Generalized Image Reconstruction in Optical Coherence Tomography Using Redundant and Non-Uniformly-Spaced Samples

Nagib, Karim; Mezgebo, Biniyam; Fernando, Namal; Kordi, Behzad; Sherif, Sherif S.

doi:10.3390/s21217057

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…The theorem states: "In the process of converting analog signals to digital signals, when the sampling frequency is greater than twice the highest frequency in the signal, the sampled digital signal can completely retain the information from the original signal [7,8]". However, the Nyquist sampling theorem often leads to redundant sampling [9,10], resulting 2 of 35 in substantial costs to meet the sampling rate requirements in certain applications. Despite the ongoing advancements in various aspects of computers, there are still many challenges in data acquisition and processing [11].…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Wang,

Chen,

Zhao

et al. 2024

Sensors

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Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

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Section: Introductionmentioning

confidence: 99%

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Wang,

Chen,

Zhao

et al. 2024

Sensors

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Dental Imaging Using a Higher Signal-to-Noise Ratio Optical Coherence Tomography System

Azeez,

Mezgebo,

França

et al. 2023

2023 International Conference on Electrical, Computer and Energy Technologies (ICECET)

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Deep learning segmentation of the tear fluid reservoir under the sclera lens in optical coherence tomography images

Zhou¹,

Lin²,

Yu³

et al. 2023

Biomed. Opt. Express

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The tear fluid reservoir (TFR) under the sclera lens is a unique characteristic providing optical neutralization of any aberrations from corneal irregularities. Anterior segment optical coherence tomography (AS-OCT) has become an important imaging modality for sclera lens fitting and visual rehabilitation therapy in both optometry and ophthalmology. Herein, we aimed to investigate whether deep learning can be used to segment the TFR from healthy and keratoconus eyes, with irregular corneal surfaces, in OCT images. Using AS-OCT, a dataset of 31850 images from 52 healthy and 46 keratoconus eyes, during sclera lens wear, was obtained and labeled with our previously developed algorithm of semi-automatic segmentation. A custom-improved U-shape network architecture with a full-range multi-scale feature-enhanced module (FMFE-Unet) was designed and trained. A hybrid loss function was designed to focus training on the TFR, to tackle the class imbalance problem. The experiments on our database showed an IoU, precision, specificity, and recall of 0.9426, 0.9678, 0.9965, and 0.9731, respectively. Furthermore, FMFE-Unet was found to outperform the other two state-of-the-art methods and ablation models, suggesting its strength in segmenting the TFR under the sclera lens depicted on OCT images. The application of deep learning for TFR segmentation in OCT images provides a powerful tool to assess changes in the dynamic tear film under the sclera lens, improving the efficiency and accuracy of lens fitting, and thus supporting the promotion of sclera lenses in clinical practice.

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Generalized Image Reconstruction in Optical Coherence Tomography Using Redundant and Non-Uniformly-Spaced Samples

Cited by 4 publications

References 52 publications

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Dental Imaging Using a Higher Signal-to-Noise Ratio Optical Coherence Tomography System

Deep learning segmentation of the tear fluid reservoir under the sclera lens in optical coherence tomography images

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