Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy

Poon, Chien-Sing; Long, Feixiao; Sunar, Ulaş

doi:10.1364/boe.402508

Cited by 12 publications

(7 citation statements)

References 25 publications

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“…Table 5 lists existing deep learning methods applied to DCS techniques. It shows that DCS-NET’s training is much faster than 2DCNNs, 40 approximately 140-fold faster. Although the remaining models, RNN, 39 LSTM, 41 and ConvGRU, 42 have fewer total layers, they are limited to a specific ρ.…”

Section: Discussionmentioning

confidence: 99%

“…First, DCS-NET’s training datasets were generated using the semi-infinite diffusion model as advised in Ref. 40. Nevertheless, this model does not consider scalp and skull thicknesses, which could potentially explain why the error range (−6%∼+8%) caused by Δ1 and Δ2 is much broader than that (−1%∼+5%) caused by μa and μs′ (Figs.…”

Section: Discussionmentioning

confidence: 99%

“…34 Deep learning, an increasingly popular method, has been widely applied to biomedical time sequence data, including electroencephalogram (EEG) and electrocardiogram (ECG), 37,38 but has yet to be broadly used in DCS. Very recently, Zhang et al 39 proposed the first recurrent neural network (RNN) regression model to DCS, followed by 2D convolution neural networks (2D CNNs), 40 long short-term memory (LSTM), 41 and ConvGRU. 42 LSTM, as a typical RNN structure, has proven stable and robust for quantifying relative blood flow in previous studies in phantom and in vivo experiments.…”

Section: Introductionmentioning

confidence: 99%

“…Very recently, Zhang et al 39 . proposed the first recurrent neural network (RNN) regression model to DCS, followed by 2D convolution neural networks (2D CNNs), 40 long short-term memory (LSTM), 41 and ConvGRU 42 . LSTM, as a typical RNN structure, has proven stable and robust for quantifying relative blood flow in previous studies in phantom and in vivo experiments 41 .…”

Section: Introductionmentioning

confidence: 99%

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Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

Wang,

Pan,

Zang

et al. 2024

J. Biomed. Opt.

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Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient μ a and reduced scattering coefficient μ 0 s ) and scalp and skull thicknesses. Aim:We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance.Approach: We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor (β) estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5,10,15,20,25, and 30 mm) on BFi and β estimations among DCS-NET, semi-infinite, and three-layer fitting models.Results: DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of μ a and μ 0 s at a source-detector separation of 30 mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively.Conclusions: DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous realtime blood flow measurements.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

Wang,

Pan,

Zang

et al. 2024

J. Biomed. Opt.

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“…The computational processing requirements of holographic FD-DCS are high, especially when operating in real-time at fast

frame rates. With a view to reducing the computational demand of conventional DCS experiments, deep learning techniques have recently been employed [ 44 ], resulting in a 23-fold increase in the speed of blood flow quantification. The application of deep learning techniques to holographic FD-DCS would be an interesting further study.…”

Section: Outlook and Discussionmentioning

confidence: 99%

Performance optimisation of a holographic Fourier domain diffuse correlation spectroscopy instrument

James

Powell

Munro

2022

Biomed. Opt. Express

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We have previously demonstrated a novel interferometric multispeckle Fourier domain diffuse correlation spectroscopy system that makes use of holographic camera-based detection, and which is capable of making in vivo pulsatile flow measurements. In this work, we report on a systematic characterisation of the signal-to-noise ratio performance of our system. This includes demonstration and elimination of laser mode hopping, and correction for the instrument’s modulation transfer function to ensure faithful reconstruction of measured intensity profiles. We also demonstrate a singular value decomposition approach to ensure that spatiotemporally correlated experimental noise sources do not limit optimal signal-to-noise ratio performance. Finally, we present a novel multispeckle denoising algorithm that allows our instrument to achieve a signal-to-noise ratio gain that is equal to the square root of the number of detected speckles, whilst detecting up to ∼1290 speckles in parallel. The signal-to-noise ratio gain of 36 that we report is a significant step toward mitigating the trade-off that exists between signal-to-noise ratio and imaging depth in diffuse correlation spectroscopy.

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Deep Learning in Biomedical Optics

et al. 2021

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This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC

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Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy

Cited by 12 publications

References 25 publications

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

Performance optimisation of a holographic Fourier domain diffuse correlation spectroscopy instrument

Deep Learning in Biomedical Optics

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