Ophthalmic device, method for controlling same, program, and recording medium

Abstract

An ophthalmic device according to an embodiment executes OCT blood flow measurement for measuring blood flow dynamics in a fundus blood vessel. A scan unit of the ophthalmic device applies an OCT scan to the fundus of a subject eye and collects data. A scan control unit controls the scan unit. A data processing unit processes the data collected by the scan unit. The scan control unit cyclically applies OCT scans to a plurality of cross-sections of one or more blood vessels of interest in the fundus of the subject eye and collects a plurality of datasets respectively corresponding to the plurality of cross-sections. The data processing unit generates a plurality of pieces of blood flow dynamics information respectively corresponding to the plurality of cross-sections on the basis of the plurality of datasets collected by the cyclic OCT scans.

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] An ophthalmologic apparatus according to some embodiments performs optical coherence tomography blood flow measurement for measuring blood flow dynamics in fundus blood vessels, and includes a scanning unit, a scanning control unit, and a data processing unit. The scanning unit is configured to collect data by applying an optical coherence tomography scan to the fundus of a subject's eye. The scanning control unit is configured to control the scanning unit. The data processing unit is configured to process the data collected by the scanning unit. The scanning control unit controls the scanning unit to apply optical coherence tomography scans cyclically to multiple cross sections of one or more blood vessels of interest in the fundus of the subject's eye to collect multiple data sets corresponding to the multiple cross sections. The data processing unit generates multiple pieces of blood flow dynamics information corresponding to the multiple cross sections based on the multiple data sets collected by the cyclic optical coherence tomography scans.

[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. FIG. 14 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 15 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 16 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 17 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 18 is a schematic diagram showing a configuration of an ophthalmic apparatus according to a non-limiting embodiment. FIG. 19 is a schematic diagram showing a configuration of an o 1 is a schematic diagram for explaining the operation of an ophthalmologic apparatus according to a non-limiting embodiment, and FIG. 2 is a schematic diagram for explaining the operation of an ophthalmologic 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] The embodiments according to the present disclosure can be employed to solve problems that arise in measuring the dynamics of fundus hemodynamics using Doppler OCT. There are various problems in measuring the dynamics of fundus hemodynamics using Doppler OCT.

[0013] Some embodiments of the present disclosure can perform hemodynamic measurements at multiple locations in the fundus vascular network in parallel. In other words, some embodiments of the present disclosure can perform hemodynamic measurements at multiple locations in the fundus vascular network as a series of scan sequences. In other words, some embodiments of the present disclosure can perform hemodynamic measurements at multiple locations in the fundus vascular network as a single scan protocol.

[0014] This embodiment has various aspects. In some aspects, multiple locations in a single blood vessel are measured in parallel. This aspect has various advantages. Non-limiting advantages include the ability to improve the quality of measurement of hemodynamics in a single blood vessel (e.g., at least one of accuracy, precision, reproducibility, and reliability), and the ability to compare measurement results at multiple locations in a single blood vessel. Advantages of this aspect are not limited to these. This aspect provides actions and effects according to its application.

[0015] Some embodiments measure multiple locations on multiple blood vessels in parallel. This embodiment has various advantages. Non-limiting advantages include the ability to measure multiple blood vessels in a shorter time than conventional methods, reducing the effort required for measurement, reducing the processing load during measurement, and reducing the burden on the subject. The advantages of this embodiment are not limited to these. This embodiment provides actions and effects according to its application. Cases considered by this embodiment (i.e., embodiments in which multiple locations on multiple blood vessels are measured) include not only cases in which one measurement location is set for each of the multiple blood vessels, but also cases in which two or more measurement locations are set for one of the multiple blood vessels, and cases in which two or more measurement locations are set for each of two or more blood vessels. In other words, the number of the multiple measurement locations is equal to or greater than the number of the multiple blood vessels.

[0016] Some embodiments of the present disclosure can provide information indicating the morphology and appearance of the fundus vascular network using the results of hemodynamic measurements performed in parallel on multiple locations of the fundus vascular network. Non-limiting examples of the provided information include fundus blood vessel orientation information and fundus blood vessel distribution (vascular map). The provided information may be used to visualize and digitize the fundus blood vessel morphology, and may also be used to determine target locations for further measurement. Applications of the provided information are not limited to these. This aspect also provides functions and effects according to the application. For example, the phase signal obtained by fundus hemodynamic measurement has a good contrast mechanism. However, when measurements are performed during the diastolic phase, when pulsation is relatively weak, the detected signal intensity is low, which may result in poor measurement quality. When measurements are performed on veins, which have weaker pulsation than arteries, or when measurements are performed at an unsuitable Doppler angle, the detected signal intensity may also be reduced, resulting in poor measurement quality. Furthermore, fundus blood vessels run and are distributed in a complex three-dimensional manner. The actual vascular course can be determined by performing fundus imaging. Conventionally, this information has not been utilized. The present aspect contributes to the realization of selecting a measurement position (e.g., selecting a blood vessel or a cross section) for detecting a phase signal with sufficient strength by providing blood vessel orientation information and a blood vessel map that can be used for measuring fundus hemodynamics.

[0017] It will be understood by those skilled in the art that the problems that can be addressed using the technology disclosed herein are not limited to the examples given above.

[0018] <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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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).

[0032] 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 and autofocusing can be performed.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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).

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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 supplemental cross section Cp shown in FIG. 5B is applied, the Doppler angle calculation unit 233 can analyze the supplemental cross section image corresponding to the supplemental 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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).

[0086] 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.

[0087] 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.

[0088] 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.).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] <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.

[0103] 7 shows the configuration of an ophthalmologic apparatus 1000 according to one non-limiting embodiment. The ophthalmologic apparatus 1000 includes a scanning unit 1010, a scanning control unit 1020, and a data processing unit 1030. The data processing unit 1030 includes an image generating unit 1031, a hemodynamic information generating unit 1032, a map information generating unit 1033, a hemodynamic information comparing unit 1034, a cardiac waveform generating unit 1035, and a cardiac waveform comparing unit 1036.

[0104] The scanning unit 1010 is configured to collect data by applying an OCT scan to the fundus Ef of the subject's eye E. The ophthalmologic apparatus 1 of a non-limiting embodiment can realize the function of the scanning unit 1010 using the fundus camera unit 2 and the OCT unit 100.

