Angular compounding for speckle reduction in optical coherence tomography using geometric image registration algorithm and digital focusing

Zhao, Jingjing; Winetraub, Yonatan; Yuan, Edwin; Chan, Winnie; Aasi, Sumaira Z.; Sarin, Kavita Y.; Zohar, Orr; Zerda, Adam de la

doi:10.1038/s41598-020-58454-0

Cited by 9 publications

(5 citation statements)

References 54 publications

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“…For diagnostic OCT, averaging of multiple spatially slightly displaced frames is a very simple yet effective method to reduce the noise level and yield extremely high quality scans. Especially advanced techniques such as speckle modulation 11 or angular compounding 12 can achieve high quality images without loss of resolution. However, the speed requirements as well as the dynamic scene limit the applicability of averaging in an intra-surgical scenario.…”

Section: Introductionmentioning

confidence: 99%

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

Nienhaus

Matten

Britten

et al. 2023

Sci Rep

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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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Section: Introductionmentioning

confidence: 99%

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

Nienhaus

Matten

Britten

et al. 2023

Sci Rep

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“…Speckle suppression is often achieved by incoherent averaging of images with different speckle realizations [7], e.g., through angular compounding [8,9]. Averaging methods attempt to preserve the resolution while suppressing speckle arising from non-resolved tissue structure; yet, some methods produce blurred images.…”

Section: Introductionmentioning

confidence: 99%

Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction

Pereg

2023

J. Imaging

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Speckle noise has long been an extensively studied problem in medical imaging. In recent years, there have been significant advances in leveraging deep learning methods for noise reduction. Nevertheless, adaptation of supervised learning models to unseen domains remains a challenging problem. Specifically, deep neural networks (DNNs) trained for computational imaging tasks are vulnerable to changes in the acquisition system’s physical parameters, such as: sampling space, resolution, and contrast. Even within the same acquisition system, performance degrades across datasets of different biological tissues. In this work, we propose a few-shot supervised learning framework for optical coherence tomography (OCT) noise reduction, that offers high-speed training (of the order of seconds) and requires only a single image, or part of an image, and a corresponding speckle-suppressed ground truth, for training. Furthermore, we formulate the domain shift problem for OCT diverse imaging systems and prove that the output resolution of a despeckling trained model is determined by the source domain resolution. We also provide possible remedies. We propose different practical implementations of our approach, verify and compare their applicability, robustness, and computational efficiency. Our results demonstrate the potential to improve sample complexity, generalization, and time efficiency, for coherent and non-coherent noise reduction via supervised learning models, that can also be leveraged for other real-time computer vision applications.

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“… 17 To overcome these limitations, a recent study proposed the combination of angular compounding with geometric image registration and digital focusing. 18 …”

Section: Introductionmentioning

confidence: 99%

Adaptive compounding speckle-noise-reduction filter for optical coherence tomography images

et al. 2021

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. Significance: Speckle noise limits the diagnostic capabilities of optical coherence tomography (OCT) images, causing both a reduction in contrast and a less accurate assessment of the microstructural morphology of the tissue. Aim: We present a speckle-noise reduction method for OCT volumes that exploits the advantages of adaptive-noise wavelet thresholding with a wavelet compounding method applied to several frames acquired from consecutive positions. The method takes advantage of the wavelet representation of the speckle statistics, calculated properly from a homogeneous sample or a region of the noisy volume. Approach: The proposed method was first compared quantitatively with different state-of-the-art approaches by being applied to three different clinical dermatological OCT volumes with three different OCT settings. The method was also applied to a public retinal spectral-domain OCT dataset to demonstrate its applicability to different imaging modalities. Results: The results based on four different metrics demonstrate that the proposed method achieved the best performance among the tested techniques in suppressing noise and preserving structural information. Conclusions: The proposed OCT denoising technique has the potential to adapt to different image OCT settings and noise environments and to improve image quality prior to clinical diagnosis based on visual assessment.

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Angular compounding for speckle reduction in optical coherence tomography using geometric image registration algorithm and digital focusing

Cited by 9 publications

References 54 publications

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

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

Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction

Adaptive compounding speckle-noise-reduction filter for optical coherence tomography images

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