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EP-4738255-A2 - QUALITY MAPS FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY

EP4738255A2EP 4738255 A2EP4738255 A2EP 4738255A2EP-4738255-A2

Abstract

A system, method, and/or device for determinizing a quality measure of OCT structural data and/or OCTA functional data uses a machine learning model trained to provide a single overall quality measure, or a quality map distribution for the OCT/OCTA data based on the generation of multiple features maps extracted from one or more slab views of the OCT/OCTA data. The extracted feature maps may be different texture-type maps, and the machine model is trained to determine the quality measure based on the texture maps.

Inventors

  • DE SISTERNES, Luis

Assignees

  • Carl Zeiss Meditec, Inc.
  • Carl Zeiss Meditec AG

Dates

Publication Date
20260506
Application Date
20211129

Claims (15)

  1. A method for generating a quality measure of optical coherence tomography (OCT) data, comprising: Acquiring (45) a volume of OCT data; defining one or more slab views (41) from the volume OCT data; generating a plurality of feature maps (43) from each slab view; determining the quality measure (47) based on image properties of the plurality of feature maps; displaying the quality measure or storing the quality measure for further processing; wherein: acquiring the volume of OCT data includes: a) collecting a plurality of different OCT volume samples of the same retinal tissue region; b) applying to each of the OCT volume samples the steps of defining one or more slab views, generating the plurality of feature maps, and determining the quality measure based on the plurality of feature maps, whereby a plurality of 2D quality map samples corresponding to the plurality of different OCT volume samples are defined; and the method further comprises: defining composite OCT data based on the higher quality regions of the plurality of different OCT volume samples based on their respective 2D quality map samples.
  2. The method of claim 1, wherein a plurality of said slab views are defined.
  3. The method of claim 1 or 2, wherein each slab view is a frontal, planar view of a sub-volume of the volume of OCT data.
  4. The method of claim 1, 2, or 3, wherein determining the quality measure includes submitting the plurality of feature maps to a machine model trained using a plurality of pre-graded OCT data volume samples, one or more training slab view defined per OCT data volume sample, and a plurality of training feature maps generated from each training slab view.
  5. The method of claim 4, wherein the machine model is a deep learning model or a neural network model.
  6. The method of any of claims 1 to 5, wherein the image properties of the feature maps include image texture features or Haralick features.
  7. The method of any of claims 1 to 6, wherein the OCT data is OCT angiography data.
  8. The method of any of claims 1 to 7, wherein the quality measure is a two-dimensional (2D) quality map identifying a quality measure for different regions of its corresponding slab view.
  9. The method of claims 1 or 8, wherein: determining the quality measure includes submitting the plurality of feature maps to a machine model trained to determine the quality maps based on the plurality of feature maps; and the machine model is further trained to identify one or more causes of a quality region within the quality map having a lower quality measure than a predefined threshold, and identify one or more corrective actions to improve the lower quality measure in a subsequent OCT acquisition.
  10. The method of claim 9 wherein the one or more causes are selected from a predefined list of error-sources including one or more of incorrect focusing, opacities, illumination below a predefined threshold, light penetration less than a predefined threshold, tracking or motion artifacts, and noise above a predefined threshold.
  11. The method of claims 9 or 10, wherein the one or more corrective actions include a focus adjustment, a recommendation for pupil dilation, identifying an alternate imaging angle, or identifying possible cause of loss of eye tracking.
  12. The method of any of claims 9 to 11, wherein the corrective action is transferred to an automated sub-system that automatically implements the corrective action prior to the subsequent acquisition.
  13. The method of any of claims 8 to 12, further including defining an overall quality score for the acquisition based at least in part on the average of the individual quality measure distribution of the quality map.
  14. The method of any of claims 8 to 13, wherein the acquisition is an OCTA acquisition defined from a plurality of OCT scans of the same region of the retina, and the method further includes defining an overall quality score for the OCTA acquisition based at least in part on the average of the individual quality measure distribution of the quality maps of the plurality of OCT scans from which the OCTA acquisition is defined.
  15. The method of any of claims 8 to 14, further including, identifying a target region within the acquisition, and designating the entire acquisition as good or bad based on the quality map measures corresponding to the target region.