[0105] The scan control unit 1020 is configured to control the scan unit 1010. For example, the scan control unit 1020 controls the scan unit 1010 to apply an OCT scan for OCT blood flow measurement to the fundus Ef. In this embodiment, OCT blood flow measurement is applied to each of multiple locations on the fundus Ef. The OCT scan mode executed by the scan unit 1010 under the control of the scan control unit 1020 may be any. Some non-limiting examples of this OCT scan mode will be described later.

[0106] The scan control unit 1020 is realized by cooperation between hardware including circuits and scan control software. 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).

[0107] The data processing unit 1030 is configured to process data collected from the fundus oculi Ef by the scanning unit 1010. The data processing unit 1030 may be configured to process other data. For example, the data processing unit 1030 may be configured to process frontal fundus images such as fundus camera images or SLO images.

[0108] The data processing unit 1030 is realized by cooperation between hardware including circuits and data processing software. The ophthalmologic apparatus 1 of a non-limiting embodiment can realize the function of the data processing unit 1030 by using the data processing unit 230.

[0109] The image generating unit 1031 is configured to generate an image based on data collected from the fundus oculi Ef by the scanning unit 1010. The data generated by the scanning unit 1010 includes an interference signal.

[0110] The image generating unit 1031 executes a process of extracting intensity information from the interference signal. The image generating unit 1031 may be capable of executing a process of converting the intensity information extracted from the interference signal into visual information to generate an OCT intensity image. For details of these processes, please refer to the above-mentioned description of the cross-sectional image generating unit 221.

[0111] The image generating unit 1031 executes a process of extracting phase information from the interference signal. The image generating unit 1031 may be capable of executing a process of determining a time-series change in phase difference (phase difference information) from the phase information extracted from the interference signal. Furthermore, the image generating unit 1031 may be capable of executing a process of converting this phase difference information into visual information to generate a phase image. For details of these processes, please refer to the above-mentioned description of the phase image generating unit 222.

[0112] The image generation unit 1031 is realized by cooperation between hardware including a circuit and image generation software. The ophthalmologic apparatus 1 according to a non-limiting embodiment can realize the function of the image generation unit 1031 by using the image generation unit 220.

[0113] The hemodynamic information generating unit 1032 is configured to generate hemodynamic information based on the information generated by the image generating unit 1031. In this embodiment, OCT blood flow measurement is applied to each of multiple locations on the fundus Ef, and hemodynamic information at each location is generated. Some non-limiting examples of the process of generating hemodynamic information will be described later.

[0114] The hemodynamic information generating unit 1032 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 1032 using the data processing unit 230 (such as the vascular region specifying unit 231, the blood flow velocity calculating unit 234, the vascular diameter calculating unit 235, and the blood flow rate calculating unit 236).

[0115] The map information generating unit 1033 is configured to generate first map information based on the hemodynamic information generated by the hemodynamic information generating unit 1032. In this embodiment, OCT blood flow measurement is applied to multiple locations on the fundus Ef, and hemodynamic information at each location is generated. The first map information represents the distribution of measurement results (hemodynamic information) at the multiple locations where OCT blood flow measurement was applied. This distribution is defined by the correspondence between position information and hemodynamic information. Some non-limiting examples of the process of generating such first map information (hemodynamic map information) will be described later.

[0116] In this embodiment, OCT blood flow measurements are applied to multiple locations on the fundus oculi Ef, and OCT intensity images (cross-sectional images) are generated at each location. The map information generator 1033 is configured to generate second map information based on the cross-sectional images at the multiple locations where OCT blood flow measurements were applied. The second map information represents the position information and cross-sectional morphology of each location where OCT blood flow measurements were applied. Such second map information represents the morphology of blood vessels to which OCT blood flow measurements were applied. Some non-limiting examples of the process of generating such second map information (vascular morphology map information) will be described later.

[0117] The map information generating unit 1033 is realized by cooperation between hardware including a circuit and map information generating software. The ophthalmologic apparatus 1 according to a non-limiting embodiment can realize the function of the map information generating unit 1033 by using the data processing unit 230.

[0118] In this embodiment, OCT blood flow measurement is applied to multiple locations on the fundus Ef, and hemodynamic information at each location is generated. The hemodynamic information comparison unit 1034 compares two or more pieces of hemodynamic information corresponding to two or more of the multiple locations to which OCT blood flow measurement was applied. The information generated by this hemodynamic comparison process is referred to as hemodynamic comparison information. Any combination of two or more pieces of hemodynamic information to be compared may be used. For example, hemodynamic comparison information may be generated by comparing two or more pieces of information corresponding to two or more cross sections set on the same blood vessel, or by comparing two or more pieces of information corresponding to two or more cross sections set on two or more different blood vessels.

[0119] The hemodynamic comparison process is a process for acquiring characteristics between two or more pieces of hemodynamic information. For example, the hemodynamic comparison process acquires at least one of commonalities, similarities, and differences between two or more pieces of hemodynamic information. The hemodynamic comparison process includes, for example, any process for detecting, extracting, deriving, or estimating such characteristics. The hemodynamic comparison process may be, for example, an information comparison method using a machine learning algorithm (machine learning model) or an information comparison method using a non-machine learning algorithm. The items compared in the hemodynamic comparison process may be any. The comparison items may include, for example, the magnitude, amount of change, rate of change, maximum value, minimum value, average value, and variance of the values ​​of the hemodynamic parameters. More generally, the comparison items may include any type of absolute quantity, any type of relative quantity, any type of statistical quantity, and the like. Some non-limiting examples of the hemodynamic comparison process will be described later.

[0120] The hemodynamic information comparison unit 1034 is realized by cooperation between hardware including a circuit and hemodynamic comparison software. The ophthalmologic apparatus 1 according to a non-limiting embodiment can realize the function of the hemodynamic information comparison unit 1034 using the data processing unit 230.

[0121] In this embodiment, OCT blood flow measurement is applied to multiple locations on the fundus oculi Ef, and hemodynamic information at each location is generated. The hemodynamic information includes blood flow velocity. In the process of generating the blood flow velocity, Doppler angle and phase difference information are generated. The cardiac waveform generation unit 1035 is configured to generate a cardiac waveform based on the Doppler angle and phase difference information.

