@article{Heldt:25,
author = {Noah Heldt and Tual Monfort and Rion Morishita and Robert Sch\"{o}nherr and Olivier Thouvenin and Ibrahim Abd El-Sadek and Peter K\"{o}nig and Gereon H\"{u}ttmann and Kate Grieve and Yoshiaki Yasuno},
journal = {Biomed. Opt. Express},
keywords = {Dynamic light scattering; Image metrics; Imaging systems; In vivo imaging; Spatial resolution; Spectral domain optical coherence tomography},
number = {11},
pages = {4851--4870},
publisher = {Optica Publishing Group},
title = {Guide to dynamic OCT data analysis},
volume = {16},
month = {Nov},
year = {2025},
url = {https://opg.optica.org/boe/abstract.cfm?URI=boe-16-11-4851},
doi = {10.1364/BOE.571394},
abstract = {Dynamic optical coherence tomography (DOCT) enhances conventional OCT by providing specific information related to flow dynamics, cell motility, and organelle metabolic activity. These biological phenomena can be detected with varying sensitivity depending on the OCT architecture parameters, including wavelength, numerical aperture, and implementation method (time domain or Fourier domain). Despite its potential, the field lacks standardization as various research groups have independently developed algorithms for specific applications. In this paper, we compare four widely used DOCT algorithms, each employing a distinct analytical approach: power spectral density moment analysis, frequency band visualization, logarithmic intensity variation evaluation, and motility-based analysis. These algorithms were originally optimized for different OCT technologies (full-field OCT, microscopic OCT, swept-source OCT, and spectral domain OCT), which vary in temporal and spatial resolution as well as susceptibility to motion artifacts. To conduct a fair evaluation, we perform comprehensive cross-wise comparisons using datasets acquired from each of these setups. Our findings reveal that each method exhibits unique advantages in specific imaging environments, thereby providing valuable guidance for algorithm selection based on particular application requirements.},
}

@ARTICLE{10.3389/fmed.2025.1658890,
  
AUTHOR={Holzhausen, Cornelia  and Schulz-Hildebrandt, Hinnerk  and Ahrens, Martin  and Heldt, Noah  and Pieper, Mario  and Biller, Heike  and von Weihe, Sönke  and Ellebrecht, David  and Abdo, Mustafa  and Steurer, Stefan  and Fraune, Christoph  and Rabe, Klaus F.  and Hüttmann, Gereon  and König, Peter },
         
TITLE={Imaging of human airways by endoscope-compatible dynamic microscopic optical coherence tomography},
        
JOURNAL={Frontiers in Medicine},
        
VOLUME={Volume 12 - 2025},

YEAR={2025},

URL={https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1658890},

DOI={10.3389/fmed.2025.1658890},

ISSN={2296-858X},

ABSTRACT={IntroductionMicroscopy is a cornerstone for diagnostics in lung disease but was traditionally restricted to biopsies and explanted tissue. Microscopic optical coherence tomography (mOCT) produces images with microscopic resolution without the need for exogenous markers. As recently demonstrated in excised mouse tissue, the combination with dynamic contrast (dmOCT) generates high contrast images of airway tissue. DmOCT therefore has the potential to be used for virtual biopsies in humans.MethodsTo assess the potential of dmOCT combined with endoscopic imaging, we scanned excised human lung tissue through a custom-built endoscope optic and compared the resulting dmOCT images with conventional histologic sections of the same tissue. We also assessed if imaging time can be substantially reduced while keeping sufficient dmOCT image quality.ResultsEndoscopic dmOCT successfully visualized the epithelium and subepithelial tissue of human airways including smooth muscle cells and glands. The technique detected key structural changes such as inflammatory cell infiltration, basement membrane thickening, epithelial damage, and the transition to carcinoma in situ. In addition, dmOCT distinguished between different morphologies of human lung cancer present in the examined tissue. The image contrast for discriminating these structures remained sufficient even after the acquisition time was reduced to 0.054s.DiscussionWe have shown that dmOCT, when combined with endoscopic optics, reaches the image quality and imaging speed making its use for virtual biopsies in vivo realistic in the future.}}

