Surgical microscope integrated MHz SS-OCT with live volumetric visualization

Britten, Anja; Matten, Philipp; Weiß, Jakob; Niederleithner, Michael; Roodaki, Hessam; Sorg, Benjamin; Hecker‐Denschlag, Nancy; Drexler, Wolfgang; Leitgeb, Rainer A.; Schmoll, Tilman

doi:10.1364/boe.477386

Cited by 24 publications

(19 citation statements)

References 25 publications

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“…DC-SC offers a promising method for scanning over large volumes like the middle ear, by making efficient use of available scanning bandwidth and enabling higher volume rates than is possible with sequential B-mode scanning. The imaging technique is computationally efficient and on standard hardware can run many times faster than data is acquired in even the fastest reported 4D OCT systems [15].…”

Section: Discussionmentioning

confidence: 99%

“…In addition to clinical diagnostics, there may be a role for 4D ME-OCT in surgical guidance including robotic surgeries [53][54][55]. Surgical guidance applications have been the primary motivator of recent developments in ophthalmic 4D OCT imaging technology [15].…”

Section: Discussionmentioning

confidence: 99%

“…Volumetric OCT imaging has previously been reported for ophthalmic OCT imaging systems employing a constant linear velocity spiral scanning (CVL-SC) pattern to drive a galvanometric scanner [12][13][14][15]. Scanning in a spiral pattern allows continuous, uninterrupted data collection and optimizes the available mirror bandwidth by scanning both mirror axes at similar speeds, resulting in efficient interrogation of a volume.…”

Section: Introductionmentioning

confidence: 99%

“…Scanning in a spiral pattern allows continuous, uninterrupted data collection and optimizes the available mirror bandwidth by scanning both mirror axes at similar speeds, resulting in efficient interrogation of a volume. In previous studies employing spiral scanning, data collected along the spiral beam trajectory was interpolated onto a uniform Cartesian grid for display either in post-processing using bi-linear interpolation [12] or in real time using nearest-neighbor interpolation [15]. Geometric distortion in OCT images is well documented, and many approaches to correcting for it have been reported [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Geometrically accurate real-time volumetric visualization of the middle ear using optical coherence tomography

Adamson¹,

Farrell²,

Wang³

et al. 2023

Preprint

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We introduce a novel system for geometrically accurate , continuous, live, volumetric middle ear optical coherence tomography imaging over a 13 mm × 30° × 30° field of view from a handheld imaging probe. The system employs a discretized spiral scanning (DC-SC) pattern to rapidly collect volumetric data and applies real-time scan conversion and lateral angular distortion correction to reduce geometric inaccuracies to below the system’s lateral resolution over 91% of the field of view. We validate the geometric accuracy of the resulting images through comparison with co-registered micro-computed tomography (micro-CT) volumes of a phantom target and a cadaveric middle ear. The system’s real-time volumetric imaging capabilities are assessed by imaging the ear of a healthy subject while performing dynamic pressurization of the middle ear in a Valsalva maneuver.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Geometrically accurate real-time volumetric visualization of the middle ear using optical coherence tomography

Adamson¹,

Farrell²,

Wang³

et al. 2023

Preprint

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“…Our work is based on a previously published 31 4D-OCT system. We extended it by incorporating a neural network for noise suppression.…”

Section: Methodsmentioning

confidence: 99%

Live 4D-OCT denoising with self-supervised deep learning

Nienhaus

Matten

Britten

et al. 2023

Sci Rep

Self Cite

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By providing three-dimensional visualization of tissues and instruments at high resolution, live volumetric optical coherence tomography (4D-OCT) has the potential to revolutionize ophthalmic surgery. However, the necessary imaging speed is accompanied by increased noise levels. A high data rate and the requirement for minimal latency impose major limitations for real-time noise reduction. In this work, we propose a low complexity neural network for denoising, directly incorporated into the image reconstruction pipeline of a microscope-integrated 4D-OCT prototype with an A-scan rate of 1.2 MHz. For this purpose, we trained a blind-spot network on unpaired OCT images using a self-supervised learning approach. With an optimized U-Net, only a few milliseconds of additional latency were introduced. Simultaneously, these architectural adaptations improved the numerical denoising performance compared to the basic setup, outperforming non-local filtering algorithms. Layers and edges of anatomical structures in B-scans were better preserved than with Gaussian filtering despite comparable processing time. By comparing scenes with and without denoising employed, we show that neural networks can be used to improve visual appearance of volumetric renderings in real time. Enhancing the rendering quality is an important step for the clinical acceptance and translation of 4D-OCT as an intra-surgical guidance tool.

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