[0122] The cardiac waveform generated by the cardiac waveform generating unit 1035 may be any type of waveform (graph) showing the state of the heartbeat. For example, the cardiac waveform may be a waveform representing a change in blood flow velocity over time, a waveform representing a change in blood flow volume over time, or a waveform representing a change in another hemodynamic parameter over time. The cardiac waveform is generated by plotting the values ​​of the hemodynamic parameter at each time point.

[0123] 8 shows an example of a heartbeat waveform. The heartbeat waveform in this example is defined by the horizontal axis representing time t and the vertical axis representing blood flow velocity v, and expresses changes in blood flow dynamics resulting from heartbeats as changes in blood flow velocity v that depend on time t.

[0124] The cardiac waveform generating unit 1035 is realized by cooperation between hardware including a circuit and cardiac waveform generating software. The ophthalmologic apparatus 1 according to a non-limiting embodiment can realize the function of the cardiac waveform generating unit 1035 using the data processing unit 230.

[0125] In this embodiment, OCT blood flow measurement is applied to multiple locations on the fundus Ef, and hemodynamic information at each location is generated, generating a heartbeat waveform at each location. The heartbeat waveform comparison unit 1036 compares two or more heartbeat waveforms corresponding to two or more of the multiple locations. The information generated by this heartbeat waveform comparison process is referred to as heartbeat waveform comparison information. Any combination of the two or more heartbeat waveforms to be compared may be used. For example, the heartbeat waveform comparison information may be generated by comparing two or more heartbeat waveforms corresponding to two or more cross sections set on the same blood vessel, or the heartbeat waveform comparison information may be generated by comparing two or more heartbeat waveforms corresponding to two or more cross sections set on two or more different blood vessels.

[0126] The cardiac waveform comparison process is a process for acquiring characteristics between two or more cardiac waveforms. For example, the cardiac waveform comparison process acquires at least one of commonalities, similarities, and differences between two or more cardiac waveforms. The cardiac waveform comparison process includes, for example, any process for detecting, extracting, deriving, or estimating such characteristics. The cardiac waveform comparison process may be, for example, an information comparison method using a machine learning algorithm (machine learning model) or an information comparison method using a non-machine learning algorithm. The comparison items in the cardiac waveform comparison process may be any. The comparison items may include, for example, the magnitude, amount of change, rate of change, maximum value, minimum value, average value, and variance of the values ​​of parameters (cardiac waveform parameters) represented by the cardiac waveforms. More generally, the comparison items may include any type of absolute quantity, any type of relative quantity, any type of statistical quantity, and the like. Some non-limiting examples of the cardiac waveform comparison process will be described below.

[0127] The heartbeat waveform comparison unit 1036 is realized by cooperation between hardware including a circuit and heartbeat waveform comparison software. The ophthalmologic apparatus 1 of a non-limiting embodiment can realize the function of the heartbeat waveform comparison unit 1036 using the data processing unit 230.

[0128] Several operation examples of the ophthalmologic apparatus 1000 will be described. The processing contents of the steps in each operation example are not limited and may be modified as desired. Furthermore, the order of the steps in each operation example is not limited and may be modified as desired.

[0129] 9 shows an example of the operation of the ophthalmic apparatus 1000. In this example, hemodynamic measurements are applied in parallel to multiple locations in a single blood vessel.

[0130] First, in step S1, the ophthalmic device 1000 or the user designates one blood vessel of interest from the vascular network of the fundus oculi Ef. Furthermore, the ophthalmic device 1000 or the user designates multiple cross sections (multiple cross sections of interest) for this blood vessel of interest.

[0131] The designation of a vessel of interest is performed based on predetermined criteria (selection criteria) for selecting the vessel of interest from among the numerous blood vessels in the fundus oculi Ef. An example of an index used as a selection criterion is the direction of the vessel (Doppler angle). Typically, the preferred range of the Doppler angle is set to 77 degrees to 83 degrees, with the optimal value set to 80 degrees. Other indices include the type of vessel (e.g., artery, vein), size (vessel diameter), tortuosity, and position (e.g., position relative to the optic disc). The ophthalmic apparatus 1000 (data processing unit 1030) can select a vessel region of interest by considering two or more indices in stages or in parallel. Multiple cross sections of interest can be designated based on similar criteria. A previously acquired OCT image and / or a frontal fundus image is used to designate the vessel of interest and multiple cross sections of interest. The process of designating the vessel of interest may be either or both of a processing step using a machine learning algorithm (machine learning model) and a processing step using a non-machine learning algorithm. Furthermore, the process of specifying a plurality of cross sections of interest may be either or both of a processing step using a machine learning algorithm (machine learning model) and a processing step using a non-machine learning algorithm.

[0132] The multiple cross sections of interest specified in step S1 are arranged along the axial direction of the blood vessel of interest. That is, each cross section of interest is a transverse cross section of the blood vessel of interest. The spacing between the multiple cross sections of interest (i.e., the distance between adjacent cross sections) may be constant or variable. Furthermore, the multiple cross sections of interest may be densely arranged. More specifically, the spacing between the multiple cross sections of interest may be set to be smaller than a predetermined distance. This predetermined distance may be determined depending on the content of subsequent processing, the purpose of the generated information, and the like. For example, the predetermined distance is set to a value necessary or appropriate for expressing positional changes in the morphology of the blood vessel of interest (e.g., changes in blood vessel diameter, curvature, position, etc.) with a resolution appropriate for the purpose. If the purpose is to grasp the morphology of the blood vessel of interest in detail, the predetermined distance is set to a relatively small value. If the purpose is to grasp the general morphology of the blood vessel of interest, the predetermined distance is set to a relatively large value.

[0133] In step S2, the scan unit 1010 applies OCT scans to the multiple cross sections of interest specified in step S1 in a cyclical manner under the control of the scan control unit 1020. In other words, the scan unit 1010 repeatedly applies OCT scans to the multiple cross sections of interest in a predetermined order.

[0134] The order in which OCT scans are applied to multiple cross sections of interest (scan order) may be determined by the ophthalmic apparatus 1000 (scan control unit 1020 and / or data processing unit 1030) or may be set by the user.

[0135] The time or number of times to perform OCT scans on multiple slices of interest may be set in advance. In some embodiments, the time or number of times to perform OCT scans on multiple slices of interest can be determined so that the scan period for each slice of interest (the period from when the first scan is applied to when the last scan is applied) is longer than a standard cardiac cycle (e.g., 2 seconds). This makes it possible to determine the hemodynamics of blood flow in all cardiac phases for each slice of interest. In addition, in some embodiments, the time or number of times to perform OCT scans on multiple slices of interest can be determined by referring to data from a biosignal monitor, such as an electrocardiograph, applied to the subject.

