Compressive sensing for fast 3-D and random-access two-photon microscopy

Wen, Chenyang; Ren, Mindan; Feng, Fu; Chen, Wang; Chen, Shih‐Chi

doi:10.1364/ol.44.004343

Cited by 11 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In all of these modalities, serial scanning of the sample volume is required, which limits the imaging speed and throughput, reducing the temporal resolution, also introducing potential photobleaching on the sample. Different imaging methods have been proposed to improve the throughput of scanning-based 3D microscopy techniques, such as multifocal imaging [8][9][10][11][12][13], light-field microscopy [14,15], microscopy with engineered point spread functions (PSFs) [16][17][18] and compressive sensing [19][20][21]. Nevertheless, these solutions introduce trade-offs, either by complicating the microscope system design, compromising the image quality and/or resolution or prolonging the image post-processing time.…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network-based volumetric fluorescence microscopy

et al. 2021

View full text Add to dashboard Cite

Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume. Through a recurrent convolutional neural network, which we term as Recurrent-MZ, 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field. Using experiments on C. elegans and nanobead samples, Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume. We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions, including e.g., different sequences of input images, covering various axial permutations and unknown axial positioning errors. We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input, matching confocal microscopy images of the same sample volume. Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework, overcoming the limitations of current 3D scanning microscopy tools.

show abstract

Section: Introductionmentioning

confidence: 99%

Recurrent neural network-based volumetric fluorescence microscopy

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, TPFM is mainly limited by the low imaging speed (about 0.5 Hz), and it take tens of minutes to obtain a high-resolution image stack for the traditional TPEM [61]. Recently, an advanced TPFM with a high sampling speed of 2.7 kHz has been developed using a multilfocus compressive sensing method and a digital micromirror device [62]. Besides, the spatiotemporal resolution of TPFM is relatively low, which is theoretically 500 nm laterally and 1 µm axially; it also follows the diffraction limitation [63].…”

Section: Two-photon Fluorescence Microscope (Tpfm)mentioning

confidence: 99%

Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications

et al. 2020

View full text Add to dashboard Cite

Intracellular micromanipulation assisted by robotic systems has valuable applications in biomedical research, such as genetic diagnosis and genome-editing tasks. However, current studies suffer from a low success rate and a large operation damage because of insufficient information on the operation information of targeted specimens. The complexity of the intracellular environment causes difficulties in visualizing manipulation tools and specimens. This review summarizes and analyzes the current development of advanced biological imaging sampling and computational processing methods in intracellular micromanipulation applications. It also discusses the related limitations and future extension, providing an important reference about this field.

show abstract

“…Built upon our experience in DMD-based light microscopy, ,− by integration with conventional diamond quantum sensing, we present a dynamic super-resolved WQDM. First, we experimentally show SIM-based super-resolution imaging of fluorescent nanodiamonds (FNDs) with a lateral subdiffraction resolution of 202 nm.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Enabled Widefield Quantum Diamond Microscopy

Xu,

Chen,

Hou

et al. 2023

ACS Photonics

Self Cite

View full text Add to dashboard Cite

Widefield quantum diamond microscopy (WQDM) based on Kohler illumination has been widely adopted in the field of quantum sensing; however, practical applications are still limited by issues such as unavoidable photodamage and unsatisfied spatialresolution. Here, we design and develop a super-resolution-enabled WQDM using a digital micromirror device (DMD)-based structured illumination microscope. With the rapidly programmable illumination patterns, we have first demonstrated how to mitigate phototoxicity when imaging nanodiamonds in cell samples. As a showcase, we have performed the super-resolved quantum sensing measurements of two individual nanodiamonds not even distinguishable with conventional WQDM. The DMD-powered WQDM presents not only excellent compatibility with quantum sensing solutions, but also strong advantages in high imaging speed, high resolution, low phototoxicity, and an enhanced signal-to-background ratio, making it a competent tool for applications in demanding fields such as biomedical science.

show abstract

Compressive sensing for fast 3-D and random-access two-photon microscopy

Cited by 11 publications

References 11 publications

Recurrent neural network-based volumetric fluorescence microscopy

Recurrent neural network-based volumetric fluorescence microscopy

Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications

Super-Resolution Enabled Widefield Quantum Diamond Microscopy

Contact Info

Product

Resources

About