3D Hessian deconvolution of thick light-sheet z-stacks for high-contrast and high-SNR volumetric imaging

Zhang, Zhe; Gou, Dongzhou; Fang, Feng; Zheng, Ruyi; Du, Ke; Hua, Yang; Zhang, Guangyi; Zhang, Huitao; Tao, Louis; Chen, Liangyi

doi:10.1364/prj.388651

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…Figures 7A-C show the phase modulation for generating an apertured Gaussian, a Bessel, and an Airy profile, which can all be realized by a single SLM [43,80]. The intensity distribution of a Bessel beam along the axial direction can be modulated by adjusting the concentric ring patterns on the SLM [84].…”

Section: Spatial Light Modulatormentioning

confidence: 99%

“…(L,M) Simulated intensity distributions of single (L) and dual (M) vortex Airy beams [48]. Reproduced with permission from Springer Nature [82], reprinted with permission of OSA [80,81,83], and reprinted with the permission of AIP Publishing [48].…”

Section: Spatial Light Modulatormentioning

confidence: 99%

“…FIGURE 7 | (A-C) Creation of (A) apertured Gaussian, (B) Bessel, and (C) optical Airy beams[80]. (D) Phase masks on the SLM and their superposition[81].…”

mentioning

confidence: 99%

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Non-Diffracting Light Wave: Fundamentals and Biomedical Applications

Ren

Tang

et al. 2021

Front. Phys.

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The light propagation in the medium normally experiences diffraction, dispersion, and scattering. Studying the light propagation is a century-old problem as the photons may attenuate and wander. We start from the fundamental concepts of the non-diffracting beams, and examples of the non-diffracting beams include but are not limited to the Bessel beam, Airy beam, and Mathieu beam. Then, we discuss the biomedical applications of the non-diffracting beams, focusing on linear and nonlinear imaging, e.g., light-sheet fluorescence microscopy and two-photon fluorescence microscopy. The non-diffracting photons may provide scattering resilient imaging and fast speed in the volumetric two-photon fluorescence microscopy. The non-diffracting Bessel beam and the Airy beam have been successfully used in volumetric imaging applications with faster speed since a single 2D scan provides information in the whole volume that adopted 3D scan in traditional scanning microscopy. This is a significant advancement in imaging applications with sparse sample structures, especially in neuron imaging. Moreover, the fine axial resolution is enabled by the self-accelerating Airy beams combined with deep learning algorithms. These additional features to the existing microscopy directly realize a great advantage over the field, especially for recording the ultrafast neuronal activities, including the calcium voltage signal recording. Nonetheless, with the illumination of dual Bessel beams at non-identical orders, the transverse resolution can also be improved by the concept of image subtraction, which would provide clearer images in neuronal imaging.

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Section: Spatial Light Modulatormentioning

confidence: 99%

Section: Spatial Light Modulatormentioning

confidence: 99%

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Non-Diffracting Light Wave: Fundamentals and Biomedical Applications

Ren

Tang

et al. 2021

Front. Phys.

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“…For thick light sheet images obtained by using different optical setups, ref. [9] presents a comprehensive pipeline. It involves the removal of Poisson-Gaussian mixed noise slice by slice (by using a side window filtering technique), deconvolution of the 3D image stack, and elimination of dark stripe artifacts introduced by their deconvolution scheme.…”

Section: Related Workmentioning

confidence: 99%

Distortion Correction and Denoising of Light Sheet Fluorescence Images

Julia,

Iguernaissi,

Michel

et al. 2024

Sensors

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Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson–Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data.

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“…Moreover, in [ 23 ] the authors introduce an image formation model similar to the one described in the present manuscript. However, the regions of the resulting PSF where the light-sheet is out of focus are discarded, hence approximating the overall PSF with a constant PSF and then performing deconvolution using the ADMM algorithm.…”

Section: Introductionmentioning

confidence: 99%

Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise

et al. 2022

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We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson and Gaussian noise. The spatial variation of the point spread function of a light-sheet microscope is determined by the interaction between the excitation sheet and the detection objective PSF. We introduce a model of the image formation process that incorporates this interaction and we formulate a variational model that accounts for the combination of Poisson and Gaussian noise through a data fidelity term consisting of the infimal convolution of the single noise fidelities, first introduced in L. Calatroni et al. (SIAM J Imaging Sci 10(3):1196–1233, 2017). We establish convergence rates and a discrepancy principle for the infimal convolution fidelity and the inverse problem is solved by applying the primal–dual hybrid gradient (PDHG) algorithm in a novel way. Numerical experiments performed on simulated and real data show superior reconstruction results in comparison with other methods.

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3D Hessian deconvolution of thick light-sheet z-stacks for high-contrast and high-SNR volumetric imaging

Cited by 7 publications

References 41 publications

Non-Diffracting Light Wave: Fundamentals and Biomedical Applications

Non-Diffracting Light Wave: Fundamentals and Biomedical Applications

Distortion Correction and Denoising of Light Sheet Fluorescence Images

Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise

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