[0136] 10A shows a non-limiting example of a cyclic scan mode (cyclic scan sequence, cyclic scan protocol) applied to the fundus Ef in this example. Reference numeral 1100 denotes the optic disc of the fundus Ef. Reference numeral 1110 denotes a blood vessel (vessel of interest) extending from the optic disc 1100. The cyclic scan mode in this example performs sequential and repetitive scans of N cross sections of interest specified for the blood vessel of interest 1110.

[0137] Sequential scans for N cross sections of interest are referred to as the first to Nth scans 1111(1) to 1111(N), respectively. Generally, a scan for the nth cross section is referred to as the nth scan 1111(n). Here, n is an integer greater than or equal to 1 and less than or equal to N. The number N of cross sections of interest is an integer greater than or equal to 2, and typically an integer greater than or equal to 3. The number N of cross sections of interest may be determined, for example, by the width of the range to which measurement is applied and the spacing between the cross sections of interest described above. Reference numeral 1112 denotes the region to which the N scans 1111(1) to 1111(N) are applied.

[0138] FIG. 10B shows a non-limiting example of a scan sequence in the cyclic scan mode of this example. N scans 1111(1) to 1111(N) correspond to a cycle of scans for N planes of interest. The cyclic scan mode of this example repeatedly executes this cycle of scans. That is, as shown in FIG. 10B , the cyclic scan mode of this example executes a cycle of scans consisting of a B scan for the first plane of interest, a B scan for the second plane of interest, ..., a B scan for the nth plane of interest, a B scan for the (n+1)th plane of interest, ..., and a B scan for the Nth plane of interest (N B scans), followed immediately by a B scan for the first plane of interest and executing the cycle of scans again. The cycle of scans continues until the aforementioned time or number of scans is reached. This allows hemodynamic measurements to be performed in parallel at multiple locations in the blood vessel of interest 1110. In other words, hemodynamic measurements to be performed at multiple locations in the blood vessel of interest 1110 are executed as a series of scan sequences (a single scan protocol).

[0139] FIG. 10C shows a non-limiting example of data collection in the cyclic scan mode of this example. The scan sequence of the cyclic scan mode shown in FIG. 10C executes a cycle of scans consisting of N scans 1111(1) to 1111(N) performed M times. The number of iterations, M, is set as described above. The data set collected by the first cycle of scans (first cycle of scans) is denoted by the symbol D1. Generally, the data set collected by the mth cycle of scans is denoted by the symbol Dm, where m is an integer greater than or equal to 1 and less than or equal to M. The data set Dm collected by the mth cycle of scans includes N pieces of data D(1,m) to D(N,m) collected by the N scans 1111(1) to 1111(N) that make up the mth cycle of scans.

[0140] In step S3, the data processing unit 1030 generates a plurality of pieces of hemodynamic information corresponding to the plurality of cross sections based on data collected from the plurality of cross sections of the blood vessel of interest 1110 of the fundus Ef in the cyclic OCT scan in step S2. Some non-limiting examples of the processing performed in step S3 are described below.

[0141] FIG. 10D shows a non-limiting example of processing performed on datasets collected by the cyclic scan mode of this example. The data processing unit 1030 classifies the data groups collected by the cyclic OCT scans in step S2 into multiple datasets corresponding to multiple cross sections, respectively. In the example shown in FIGS. 10C and 10D , the data processing unit 1030 classifies data groups D(1,1) to D(N,M) included in M ​​datasets D1 to DM shown in FIG. 10C into N datasets D(1) to D(N) shown in FIG. 10D . Each dataset D(n) includes M pieces of data D(n,1) to D(n,M) collected by M scans 1111(n) on the nth cross section. Each data D(n,m) is B-scan data collected by the mth scan 1111(n) on the nth cross section. This forms N datasets D(1) to D(N), corresponding to the first to Nth cross sections, respectively.

[0142] The image generator 1031 generates intensity images (B-scan images) based on each data D(n, m) included in each data set D(n). As a result, M intensity images (intensity image sets) are generated for each of the N cross sections specified in step S1. That is, N intensity image sets are generated.

[0143] Furthermore, the image generator 1031 generates phase difference information based on the M pieces of data D(n,1) to D(n,M) included in each data set D(n). This generates N pieces of phase difference information corresponding to the N cross sections specified in step S1. The image generator 1031 may generate a phase image.

[0144] The hemodynamic information generating unit 1032 calculates the Doppler angle at each of the N cross sections specified in step S1 based on the N intensity image sets and / or N phase images generated by the image generating unit 1031. The Doppler angle at the nth cross section is the magnitude of the angle between the OCT measurement light LS and the blood vessel of interest 1110 in the OCT blood flow measurement for the nth cross section.

[0145] In a non-limiting example of a process for calculating the Doppler angle at the nth cross section (n is an integer greater than or equal to 2 and less than or equal to N-1), the hemodynamic information generator 1032 performs the following steps: identifying a vascular region corresponding to the blood vessel of interest 1110 from an image (magnitude image and / or phase image) of the n-1th cross section; identifying a vascular region corresponding to the blood vessel of interest 1110 from an image (magnitude image and / or phase image) of the n+1th cross section; identifying the central position (vascular center) of the vascular region in the n-1th cross section; identifying the central position (vascular center) of the vascular region in the n+1th cross section; and calculating the Doppler angle at the nth cross section based on the vascular center in the n-1th cross section and the vascular center in the n+1th cross section. The Doppler angle at the first cross section can be determined, for example, by applying a similar calculation to the image of the first cross section and the image of the second cross section. The Doppler angle at the Nth cross section can also be determined, for example, by applying a similar calculation to the image of the N-1th cross section and the image of the Nth cross section.

