Honeycomb pattern removal for fiber bundle endomicroscopy based on a two-step iterative shrinkage thresholding algorithm

Liu, Jialin; Zhou, Wei; Xu, Baoteng; Xiao-yan, Yang; Xiong, Daxi

doi:10.1063/1.5143929

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…Previous works have attempted to use spatial frequency domain filtering on the images 9,10 . Other groups have focused on small movements of the fiber bundle, followed by various technique attempts like superimposition of images 11 , cross-correlation calculations and merging 12 , and other algorithmic approaches [13][14][15] . These are just a few example techniques that have been attempted to reduce the visual effect of honeycombing, with many more available in the literature to address specific applications.…”

Section: 'Honeycombing'mentioning

confidence: 99%

Comparison of micro-optic systems for close-focus microendoscopic applications

Galvez,

Hong,

Rocha

et al. 2024

Endoscopic Microscopy XIX

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To detect disease in organs with small lumens like the pancreas or fallopian tubes, where lumens may be collapsed or filled with mucus, cilia, or plicae, minimally invasive submillimeter endoscopes must meet unique optical requirements. They must provide a moderate angular field of view (AFOV), a close working distance (WD), and provide adequate resolution for the application. Additionally, when using miniature fiber bundles for image relay, the effect of the honeycomb pattern and potential fiber crosstalk must be considered. We compare the optical performance of a gradient refractive index (GRIN) singlet, a 3D-printed doublet, and a 3D-printed triplet, as well as discuss the consequences of fiber bundle use. We show that for our designs, both GRIN lenses and 3D printed aspheric lens systems can meet our application requirements, with to the ability to resolve less than 10µm at 10% contrast. The effect of fiber bundle crosstalk is shown to be negligible because of limited mode coupling in realistic manufactured fiber bundles.

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Section: 'Honeycombing'mentioning

confidence: 99%

Comparison of micro-optic systems for close-focus microendoscopic applications

Galvez,

Hong,

Rocha

et al. 2024

Endoscopic Microscopy XIX

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“…A flat field image of the uniformly scattering opal is cropped such that the fiber bundle is centered in a square field of view, and the Fourier transform of the cropped image is taken. This enables us to gather information about the fiber bundle arrangement, similar to the method described Lui et al 23 . The fiber bundle samples object space irregularly due to the irregular arrangement of fiber cores in the bundle, but fibers tend to be generally equidistant from their nearest neighbors.…”

Section: Fiber Bundlementioning

confidence: 99%

Iterative prototyping based on lessons learned from the falloposcope in vivo pilot study experience

Rocha

Drake²,

Rice³

et al. 2023

Endoscopic Microscopy XVIII

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High grade serous ovarian cancer is the most deadly gynecological cancer, and it is now believed that most originate in the fallopian tubes (FTs). We developed a FT endoscope, the falloposcope, as a method for detecting ovarian cancer. The falloposcope clinical prototype is being implemented in a pilot study with 20 volunteers (12 enrolled to date) to evaluate the safety and feasibility of FT imaging prior to standard of care salpingectomy in normal-risk volunteers. The falloposcope is approximately 0.8 mm in diameter and is introduced via a minimally invasive approach through a commercially available hysteroscope and introducing catheter. To date, FT navigation video, multispectral reflectance and fluorescence images, and optical coherence tomography (OCT) of human FT have successfully been acquired. This manuscript describes the fabrication improvements and iterative design changes that have been introduced to improve usability and reduce failure points based on clinical implementation. We discuss falloposcope improvements made with respect to the following subjects: improving perceived image quality with the fiber bundle, GRIN lens stray light, and improving the proximal imaging system. Navigation and MFI are limited by the 3,000 element fiber bundle and lens working distance (WD). A future system is being developed with a 10,000 element fiber bundle, more uniform illumination, a closer WD lens, and wire cytology instead of OCT probe.

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“…Over the past few years, various methods have been developed to eliminate honeycomb patterns, such as employing bandpass filters in the Fourier domain [8][9][10][11]. However, selecting an appropriate threshold in the frequency domain to eliminate honeycomb patterns without blurring the underlying imaging structures poses a significant challenge, which would severely affect image clarity [12][13][14]. Therefore, there is an urgent need to remove noise and enhance resolution.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Joint Learning for pCLE Image Denoising

Yang,

Zhang,

Qiu

et al. 2024

Sensors

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Probe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles. Recent advancements in deep learning offer promise in image denoising, but the acquisition of clean-noisy image pairs for training networks across all potential scenarios can be prohibitively costly. Few studies have explored training denoising networks on such pairs. Here, we propose an innovative self-supervised denoising method. Our approach integrates noise prediction networks, image quality assessment networks, and denoising networks in a collaborative, jointly trained manner. Compared to prior self-supervised denoising methods, our approach yields superior results on pCLE images and fluorescence microscopy images. In summary, our novel self-supervised denoising technique enhances image quality in pCLE diagnosis by leveraging the synergy of noise prediction, image quality assessment, and denoising networks, surpassing previous methods on both pCLE and fluorescence microscopy images.

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Honeycomb pattern removal for fiber bundle endomicroscopy based on a two-step iterative shrinkage thresholding algorithm

Cited by 12 publications

References 27 publications

Comparison of micro-optic systems for close-focus microendoscopic applications

Comparison of micro-optic systems for close-focus microendoscopic applications

Iterative prototyping based on lessons learned from the falloposcope in vivo pilot study experience

Self-Supervised Joint Learning for pCLE Image Denoising

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