Description

FIELD OF INVENTION The present invention is generally directed to optical coherence tomography (OCT) systems. More specifically, it is directed to methods of determining quality measures of OCT and OCT angiography scans, and generating quality maps. BACKGROUND Optical coherence tomography (OCT) is a non-invasive imaging technique that uses light waves to penetrate tissue and produce image information at different depths within the tissue. Basically, an OCT system is an interferometric imaging system that determines a scattering profile of a sample along an OCT beam by detecting the interference of light reflected from the sample and a reference beam to create a three-dimensional (3D) representation of the sample. Each scattering profile in the depth direction (e.g., z-axis or axial direction) may be reconstructed individually into an axial scan, or A-scan. Cross-sectional slice images (e.g., two-dimensional (2D) bisecting scans, or B-scans) and volume images (e.g., three-dimensional (3D) cube scans, or C-scans) may be built up from multiple A-scans acquired as the OCT beam is scanned/moved through a set of transverse (e.g., x-axis and y-axis) locations on the sample. An OCT system also permits construction of a planar, frontal view (e.g., en face) image of a select portion of a tissue volume (e.g., a target tissue slab view (sub-volume) or target tissue layer(s), such as the retina of an eye). Within the ophthalmic field, OCT systems were initially developed to provide structural data, such as cross-section images of retinal tissue, but today may provide functional information as well, such as flow information. Whereas OCT structural data permits one to view the distinctive tissue layers of the retina, OCT angiography (OCTA) expands the functionality of an OCT system to also identify (e.g., render in image format) the presence, or lack, of blood flow in retinal tissue. For example, OCTA may identify blood flow by identifying differences over time (e.g., contrast differences) in multiple OCT scans of the same retinal region, and designating differences that meet predefined criteria as blood flow. Although data produced by an OCT system (e.g., OCT data) could include both OCT structural data and OCT flow data, depending on the functionality of the OCT system, for ease of discussion, unless otherwise stated or understood from context, OCT structural data may herein be termed "OCT data" and OCT angiography (or flow) data may herein be termed "OCTA data". Thus, OCT may be said to provide structural information, whereas OCTA provides flow (e.g., functional) information. However, since both OCT data and OCTA data may be extracted from the same one or more OCT scan, the term "OCT scan" may be understood to include an OCT structural scan (e.g., OCT acquisition) and/or an OCT functional scan (e.g., OCTA acquisition), unless otherwise stated. A more in-depth discussion of OCT and OCTA is provided below. OCTA provides valuable diagnostic information not found in structural OCT, but OCTA scans may suffer from acquisition issues that can make their quality sub-optimal. Prior attempts to quantify OCT scan quality focus on OCT structural data and generally depend upon a signal strength measurement, such as described in "A new quality assessment parameter for optical coherence tomography," by D.M. Stein et al., Br J. Ophthalmol, 2006, 90:186-190. Although signal strength measurements for assessing OCT structural data have found utility, such approaches are of limited use in OCTA data due to the quality of the derived flow information being dependent upon on many other factors that are not included in such quantifications. Consequently, OCTA scan quality is often determined subjectively by observers in order to determine whether a particular OCTA acquisition (e.g., OCTA scan) can be used for diagnosis or included in a broad study. Examples of this approach are found in: "Determinants of Quantitative Optical Coherence Tomography Angiography Metrics in Patients with Diabetes," by Tang FY et al., Scientific Reports, 2018;8:7314; "Swept Source Optical Coherence Tomography Angiography for Contact Lens-Related Corneal Vascularization", by Ang M et al., Journal of Ophthalmology, 2016, 2016, 9685297; and "Impact of eye-tracking technology on OCT-angiography imaging quality in age-related macular degeneration," by Lauermann et al., Graefes Arch Clin Exp Ophthalmol, 2017, 255: 1535. These approaches, however, are extremely subjective and time consuming. In addition, subjective quality is often assessed after a patient examination during a-posteriori scan review when a patient has left a clinic, making it impossible to try and acquire an additional scan of better quality to replace low-quality data and causing loss of data or uncertain diagnosis. Even with operators that can actively judge the quality of OCTA scans during acquisition while the patient is still in the clinic, there is no guidance currently available with a quantitative