[0146] In some embodiments, the hemodynamic information generating unit 1032 may calculate the Doppler angle at the nth cross section by a series of processes described below. First, the hemodynamic information generating unit 1032 generates a composite image by combining intensity image sets corresponding to the (n-1)th cross section, and then generates a composite image by combining intensity image sets corresponding to the (n+1)th cross section. Image combination may be an arithmetic average. The hemodynamic information generating unit 1032 analyzes each of the generated composite images to identify the vascular position of the blood vessel of interest 1110. The vascular position is expressed, for example, as the vascular center position. This allows the vascular position corresponding to the (n-1)th cross section and the vascular position corresponding to the (n+1)th cross section to be obtained. The hemodynamic information generating unit 1032 calculates the Doppler angle at the nth cross section based on these two vascular positions. According to this example, a composite image whose quality has been improved by image combination is used, thereby improving the accuracy and precision of the Doppler angle calculation. For example, by using an averaged image in which random noise such as speckle noise has been reduced, the accuracy and precision of Doppler angle calculations can be improved.

[0147] The hemodynamic information generating unit 1032 generates hemodynamic information indicating the hemodynamics of the blood vessel 1110 of interest in the nth cross section based on the Doppler angle of the blood vessel 1110 in the nth cross section and the phase difference information in the nth cross section. The hemodynamic information generated based on the Doppler angle and the phase difference information is, for example, blood velocity. When generating other types of hemodynamic information, please refer to the generation method described above.

[0148] This operation example considers the case described above where the multiple cross sections of interest specified in step S1 are a series of cross sections arranged along the axial direction of the blood vessel of interest at intervals smaller than a predetermined distance. Step S3 generates a series of hemodynamic information corresponding to this series of cross sections. In the example of Figures 10A to 10D, N pieces of hemodynamic information corresponding to the N cross sections of interest specified for the blood vessel of interest 1110 are generated.

[0149] In step S4, the map information generator 1033 generates hemodynamic map information indicating the distribution of the N pieces of hemodynamic information generated in step S3. This hemodynamic map information is information that expresses the correspondence between the position information of the N pieces of cross sections of interest and the N pieces of hemodynamic information.

[0150] FIG. 11 shows a non-limiting example of hemodynamic map information. Reference numeral 1120 denotes a blood vessel of interest 1110. Reference numeral 1121(1) denotes image information (position information) indicating the position of the first cross-section of interest to which the first scan 1111(1) was applied. Reference numeral 1131(1) denotes hemodynamic information (blood flow velocity value v(1)) acquired for the first cross-section of interest to which the first scan 1111(1) was applied. In this manner, the hemodynamic map information of FIG. 11 presents position information 1121(1) of the first cross-section of interest and hemodynamic information 1131(1) corresponding to the first cross-section of interest in a mutually associated manner. Similarly, the hemodynamic map information of FIG. 11 presents position information 1121(n) of the nth cross-section of interest and hemodynamic information 1131(n) corresponding to the nth cross-section of interest in a mutually associated manner. Here, n is an integer greater than or equal to 1 and less than or equal to N.

[0151] The hemodynamics map information makes it possible to present the distribution state of hemodynamics measured at multiple locations on the fundus Ef. By displaying the hemodynamics map information, the user can visually grasp the distribution state of hemodynamics.

[0152] In step S5, the map information generator 1033 generates vascular morphology map information representing the morphology of the blood vessel of interest. The vascular morphology map information is generated based on the intensity image of each cross section of interest generated in step S3. The intensity image of each cross section of interest may be a single B-scan image representing the cross section of interest, or may be a composite B-scan image generated by combining (e.g., averaging) multiple B-scan images representing the cross section of interest.

[0153] Fig. 12 shows a non-limiting example of vascular morphology map information. The region indicated by the reference numeral 1112 is the region 1112 shown in Fig. 10A. The region 1112 is a region in the fundus Ef to which N scans 1111(1) to 1111(N) are applied. The vascular morphology map information in Fig. 12 presents image information 1140 that represents the morphology of a blood vessel of interest within the region 1112.

[0154] The vascular morphology map information makes it possible to present the morphology and position of the blood vessels of interest in the region where hemodynamic measurement is applied. By displaying the vascular morphology map information, the user can visually grasp the state of the blood vessels of interest in the region where hemodynamic measurement is applied.

[0155] In step S6, the hemodynamic information comparison unit 1034 generates hemodynamic comparison information by comparing the multiple pieces of hemodynamic information generated for the multiple cross sections of interest in step S3. Visual information can be generated based on the hemodynamic comparison information. For example, positional changes and positional differences in the hemodynamic information can be represented using display parameters (color, brightness, density, etc.). This allows the user to visually grasp the positional changes and positional differences in the hemodynamic information. The hemodynamic information comparison unit 1034 may compare all of the hemodynamic information generated in step S3, or may compare two or more pieces of hemodynamic information selected from the hemodynamic information.

[0156] The hemodynamics comparison information makes it possible to provide changes and differences in hemodynamics information at multiple locations on the fundus Ef. By displaying the hemodynamics comparison information, the user can visually grasp the changes and differences in hemodynamics information at multiple locations on the fundus Ef.

[0157] In step S7, the cardiac waveform generation unit 1035 generates a cardiac waveform for each slice of interest based on the Doppler angle and phase difference information obtained for that slice of interest in step S3. This generates multiple cardiac waveforms corresponding to the multiple slices of interest. In the example shown in FIGS. 10A to 10D, N cardiac waveforms are generated corresponding to N slices of interest to which N scans 1111(1) to 1111(N) have been applied. The cardiac waveforms make it possible to present the state of the heartbeat.

[0158] In step S8, the heartbeat waveform comparison unit 1036 generates heartbeat waveform comparison information by comparing the multiple heartbeat waveforms generated in step S7. The heartbeat waveform comparison unit 1036 may compare all of the heartbeat waveforms generated in step S7, or may compare two or more heartbeat waveforms selected from the heartbeat waveforms.

[0159] The cardiac waveform comparison information makes it possible to provide changes and differences in cardiac waveforms (cardiac states) at multiple locations on the fundus Ef. By displaying the cardiac waveform comparison information, the user can visually grasp changes and differences in cardiac waveforms (cardiac states) at multiple locations on the fundus Ef.

[0160] In this example, hemodynamic measurements are performed in parallel at multiple locations on the fundus vascular network. In particular, this example measures multiple locations on a single blood vessel in parallel. This example improves the quality (e.g., at least one of accuracy, precision, reproducibility, and reliability) of the hemodynamic measurements on a single blood vessel. Furthermore, this example can provide new information by comparing measurement results from multiple locations on a single blood vessel.

