination unit determines, as the first feature position, a minimum point located between two maximum points in the first average intensity profile corresponding to the two partial regions of the first snowman-shaped region, and determines, as the second feature position, a minimum point located between two maximum points in the second average intensity profile corresponding to the two partial regions of the second snowman-shaped region.

10. An ophthalmic device according to any one of claims 4 to 9, wherein the dimension of the first strip-shaped region in the transverse direction of the first cross section relative to the fundus blood vessels is equal to or smaller than the dimension of the first blood vessel region in that transverse direction, and preferably is half the dimension of the first blood vessel region; and the dimension of the second strip-shaped region in the transverse direction of the second cross section relative to the fundus blood vessels is equal to or smaller than the dimension of the second blood vessel region in that transverse direction, and preferably is half the dimension of the second blood vessel region.

11. An ophthalmic device according to any one of claims 2 to 10, wherein the phase change information generating unit generates first phase change information representing a time series change in phase information in the first data set and second phase change information representing a time series change in phase information in the second data set, and the feature position determining unit identifies the first vascular region based on the first phase change information, and identifies the second vascular region based on the second phase change information.

12. An ophthalmic device according to any one of claims 1 to 11, wherein the image synthesis unit applies an image averaging process as the image synthesis process to the first intensity image set to generate a first average intensity image as the first synthesized intensity image, and applies an image averaging process as the image synthesis process to the second intensity image set to generate a second average intensity image as the second synthesized intensity image.

13. An ophthalmologic apparatus for measuring blood flow dynamics in fundus blood vessels of a subject's eye using optical coherence tomography scanning, comprising: a scanning unit that applies a first scan to a first cross section across the fundus blood vessels to collect first data, applies a second scan to a second cross section across the fundus blood vessels to collect second data, and applies repeated scans to a third cross section across the fundus blood vessels to collect a data set; an intensity image generating unit that generates a first intensity image based on the first data and generates a second intensity image based on the second data; and a denoising unit that has a denoising model constructed using machine learning, and generates a first denoising intensity image from the first intensity image using the denoising model, and generates a second denoising intensity image from the second intensity image using the denoising model. an orientation information generating unit that generates orientation information indicating an orientation of the fundus blood vessel in the third cross section based on the first and second feature positions; a phase change information generating unit that generates phase change information that indicates a time series change in phase information in the data set; and a hemodynamic information generating unit that generates hemodynamic information based on the orientation information and the phase change information.

14. A method for controlling an ophthalmologic apparatus having a scanning unit that applies an optical coherence tomography scan to fundus blood vessels of a subject's eye and a processor, comprising: a first scan control that controls the scanning unit to apply a first iterative scan to a first cross section that traverses the fundus blood vessels to collect a first data set; a second scan control that controls the scanning unit to apply a second iterative scan to a second cross section that traverses the fundus blood vessels to collect a second data set; a third scan control that controls the scanning unit to apply a third iterative scan to a third cross section that traverses the fundus blood vessels to collect a third data set; an intensity image generation process that generates a first intensity image set based on the first data set and a second intensity image set based on the second data set; and an image synthesis process that generates a first synthesized intensity image from the first intensity image set and a second synthesized intensity image from the second intensity image set. a feature position determination process for analyzing the first composite intensity image to determine a first feature position corresponding to the feature position of the fundus blood vessel and for analyzing the second composite intensity image to determine a second feature position corresponding to the feature position of the fundus blood vessel; an orientation information generation process for generating orientation information indicating the orientation of the fundus blood vessel in the third cross section based on the first feature position and the second feature position; a phase change information generation process for generating phase change information indicating a time series change in phase information in the third data set; and a hemodynamic information generation process for generating hemodynamic information based on the orientation information and the phase change information.

15. A method for controlling an ophthalmologic apparatus having a scanning unit that applies an optical coherence tomography scan to fundus blood vessels of a subject's eye and a processor, comprising: a first scanning control that controls the scanning unit to apply a first scan to a first cross section that traverses the fundus blood vessels to collect first data; a second scanning control that controls the scanning unit to apply a second scan to a second cross section that traverses the fundus blood vessels to collect second data; a third scanning control that controls the scanning unit to apply repeated scans to a third cross section that traverses the fundus blood vessels to collect a data set; an intensity image generation process that generates a first intensity image based on the first data and generates a second intensity image based on the second data; and a denoising process that generates a first denoised intensity image from the first intensity image using a denoising model constructed using machine learning and generates a second denoised intensity image from the second intensity image using the denoising model. a feature position determination process for analyzing the first denoised intensity image to determine a first feature position corresponding to a feature position of the fundus blood vessel and for analyzing the second denoised intensity image to determine a second feature position corresponding to the feature position of the fundus blood vessel; an orientation information generation process for generating orientation information indicating an orientation of the fundus blood vessel in the third cross section based on the first feature position and the second feature position; a phase change information generation process for generating phase change information representing a time series change in phase information in the data set; and a hemodynamic information generation process for generating hemodynamic information based on the orientation information and the phase change information.

16. A program for causing a computer to execute the method of claim 14 or 15.

17. A computer-readable non-transitory recording medium on which the program of claim 16 is recorded.

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