Influence of noise-reduction techniques in sparse-data sample rotation tomographic imaging

Abstract

Data acquisition and processing is a critical issue for high-speed applications, especially in three-dimensional live cell imaging and analysis. This paper focuses on sparse-data sample rotation tomographic reconstruction and analysis with several noise-reduction techniques. For the sample rotation experiments, a live Candida rugosa sample is used and controlled by holographic optical tweezers, and the transmitted complex wavefronts of the sample are recorded with digital holographic microscopy. Three different cases of sample rotation tomography were reconstructed for dense angle with a step rotation at every 2°, and for sparse angles with step rotation at every 5° and 10°. The three cases of tomographic reconstruction performance are analyzed with consideration for data processing using four noise-reduction techniques. The experimental results demonstrate potential capability in retaining the tomographic image quality, even at the sparse angle reconstructions, with the help of noise-reduction techniques.

Roadmap on Digital Holography-Based Quantitative Phase Imaging

“…The IDT system has more flexibility in controlling the free-floating sample, and it is possible to extend the spatial frequency coverage along the axial direction. The continuous development and progressive solutions by the researchers in computational, experimental, and artificial intelligence algorithms has brought enhanced measurement accuracy to technique, increased certainty of analysis [112][113][114][115][131][132][133], as well as access to new multimodal techniques and functionalities [134][135][136][137][138][139][140].…”

“…At present, 3D QPI has enhanced its implementations by combining techniques, such as fluorescence or Raman imaging, into multimodal operations [135][136][137][138][139][140][141]152,153,155]. Recent developments in artificial intelligence algorithms and machine learning approaches are now the focus in 3D QPI systems, aimed at improving system architecture and measurement accuracy in a more effective way [112][113][114][115][131][132][133]176,177].…”

Advances in Microfluidics for Single Red Blood Cell Analysis

“…3D Holographic Tomography being one of the most powerful 3D QPI methods advances further by combining various techniques into multimodal operations, integrating Raman imaging, Brillouin spectroscopy or fluorescence [33][34][35]. Artificial intelligence algorithms and machine learning approaches impact the system architecture improving measurement accuracy becoming the focus in 3D QPI systems [36][37][38][39][40][41][42].…”

“…grating Raman imaging, Brillouin spectroscopy or fluorescence [33][34][35]. Artificial intelligence algorithms and machine learning approaches impact the system architecture improving measurement accuracy becoming the focus in 3D QPI systems [36][37][38][39][40][41][42].…”

Imaging acoustic field with multicolor digital holography

“…Digital holography is one of the relevant approaches to get a 3D distribution, using one single hologram, as for 3D particle imaging [1], or using a collection of holograms, as for the measurement of refractive index distribution of biological samples [2][3][4][5]. There is a technique of digital holography based tomographic method which is promising for quantitative cell imaging and analysis [6][7][8][9][10][11][12]: it can be referred in different terminology such as synthetic aperture microscopy, optical diffraction tomography, tomographic phase microscopy and holographic tomography [6,[12][13][14][15][16][17][18][19][20][21]. With a unique hologram the capability of refocus from holography is used to provide a 3D cube of the field of view.…”