[0161] FIG. 13 shows one operation example of the ophthalmic device 1000. In this operation example, hemodynamic measurements are applied in parallel to multiple locations in two blood vessels. Those skilled in the art will understand that the operation when hemodynamic measurements are applied in parallel to multiple locations in three or more blood vessels is similar to this operation example. Any of the features in the operation examples described above with reference to FIGS. 9 to 12 can be combined with this operation example. Furthermore, unless otherwise specified, this operation example provides the same actions and effects as the operation examples described above with reference to FIGS. 9 to 12.

[0162] First, in step S11, the ophthalmic device 1000 or the user specifies two or more blood vessels of interest from the vascular network of the fundus oculi Ef. In this example, two blood vessels of interest are specified. Furthermore, the ophthalmic device 1000 or the user specifies multiple cross sections (multiple cross sections of interest) for these two blood vessels of interest.

[0163] In step S12, the scanning unit 1010, under the control of the scan control unit 1020, applies OCT scans to the multiple cross sections of interest designated in step S11 in a cyclical manner.

[0164] 14A shows a non-limiting example of a cyclic scan mode applied to the fundus Ef in this example. Reference numeral 1200 denotes the optic disc of the fundus Ef. Reference numerals 1210 and 1220 denote two blood vessels of interest extending from the optic disc 1100 and specified in step S11. In this example, four cross sections of interest are specified for the blood vessel of interest 1210, and two cross sections of interest are specified for the blood vessel of interest 1220. The cyclic scan mode in this example performs sequential and repetitive scans of the six cross sections of interest specified for the two blood vessels of interest 1210 and 1220.

[0165] The four cross sections of interest that cross the blood vessel of interest 1210 are referred to as the first to fourth cross sections of interest or the first to fourth cross sections of interest. Scans for the first to fourth cross sections of interest are referred to as the first to fourth scans 1211(1) to 1211(4), respectively. Furthermore, the two cross sections of interest that cross the blood vessel of interest 1220 are referred to as the fifth and sixth cross sections of interest or the fifth and sixth cross sections of interest. Scans for the fifth and sixth cross sections of interest are referred to as the fifth and sixth scans 1221(1) and 1221(2), respectively. In this example, the cyclic scan mode sequentially applies scans to the six cross sections of interest set for the two blood vessels of interest 1210 and 1220, namely the first to sixth scans 1211(1), 1211(2), 1211(3), 1211(4), 1221(1), and 1221(2). In general, a scan for the nth cross section of interest is referred to as the nth scan 1211(n) or 1221(n), where n is an integer of 1 to 6 inclusive.

[0166] FIG. 14B shows a non-limiting example of a scan sequence in the cyclic scan mode of this example. Six scans 1211(1) to 1211(4), 1221(1), and 1221(2) correspond to a cycle of scans for six planes of interest. The cyclic scan mode of this example repeatedly executes this cycle of scans. That is, as shown in FIG. 14B , immediately after a cycle of scans consisting of B-scan 1211(1) for the first plane of interest, B-scan 1211(2) for the second plane of interest, ..., B-scan 1221(2) for the sixth plane of interest (six B-scans), the cyclic scan mode of this example returns to B-scan 1211(1) for the first plane of interest and executes the cycle of scans again. The cycle of scans continues until the aforementioned time or number of times is reached. This allows hemodynamic measurements to be performed in parallel at six locations, including four locations in the blood vessel of interest 1210 and two locations in the blood vessel of interest 1220. In other words, hemodynamic measurements at six locations in the two blood vessels of interest 1210 and 1220 are performed as a series of scan sequences (single scan protocol).

[0167] For the manner of data collection in the cyclic scan mode of this example, please refer to the example of FIG. 10C described above. In this example, it is assumed that one cycle of scanning is repeated M times. Using the notation in FIG. 10C mutatis mutandis, the data set collected by the mth cycle of scanning is denoted by the symbol Dm. The data set Dm collected by the mth cycle of scanning includes six pieces of data D(1,m) to D(6,m) collected by six scans 1211(1) to 1221(2) that make up the mth cycle of scanning.

[0168] In step S13, the data processing unit 1030 generates six pieces of hemodynamic information corresponding to the six sections of interest, based on the data collected from the six sections of interest of the two blood vessels 1210 and 1220 of the fundus Ef during the cyclic OCT scan in step S12.

[0169] The data processing unit 1030 classifies the data groups collected in the cyclic OCT scans in step S12 into six data sets corresponding to the six cross sections of interest, respectively. Similar to the examples shown in FIGS. 10C and 10D , the data processing unit 1030 classifies the data groups D(1,1) to D(6,M) included in M ​​data sets D1 to DM corresponding to M scan repetitions into six data sets D(1) to D(6), respectively corresponding to the six cross sections of interest (see the notation in FIG. 10D ). Each data set D(n) includes M pieces of data D(n,1) to D(n,M) collected by M scans 1211(n) or 1221(n) for the nth cross section of interest. Each data set D(n,m) is B-scan data collected by the mth scan 1211(n) or 1221(n) for the nth cross section of interest. This forms six data sets D(1) to D(6), corresponding to the first to sixth cross sections, respectively.

[0170] The image generator 1031 generates intensity images (B-scan images) based on each data D(n, m) included in each data set D(n), thereby generating M intensity images (intensity image sets) for each of the six cross sections of interest specified in step S11, and six intensity image sets are generated.

[0171] Furthermore, the image generating unit 1031 generates phase difference information based on the M pieces of data D(n,1) to D(n,M) included in each data set D(n). As a result, six pieces of phase difference information corresponding to the six cross sections of interest specified in step S11 are generated. The image generating unit 1031 may generate a phase image.

[0172] The hemodynamic information generation unit 1032 calculates the Doppler angle at each of the six cross sections of interest specified in step S1 based on the six intensity image sets and / or six phase images generated by the image generation unit 1031.

[0173] The hemodynamic information generation unit 1032 generates hemodynamic information indicating the hemodynamics within the blood vessel 1210 or 1220 of interest in the nth cross section of interest based on the Doppler angle of the blood vessel 1210 or 1220 of interest in the nth cross section of interest and the phase difference information in the nth cross section of interest.

[0174] If the multiple cross sections of interest specified for the same blood vessel of interest in step S11 are a series of cross sections arranged along the axial direction of the blood vessel of interest at intervals smaller than a predetermined distance, step S13 generates a series of hemodynamic information corresponding to this series of cross sections. In this example, four pieces of hemodynamic information are generated corresponding to the four cross sections of interest specified for the blood vessel of interest 1210, and two pieces of hemodynamic information are generated corresponding to the two cross sections of interest specified for the blood vessel of interest 1220.

