Dataset Information

Multiplane HiLo microscopy with speckle illumination and non-local means denoising.

ABSTRACT:

Significance

HiLo microscopy synthesizes an optically sectioned image from two images, one obtained with uniform and another with patterned illumination, such as laser speckle. Speckle-based HiLo has the advantage of being robust to aberrations but is susceptible to residual speckle noise that is difficult to control. We present a computational method to reduce this residual noise without undermining resolution. In addition, we improve the versatility of HiLo microscopy by enabling simultaneous multiplane imaging (here nine planes).

Aim

Our goal is to perform fast, high-contrast, multiplane imaging with a conventional camera-based fluorescence microscope.

Approach

Multiplane HiLo imaging is achieved with the use of a single camera and z-splitter prism. Speckle noise reduction is based on the application of a non-local means (NLM) denoising method to perform ensemble averaging of speckle grains.

Results

We demonstrate the capabilities of multiplane HiLo with NLM denoising both with synthesized data and by imaging cardiac and brain activity in zebrafish larvae at 40 Hz frame rates.

Conclusions

Multiplane HiLo microscopy aided by NLM denoising provides a simple tool for fast optically sectioned volumetric imaging that can be of general utility for fluorescence imaging applications.

SUBMITTER: Zheng S

PROVIDER: S-EPMC10704089 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

Multiplane HiLo microscopy with speckle illumination and non-local means denoising.

Zheng Shuqi S Koyama Minoru M Mertz Jerome J

Journal of biomedical optics 20231117 11

<h4>Significance</h4>HiLo microscopy synthesizes an optically sectioned image from two images, one obtained with uniform and another with patterned illumination, such as laser speckle. Speckle-based HiLo has the advantage of being robust to aberrations but is susceptible to residual speckle noise that is difficult to control. We present a computational method to reduce this residual noise without undermining resolution. In addition, we improve the versatility of HiLo microscopy by enabling simul ...[more]

PMID: 38078150

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Project description:BackgroundAdvancements in hybrid positron emission tomography-magnetic resonance (PET-MR) systems allow for combining the advantages of each modality. Integrating information from MRI and PET can be valuable for diagnosing and treating neurological disorders. However, combining diffusion MRI (dMRI) and PET data, which provide highly complementary information, has rarely been exploited in image post-processing. dMRI has the ability to investigate the white matter pathways of the brain through fibre tractography, which enables comprehensive mapping of the brain connection networks (the "connectome"). Novel methods are required to combine information present in the connectome and PET to increase the full potential of PET-MRI.MethodsWe developed a CONNectome-based Non-Local Means (CONN-NLM) filter to exploit synergies between dMRI-derived structural connectivity and PET intensity information to denoise PET images. PET-MR data are parcelled into a number of regions based on a brain atlas, and the inter-regional structural connectivity is calculated based on dMRI fibre-tracking. The CONN-NLM filter is then implemented as a post-reconstruction filter by combining the nonlocal means filter and a connectivity-based cortical smoothing. The effect of this approach is to weight voxels with similar PET intensity and highly connected voxels higher when computing the weighted-average to perform more informative denoising. The proposed method was first evaluated using a novel computer phantom framework to simulate realistic hybrid PET-MR images with different lesion scenarios. CONN-NLM was further assessed with clinical dMRI and tau PET examples.ResultsThe results showed that CONN-NLM has the capacity to improve the overall PET image quality by reducing noise while preserving lesion contrasts, and it outperformed a range of filters that did not use dMRI information. The simulations demonstrate that CONN-NLM can handle various lesion contrasts consistently, as well as lesions with different levels of inter-connectivity.ConclusionCONN-NLM has unique advantages of providing more informative and accurate PET smoothing by adding complementary structural connectivity information from dMRI, representing a new avenue to exploit synergies between MRI and PET.

Project description:BackgroundConvolutional neural network (CNN)-based methods have shown excellent performance in denoising and reconstruction of super-resolved structured illumination microscopy (SR-SIM) data. Therefore, CNN-based architectures have been the focus of existing studies. However, Swin Transformer, an alternative and recently proposed deep learning-based image restoration architecture, has not been fully investigated for denoising SR-SIM images. Furthermore, it has not been fully explored how well transfer learning strategies work for denoising SR-SIM images with different noise characteristics and recorded cell structures for these different types of deep learning-based methods. Currently, the scarcity of publicly available SR-SIM datasets limits the exploration of the performance and generalization capabilities of deep learning methods.ResultsIn this work, we present SwinT-fairSIM, a novel method based on the Swin Transformer for restoring SR-SIM images with a low signal-to-noise ratio. The experimental results show that SwinT-fairSIM outperforms previous CNN-based denoising methods. Furthermore, as a second contribution, two types of transfer learning-namely, direct transfer and fine-tuning-were benchmarked in combination with SwinT-fairSIM and CNN-based methods for denoising SR-SIM data. Direct transfer did not prove to be a viable strategy, but fine-tuning produced results comparable to conventional training from scratch while saving computational time and potentially reducing the amount of training data required. As a third contribution, we publish four datasets of raw SIM images and already reconstructed SR-SIM images. These datasets cover two different types of cell structures, tubulin filaments and vesicle structures. Different noise levels are available for the tubulin filaments.ConclusionThe SwinT-fairSIM method is well suited for denoising SR-SIM images. By fine-tuning, already trained models can be easily adapted to different noise characteristics and cell structures. Furthermore, the provided datasets are structured in a way that the research community can readily use them for research on denoising, super-resolution, and transfer learning strategies.

Dataset Information

Multiplane HiLo microscopy with speckle illumination and non-local means denoising.

Significance

Aim

Approach

Results

Conclusions

Publications

Multiplane HiLo microscopy with speckle illumination and non-local means denoising.

Similar Datasets

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