# US 11,029,141 B2 · Anticipatory depth of field adjustment for optical coherence tomography

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- Number: US 11,029,141 B2
- Document ID: US11029141B2
- Status: Granted
- Kind: B2
- Application number: 16/460,628
- Filed: 2019-07-02
- Published: 2020-01-02
- Granted: 2021-06-08
- Earliest claimed: 2017-06-12
- Assignees: Sightline Innovation Inc.
- Applicants: Sightline Innovation Inc.
- Inventors: Wallace Trenholm; Lorenzo Pons
- Web page: https://patentgrep.com/patent/US11029141B2

## CPC

- G01B 9/02035: {Shaping the focal point, e.g. elongated focus}
- A61B 5/00: Measuring for diagnostic purposes (radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00); Identification of persons
- A61B 5/0066: {Optical coherence imaging}
- A61B 5/6844: {Monitoring or controlling distance between sensor and tissue}
- G01B 9/02063: {by particular alignment of focus position, e.g. dynamic focussing in optical coherence tomography}
- G01B 9/02091: Tomographic interferometers, e.g. based on optical coherence
- G01N 21/4795: {spatially resolved investigating of object in scattering medium (in vivo A61B)}
- G01N 21/8806: {Specially adapted optical and illumination features}
- G01N 21/95: characterised by the material or shape of the object to be examined (G01N21/89 - G01N21/91, G01N21/94 take precedence)
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- G06N 3/047: Probabilistic or stochastic networks
- G01B: MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A system and method for surface inspection of an object using optical coherence tomography (OCT) with anticipatory depth of field adjustment is provided. The method includes determining a present working distance and one or more forward working distances; determining a present depth of field in which the surface of the object is in focus at the location of the present working distance and at as many of the consecutive forward surface locations as determined possible; changing to the present depth of field; performing an A-scan of the object; moving the object such that the scanner head is directed at each of the consecutive forward surface locations determined to be in the present depth of field; and performing an A-scan at each of the consecutive forward surface locations determined to be in the present depth of field.

## Claims

### Claim 1

A method of surface inspection of an object using optical coherence tomography (OCT), the OCT having an associated tunable depth of field comprising a distance range, the method comprising:
determining a present depth of field such that a series of multiple forward surface locations on the object are located within the associated distance range, wherein the multiple forward surface locations are sequentially located opposite a direction of travel of the object;
tuning the tunable depth of field to the present depth of field; and
performing a scan of the object at each surface location in the series of surface locations.

### Claim 2 (depends on 1)

The method of claim 1, wherein the scan of the object comprises an A-scan.

### Claim 3 (depends on 2)

The method of claim 2, using the A-scans data, the method further comprising detecting a feature on a surface or subsurface of the object using a neural network trained using a training set, the training set comprising A-scans data labelled with the feature.

### Claim 4 (depends on 1)

The method of claim 1, wherein the scan for each of the surface locations that are sequentially located opposite the direction of travel of the object are combined to form a B-scan.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
determining a subsequent depth of field having a subsequent series of multiple forward surface locations on the object located within the associated distance range;
tuning the tunable depth of field to the subsequent depth of field; and
performing a scan of the object at each surface location in the subsequent series of surface locations.

### Claim 6 (depends on 5)

The method of claim 5, wherein determining the present depth of field and the subsequent depth of field each comprise measuring a distance between the scanner head and each surface location of the object.

### Claim 7 (depends on 5)

The method of claim 5, wherein determining the present depth of field and the subsequent depth of field each comprise determining a distance between the scanner head and each surface location of the object using surface geometry of the object.

### Claim 8

A system for surface inspection of an object using optical coherence tomography (OCT), the OCT having a tunable depth of field comprising a distance range, the system for surface inspection comprising:
a depth-of-field module to determine a present depth of field such that a series of multiple forward surface locations on the object are located within a distance range associated with the present depth of field, wherein the multiple forward surface locations are sequentially located opposite a direction of travel of the object;
a depth-of-field adjusting mechanism to tune the tunable depth of field to the present depth of field; and
a scanner head to perform a scan of the object at each surface location in the series of surface locations by directing optical beams towards the object and detecting optical beams returning from the object.

### Claim 9 (depends on 8)

The system of claim 8, further comprising an object translator to move the object along a direction of travel.

### Claim 10 (depends on 8)

The system of claim 8, wherein the scan of the object comprises an A-scan.

### Claim 11 (depends on 10)

The system of claim 10, further comprising a computing module to, using the A-scan data, detect a feature on a surface or subsurface of the object using a neural network trained using a training set, the training set comprising A-scans data labelled with the feature.

### Claim 12 (depends on 8)

The system of claim 8, wherein the scan for each of the surface locations that are sequentially located opposite the direction of travel of the object are combined to form a B-scan.

### Claim 13 (depends on 8)

The system of claim 8, wherein the depth-of-field module further determines a subsequent depth of field having a subsequent series of multiple successive surface locations on the object located within a distance range associated with the subsequent depth of field, wherein the depth-of-field adjusting mechanism further tunes the tunable depth of field to the subsequent depth of field, and wherein the scanner head further performs a scan of the object at each surface location in the subsequent series of surface locations by directing optical beams towards the object and detecting optical beams returning from the object.

### Claim 14 (depends on 8)

The system of claim 8, wherein the depth-of-field adjusting mechanism comprises a focus-tuneable lens.

### Claim 15 (depends on 14)

The system of claim 14, wherein the focus-tuneable lens comprises a liquid lens.

### Claim 16 (depends on 14)

The system of claim 14, wherein the focus-tuneable lens comprises two or more lenses that are mechanically translated relative to each other.