[0175] In step S14, the map information generator 1033 generates hemodynamic map information indicating the distribution of the six pieces of hemodynamic information generated in step S13. This hemodynamic map information is information that expresses the correspondence between the position information of the six cross sections of interest and the six pieces of hemodynamic information. For the form of the hemodynamic map information in this example, please refer to FIG. 11 described above.

[0176] In step S15, the map information generator 1033 generates vascular morphology map information representing the morphology of the blood vessels of interest 1210 and / or 1220. The vascular morphology map information is generated based on the intensity image of each cross section of interest generated in step S13. The intensity image of each cross section of interest may be a single B-scan image representing the cross section of interest, or may be a composite B-scan image generated by combining (e.g., averaging) multiple B-scan images representing the cross section of interest. For the form of the vascular morphology map information in this example, please refer to FIG. 12 described above.

[0177] In step S16, the hemodynamic information comparison unit 1034 compares the six pieces of hemodynamic information generated for the six cross sections of interest in step S13 to generate hemodynamic comparison information. Visual information can be generated based on the hemodynamic comparison information. The hemodynamic information comparison unit 1034 may compare all of the hemodynamic information generated in step S13, or may compare two or more pieces of hemodynamic information selected from the hemodynamic information.

[0178] In step S17, the cardiac waveform generation unit 1035 generates a cardiac waveform for each cross section of interest based on the Doppler angle and phase difference information obtained for that cross section of interest in step S13. This generates six cardiac waveforms corresponding to the six cross sections of interest, respectively.

[0179] In step S18, the heartbeat waveform comparison unit 1036 generates heartbeat waveform comparison information by comparing the multiple heartbeat waveforms generated in step S17. The heartbeat waveform comparison unit 1036 may compare all of the heartbeat waveforms generated in step S17, or may compare two or more heartbeat waveforms selected from the heartbeat waveforms.

[0180] In this operation example, hemodynamic measurements are performed in parallel at multiple locations on the fundus vascular network. In particular, this operation example measures multiple locations on multiple blood vessels in parallel. According to this operation example, measurements on multiple blood vessels can be performed in a shorter time than conventional methods. This reduces the burden on the subject and improves examination throughput. Furthermore, this operation example reduces the effort required for measurement work and the processing load involved in measurement. In this operation example, when the parallel measurements on multiple locations on multiple blood vessels include parallel measurements on multiple locations on a single blood vessel of interest, the same actions and effects as those of the operation examples described above with reference to Figures 9 to 12 are also achieved.

[0181] 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.

[0182] Some embodiments are a method for controlling an ophthalmic apparatus having a scanning unit and a processor that performs OCT scans. The method causes the processor of the ophthalmic apparatus to perform scan control and hemodynamic information generation processing. The scan control controls the scanning unit to apply OCT scans cyclically to multiple cross sections of one or more blood vessels of interest in the fundus of the subject's eye to collect multiple data sets corresponding to the multiple cross sections. The hemodynamic information generation processing generates multiple pieces of hemodynamic information corresponding to the multiple cross sections based on the multiple data sets collected by the scanning unit under the scan control. With this method, the ophthalmic apparatus can perform, for example, the operation example shown in FIG. 9 and the operation example shown in FIG. 13 described above.

[0183] It is possible to create a program that causes an ophthalmic device including a computer to execute this method. It is also possible to create a computer-readable non-transitory recording medium that records such a program. This non-transitory recording medium may be in any form, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory.

[0184] Any of the items described in this disclosure may be combined with the methods, programs, and recording media described herein.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [1] An ophthalmic device that performs optical coherence tomography blood flow measurement to measure blood flow dynamics in blood vessels at the fundus of the eye, comprising: a scanning unit that applies an optical coherence tomography scan to the fundus of the eye to collect data; a scanning control unit that controls the scanning unit; and a data processing unit that processes the data collected by the scanning unit, wherein the scanning control unit controls the scanning unit to apply an optical coherence tomography scan cyclically to multiple cross sections of one or more blood vessels of interest at the fundus of the eye to collect multiple data sets corresponding to the multiple cross sections, and the data processing unit generates multiple pieces of blood flow dynamics information corresponding to the multiple cross sections based on the multiple data sets.

[0189] [2] The ophthalmic device of claim 1, wherein the one or more blood vessels of interest include a first blood vessel of interest, the multiple cross sections include two or more cross sections of the first blood vessel of interest, and the data processing unit calculates a first Doppler angle in optical coherence tomography blood flow measurement for a first cross section of the two or more cross sections based on two or more data sets collected from the two or more cross sections, respectively, generates first phase difference information based on a first data set collected from the first cross section of the two or more data sets, and generates first blood flow dynamics information corresponding to the first cross section based on the first Doppler angle and the first phase difference information.

[0190] [3] The ophthalmologic device according to claim 2, wherein the data processing unit generates two or more intensity image sets corresponding to the two or more cross sections, respectively, based on the two or more data sets; combines each of the two or more intensity image sets to generate two or more composite images corresponding to the two or more cross sections, respectively; analyzes each of the two or more composite images to identify two or more vascular positions of the first blood vessel of interest corresponding to the two or more cross sections, respectively; and calculates the first Doppler angle based on the two or more vascular positions.

[0191] [4] The ophthalmologic apparatus according to claim 2 or 3, wherein the plurality of cross sections include a series of cross sections arranged along the axial direction of the first blood vessel of interest at intervals smaller than a predetermined distance, and the data processing unit generates a series of the first blood flow dynamics information corresponding to the series of cross sections, and generates first map information indicating the distribution of the series of first blood flow dynamics information corresponding to each of the series of cross sections.

[0192] [5] The ophthalmologic apparatus according to claim 2 or 3, wherein the plurality of cross sections include a series of cross sections arranged along the axial direction of the first blood vessel of interest at intervals smaller than a predetermined distance, and the data processing unit generates a series of intensity images corresponding to the series of cross sections based on a series of data sets collected respectively from the series of cross sections, and generates second map information representing the morphology of the first blood vessel of interest based on the series of intensity images.

