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Raiola M, Sendra M, Torres M. Imaging Approaches and the Quantitative Analysis of Heart Development. J Cardiovasc Dev Dis 2023; 10:jcdd10040145. [PMID: 37103024 PMCID: PMC10144158 DOI: 10.3390/jcdd10040145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Heart morphogenesis is a complex and dynamic process that has captivated researchers for almost a century. This process involves three main stages, during which the heart undergoes growth and folding on itself to form its common chambered shape. However, imaging heart development presents significant challenges due to the rapid and dynamic changes in heart morphology. Researchers have used different model organisms and developed various imaging techniques to obtain high-resolution images of heart development. Advanced imaging techniques have allowed the integration of multiscale live imaging approaches with genetic labeling, enabling the quantitative analysis of cardiac morphogenesis. Here, we discuss the various imaging techniques used to obtain high-resolution images of whole-heart development. We also review the mathematical approaches used to quantify cardiac morphogenesis from 3D and 3D+time images and to model its dynamics at the tissue and cellular levels.
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Affiliation(s)
- Morena Raiola
- Cardiovascular Regeneration Program, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
- Departamento de Ingeniería Biomedica, ETSI de Telecomunicaciones, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Miquel Sendra
- Cardiovascular Regeneration Program, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
| | - Miguel Torres
- Cardiovascular Regeneration Program, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain
- Correspondence:
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Li C, Rai MR, Ghashghaei HT, Greenbaum A. Illumination angle correction during image acquisition in light-sheet fluorescence microscopy using deep learning. BIOMEDICAL OPTICS EXPRESS 2022; 13:888-901. [PMID: 35284156 PMCID: PMC8884226 DOI: 10.1364/boe.447392] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 05/07/2023]
Abstract
Light-sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that provides optical sectioning with reduced photodamage. LSFM is routinely used in life sciences for live cell imaging and for capturing large volumes of cleared tissues. LSFM has a unique configuration, in which the illumination and detection paths are separated and perpendicular to each other. As such, the image quality, especially at high resolution, largely depends on the degree of overlap between the detection focal plane and the illuminating beam. However, spatial heterogeneity within the sample, curved specimen boundaries, and mismatch of refractive index between tissues and immersion media can refract the well-aligned illumination beam. This refraction can cause extensive blur and non-uniform image quality over the imaged field-of-view. To address these issues, we tested a deep learning-based approach to estimate the angular error of the illumination beam relative to the detection focal plane. The illumination beam was then corrected using a pair of galvo scanners, and the correction significantly improved the image quality across the entire field-of-view. The angular estimation was based on calculating the defocus level on a pixel level within the image using two defocused images. Overall, our study provides a framework that can correct the angle of the light-sheet and improve the overall image quality in high-resolution LSFM 3D image acquisition.
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Affiliation(s)
- Chen Li
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Mani Ratnam Rai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - H. Troy Ghashghaei
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Alon Greenbaum
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
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Liang N, Huang Z, Yan C, Wang K, Kuang D. Lattice light sheets generated with a firmly arranged dielectric regular hexagonal pyramid array. OPTICS LETTERS 2021; 46:4100-4103. [PMID: 34469949 DOI: 10.1364/ol.434299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 07/16/2021] [Indexed: 06/13/2023]
Abstract
We present a firmly arranged dielectric regular hexagonal pyramid array to generate lattice light sheets with high conversion efficiency and low stray light. Both the size and working distance of the lattice light sheets can be modulated by changing the structural parameters. We experimentally recorded the lattice light sheets illumination, which is consistent with the corresponding simulation. To evaluate the imaging quality, we compared the light field generated with and without structure by using polystyrene fluorescent microspheres. This study provides a potential method for the building of light sheet fluorescence microscopy with high resolution and low phototoxicity.
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Li C, Moatti A, Zhang X, Troy Ghashghaei H, Greenabum A. Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy. BIOMEDICAL OPTICS EXPRESS 2021; 12:5214-5226. [PMID: 34513252 PMCID: PMC8407817 DOI: 10.1364/boe.427099] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/12/2021] [Accepted: 07/07/2021] [Indexed: 05/23/2023]
Abstract
Light-sheet fluorescence microscopy (LSFM) is a minimally invasive and high throughput imaging technique ideal for capturing large volumes of tissue with sub-cellular resolution. A fundamental requirement for LSFM is a seamless overlap of the light-sheet that excites a selective plane in the specimen, with the focal plane of the objective lens. However, spatial heterogeneity in the refractive index of the specimen often results in violation of this requirement when imaging deep in the tissue. To address this issue, autofocus methods are commonly used to refocus the focal plane of the objective-lens on the light-sheet. Yet, autofocus techniques are slow since they require capturing a stack of images and tend to fail in the presence of spherical aberrations that dominate volume imaging. To address these issues, we present a deep learning-based autofocus framework that can estimate the position of the objective-lens focal plane relative to the light-sheet, based on two defocused images. This approach outperforms or provides comparable results with the best traditional autofocus method on small and large image patches respectively. When the trained network is integrated with a custom-built LSFM, a certainty measure is used to further refine the network's prediction. The network performance is demonstrated in real-time on cleared genetically labeled mouse forebrain and pig cochleae samples. Our study provides a framework that could improve light-sheet microscopy and its application toward imaging large 3D specimens with high spatial resolution.
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Affiliation(s)
- Chen Li
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Adele Moatti
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
| | - Xuying Zhang
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - H. Troy Ghashghaei
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Alon Greenabum
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
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