Subtopic Deep Dive
Digital Holographic Tomography
Research Guide
What is Digital Holographic Tomography?
Digital Holographic Tomography (DHT) reconstructs three-dimensional refractive index distributions of microscopic objects using multi-angle or multi-wavelength digital holographic projections.
DHT combines digital holography with tomographic inversion to map 3D phase distributions without labels. Key advancements include multi-view acquisition systems and machine learning for cell analysis (Belashov et al., 2021, 34 citations; Behal et al., 2022, 11 citations). Over 10 papers since 2006 demonstrate applications in cell imaging and micro-object reconstruction.
Why It Matters
DHT enables label-free 3D visualization of cellular architecture, aiding biomechanics and pathology studies (Huang et al., 2024, 5 citations). Behal et al. (2022) developed reliable flow cyto-tomography for high-throughput cell analysis. Jóźwicka and Kujawińska (2006) proved 3D micro-object measurements, supporting intracellular organelle imaging without markers.
Key Research Challenges
Ill-posed inversion problems
Tomographic reconstruction from limited projections suffers from underdetermination (Williams, 2014). Liu (2014) evaluated reconstruction techniques for one-shot multi-angle setups. Algorithms require regularization to stabilize solutions.
Multi-view acquisition reliability
Precise sample rotation or illumination control is needed for projections (Behal et al., 2022). Experimental layouts must minimize aberrations in flow systems. Optimization of hardware ensures consistent holograms.
Computational processing demands
Phase retrieval and 3D inversion demand high compute for large datasets (Jóźwicka and Kujawińska, 2006). Machine learning integration aids classification but requires validation (Belashov et al., 2021). Real-time processing remains limited.
Essential Papers
Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images
А.В. Белашов, А.А. Жихорева, T. N. Belyaeva et al. · 2021 · Cells · 34 citations
In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) ba...
Digital Holographic Interferometry for the Measurement of Symmetrical Temperature Fields in Liquids
Gramoz Çubreli, Pavel Psota, Petra Dančová et al. · 2021 · Photonics · 18 citations
In this paper, we present a method of quantitatively measuring in real-time the dynamic temperature field change and visualization of volumetric temperature fields generated by a 2D axial-symmetric...
Developing a Reliable Holographic Flow Cyto-Tomography Apparatus by Optimizing the Experimental Layout and Computational Processing
Jaromír Běhal, Francesca Borrelli, Martina Mugnano et al. · 2022 · Cells · 11 citations
Digital Holographic Tomography (DHT) has recently been established as a means of retrieving the 3D refractive index mapping of single cells. To make DHT a viable system, it is necessary to develop ...
Digital holographic microscopy for microalgae biovolume assessment
A. C. Monaldi, G. G. Romero, E. E. Alanís et al. · 2014 · Optics Communications · 9 citations
Label-Free Three-Dimensional Morphological Characterization of Cell Death Using Holographic Tomography
Chung-Hsuan Huang, Yun‐Ju Lai, Li-Nian Chen et al. · 2024 · Sensors · 5 citations
This study presents a novel label-free approach for characterizing cell death states, eliminating the need for complex molecular labeling that may yield artificial or ambiguous results due to techn...
Recording of Long Low-Amplitude Bulk Elastic Waves in Transparent Solid Waveguides by Digital and Classical Holography
А.В. Белашов, А.А. Жихорева, И. В. Семенова · 2022 · Applied Sciences · 5 citations
In this paper we compare two implementations of the holographic technique for recording long, nonlinear, elastic waves of low amplitude in solid polymer waveguides: classical holographic interferom...
Experimental proof-of-principle 3D measurements of micro-objects by digital holographic tomography
Agata Jóźwicka, Małgorzata Kujawińska · 2006 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2 citations
In the paper the experimental and numerical reconstruction of phase (waveguide and multimode fiber) and amplitude (microscopic glass plates with stickers glued on it) microobjects using digital hol...
Reading Guide
Foundational Papers
Start with Jóźwicka and Kujawińska (2006) for proof-of-principle 3D measurements, then Williams (2014) for tomographic methods.
Recent Advances
Study Behal et al. (2022) for reliable apparatus and Huang et al. (2024) for cell death characterization.
Core Methods
Core techniques: multi-angle holography, phase retrieval, tomographic inversion (Liu, 2014; Monaldi et al., 2014).
How PapersFlow Helps You Research Digital Holographic Tomography
Discover & Search
Research Agent uses searchPapers with 'Digital Holographic Tomography cells' to retrieve Behal et al. (2022), then citationGraph reveals forward citations like Huang et al. (2024), and findSimilarPapers expands to multi-view systems.
Analyze & Verify
Analysis Agent applies readPaperContent on Behal et al. (2022) to extract inversion algorithms, verifyResponse with CoVe checks reconstruction claims against Jóźwicka and Kujawińska (2006), and runPythonAnalysis simulates phase unwrapping with NumPy for GRADE A verification.
Synthesize & Write
Synthesis Agent detects gaps in multi-angle acquisition via contradiction flagging between Liu (2014) and Behal et al. (2022); Writing Agent uses latexEditText for tomography equations, latexSyncCitations for 10+ papers, and latexCompile for report export.
Use Cases
"Simulate refractive index tomography from sample holograms"
Research Agent → searchPapers 'DHT inversion algorithms' → Analysis Agent → runPythonAnalysis (NumPy tomography solver on Belashov et al. data) → matplotlib 3D refractive index plot.
"Write review on DHT cell imaging advances"
Synthesis Agent → gap detection across Behal et al. (2022) and Huang et al. (2024) → Writing Agent → latexEditText (add equations), latexSyncCitations (15 papers), latexCompile → PDF with diagrams.
"Find code for holographic reconstruction"
Research Agent → paperExtractUrls (Williams 2014) → paperFindGithubRepo → Code Discovery → githubRepoInspect → verified Python phase retrieval scripts.
Automated Workflows
Deep Research workflow scans 50+ DHT papers via searchPapers, structures report with citationGraph on Behal et al. (2022) lineage, and GRADEs evidence. DeepScan applies 7-step CoVe to verify multi-view claims in Jóźwicka and Kujawińska (2006). Theorizer generates inversion hypotheses from Liu (2014) reconstruction techniques.
Frequently Asked Questions
What is Digital Holographic Tomography?
DHT reconstructs 3D refractive index maps from multi-angle holographic projections (Jóźwicka and Kujawińska, 2006).
What are common methods in DHT?
Methods include multi-view acquisition and filtered back-projection inversion (Behal et al., 2022; Williams, 2014).
What are key papers?
Belashov et al. (2021, 34 citations) on ML cell classification; Behal et al. (2022, 11 citations) on flow cyto-tomography.
What are open problems?
Real-time processing and one-shot multi-angle reconstruction from limited views (Liu, 2014).
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Part of the Digital Holography and Microscopy Research Guide