[0193] [6] The ophthalmologic device of any of items 2 to 5 above, wherein the one or more blood vessels of interest are two or more blood vessels of interest including the first blood vessel of interest and a second blood vessel of interest, the multiple cross sections further include two or more cross sections of the second blood vessel of interest, and the data processing unit further calculates a second Doppler angle in optical coherence tomography blood flow measurement for a second cross section of the two or more cross sections of the second blood vessel of interest based on two or more data sets respectively collected from the two or more cross sections of the second blood vessel of interest, generates second phase difference information based on a second data set collected from the second cross section of the two or more data sets, and generates second blood flow dynamics information corresponding to the second cross section based on the second Doppler angle and the second phase difference information.

[0194] [7] The ophthalmologic apparatus according to the above item 6, wherein the data processing unit compares the first blood flow dynamics information with the second blood flow dynamics information to generate blood flow dynamics comparison information.

[0195] [8] The ophthalmologic device according to claim 6, wherein the data processing unit generates a first cardiac waveform corresponding to the first cross section based on the first Doppler angle and the first phase difference information, generates a second cardiac waveform corresponding to the second cross section based on the second Doppler angle and the second phase difference information, and compares the first cardiac waveform with the second cardiac waveform to generate cardiac waveform comparison information.

[0196] [9] A method for controlling an ophthalmologic apparatus having a scanning unit that performs optical coherence tomography scanning and a processor, the method causing the processor to perform the following: scan control for controlling the scanning unit to apply optical coherence tomography scanning cyclically to multiple cross sections of one or more blood vessels of interest in the fundus of the subject's eye to collect multiple data sets corresponding to the multiple cross sections; and hemodynamic information generation processing for generating multiple pieces of hemodynamic information corresponding to the multiple cross sections based on the multiple data sets.

[0197]

[10] A program for causing a computer to execute the method of 9 above.

[0198]

[11] A computer-readable non-transitory recording medium on which the program of 10 above is recorded.

[0199] 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.

[0200] 1000 Ophthalmic apparatus 1010 Scan unit 1020 Scan control unit 1030 Data processing unit

Claims

1. An ophthalmic device that performs optical coherence tomography blood flow measurement to measure blood flow dynamics in blood vessels at the fundus of the eye, comprising: a scanning unit that applies optical coherence tomography scanning to the fundus of the eye to collect data; a scanning control unit that controls the scanning unit; and a data processing unit that processes the data collected by the scanning unit, wherein the scanning control unit controls the scanning unit to apply optical coherence tomography scanning cyclically to multiple cross sections of one or more blood vessels of interest at the fundus of the eye to collect multiple data sets respectively corresponding to the multiple cross sections, and the data processing unit generates multiple pieces of blood flow dynamics information respectively corresponding to the multiple cross sections based on the multiple data sets.

2. The ophthalmic device of claim 1, wherein the one or more blood vessels of interest include a first blood vessel of interest, the multiple cross sections include two or more cross sections of the first blood vessel of interest, and the data processing unit calculates a first Doppler angle in optical coherence tomography blood flow measurement for a first cross section of the two or more cross sections based on two or more data sets collected from the two or more cross sections, respectively, generates first phase difference information based on a first data set collected from the first cross section of the two or more data sets, and generates first blood flow dynamics information corresponding to the first cross section based on the first Doppler angle and the first phase difference information.

3. The ophthalmic device of claim 2, wherein the data processing unit: generates two or more intensity image sets corresponding to the two or more cross sections, respectively, based on the two or more data sets; combines each of the two or more intensity image sets to generate two or more composite images corresponding to the two or more cross sections, respectively; analyzes each of the two or more composite images to identify two or more vascular positions of the first blood vessel of interest corresponding to the two or more cross sections, respectively; and calculates the first Doppler angle based on the two or more vascular positions.

4. An ophthalmologic apparatus according to claim 2 or 3, wherein the plurality of cross sections include a series of cross sections arranged along the axial direction of the first blood vessel of interest at intervals smaller than a predetermined distance, and wherein the data processing unit generates a series of the first blood flow dynamics information corresponding to the series of cross sections, and generates first map information indicating the distribution of the series of first blood flow dynamics information corresponding to each of the series of cross sections.

5. The ophthalmologic apparatus according to claim 2 or 3, wherein the plurality of cross sections include a series of cross sections arranged along the axial direction of the first blood vessel of interest at intervals smaller than a predetermined distance, and the data processing unit generates a series of intensity images corresponding to the series of cross sections based on a series of data sets collected respectively from the series of cross sections, and generates second map information representing the morphology of the first blood vessel of interest based on the series of intensity images.

6. The ophthalmic device according to any one of claims 2 to 5, wherein the one or more blood vessels of interest are two or more blood vessels of interest including the first blood vessel of interest and a second blood vessel of interest, the multiple cross sections further include two or more cross sections of the second blood vessel of interest, and the data processing unit further calculates a second Doppler angle in optical coherence tomography blood flow measurement for a second cross section of the two or more cross sections of the second blood vessel of interest based on two or more data sets respectively collected from the two or more cross sections of the second blood vessel of interest, generates second phase difference information based on a second data set collected from the second cross section of the two or more data sets, and generates second blood flow dynamics information corresponding to the second cross section based on the second Doppler angle and the second phase difference information.

7. The ophthalmologic apparatus according to claim 6, wherein the data processing unit compares the first blood flow dynamics information with the second blood flow dynamics information to generate blood flow dynamics comparison information.

8. The ophthalmologic device of claim 6, wherein the data processing unit generates a first cardiac waveform corresponding to the first cross section based on the first Doppler angle and the first phase difference information, generates a second cardiac waveform corresponding to the second cross section based on the second Doppler angle and the second phase difference information, and compares the first cardiac waveform with the second cardiac waveform to generate cardiac waveform comparison information.

9. A method for controlling an ophthalmic apparatus having a scanning unit that performs optical coherence tomography scanning and a processor, the method comprising causing the processor to perform scan control that controls the scanning unit to apply optical coherence tomography scanning cyclically to multiple cross sections of one or more blood vessels of interest in the fundus of the subject's eye to collect multiple data sets corresponding to the multiple cross sections, and hemodynamic information generation processing that generates multiple pieces of hemodynamic information corresponding to the multiple cross sections based on the multiple data sets.

10. A program for causing a computer to execute the method of claim 9.

11. A computer-readable non-transitory recording medium on which the program of claim 10 is recorded.

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