@article{Heldt:25,
author = {Noah Heldt and Cornelia Holzhausen and Martin Ahrens and Mario Pieper and Peter K\"{o}nig and Gereon H\"{u}ttmann},
journal = {Biomed. Opt. Express},
keywords = {Biomedical imaging; Image metrics; In vivo imaging; Optical imaging; Speckle imaging; Stereo matching},
number = {10},
pages = {4203--4213},
publisher = {Optica Publishing Group},
title = {Improving dOCT image quality with short sequences and automated binning},
volume = {16},
month = {Oct},
year = {2025},
url = {https://opg.optica.org/boe/abstract.cfm?URI=boe-16-10-4203},
doi = {10.1364/BOE.572317},
abstract = {Dynamic optical coherence tomography (dOCT) uses signal fluctuations to contrast different cells and tissues. In this paper, we demonstrate that shortening the time base over which the signal fluctuations are evaluated reduces noise induced by motion while still maintaining a decent image quality. Automatic clustering using the neural-gas algorithm is introduced to optimize the border between the color channels. The performance of the automatic border optimization is demonstrated with 15 different tissue samples by quantitative assessment of motion-induced noise and image quality using the mean squared error (MSE) between images and the image quality parameters peak signal to noise ratio (PSNR) and structural similarity (SSIM).},
}

@inproceedings{Hohl:25,
author = {Svea H\"{o}hl and Tim Eixmann and Martin Ahrens and Noah Heldt and Peter K\"{o}nig and Ori Katz and Gereon H\"{u}ttmann},
booktitle = {European Conferences on Biomedical Optics 2025},
journal = {European Conferences on Biomedical Optics 2025},
keywords = {Biomedical imaging; High speed imaging; Imaging techniques; Multicore fibers; Three dimensional imaging; Tissue imaging},
pages = {W4D.2},
publisher = {Optica Publishing Group},
title = {Holoscopic Microendoscopy},
year = {2025},
url = {https://opg.optica.org/abstract.cfm?URI=ECBO-2025-W4D.2},
doi = {10.1364/ECBO.2025.W4D.2},
abstract = {We present a compact endoscopic holoscopy setup using multicore fibers (MCFs) for three-dimensional imaging of scattering tissues. This method, which integrates holographic recording, enables bend-insensitive imaging, improving the potential for optical biopsies.},
}

@inproceedings{Heldt:25,
author = {Noah Heldt and Martin Ahrens and Mario Pieper and Robert Sch\"{o}nherr and Lucie Jeschke and Peter K\"{o}nig and Marko Lampe and Gereon H\"{u}ttmann},
booktitle = {European Conferences on Biomedical Optics 2025},
journal = {European Conferences on Biomedical Optics 2025},
keywords = {Biomedical imaging; Confocal microscopy; Fourier transforms; Green fluorescent protein; Imaging techniques; Optical coherence tomography},
pages = {S4B.1},
publisher = {Optica Publishing Group},
title = {Investigating dOCT signal origins and bringing a new contrast to fluorescence imaging},
year = {2025},
url = {https://opg.optica.org/abstract.cfm?URI=ECBO-2025-S4B.1},
doi = {10.1364/ECBO.2025.S4B.1},
abstract = {We investigated the dynamic optical coherence tomography (dOCT) signal origins by correlative measurements together with spinning disk confocal microscopy (SDM). Furthermore, we propose dynamic contrasting for detecting organelle movement signatures in the sub-second range.},
}

@inproceedings{10.1117/12.3005413,
author = {Noah Heldt and Cornelia Holzhausen and Martin Ahrens and Mario Pieper and Peter K{\"o}nig and Gereon H{\"u}ttmann},
title = {{Improved image quality in dynamic OCT imaging by reduced imaging time and machine learning based data evaluation}},
volume = {PC12830},
booktitle = {Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVIII},
editor = {Joseph A. Izatt and James G. Fujimoto},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {PC128302A},
abstract = {Dynamic Optical Coherence Tomography combines high resolution tomographic imagery with a cell specific contrast by Fourier analysis. However, the conversion from frequency space into RGB images by binning requires a priori knowledge and artifacts due to global movements provide another obstacle for in vivo application.
We could show that an automated binning based on the Neural Gas algorithm can yield the highest spectral contrast without a priori knowledge and that motion artifacts can be reduced with shorter sequence lengths. Imaging murine airways, we observed that even just 6 frames are enough to generate dOCT images without losing important image information.},
keywords = {Dynamic OCT, Optical Coherence Tomography, Airways, Artificial Intelligence},
year = {2024},
doi = {10.1117/12.3005413},
URL = {https://doi.org/10.1117/12.3005413}
}

@InProceedings{pmlr-v143-gruening21a,
  title = 	 {Direct Inference of Cell Positions using Lens-Free Microscopy and Deep Learning},
  author =       {Gruening, Philipp and Nette, Falk and Heldt, Noah and de Souza, Ana Cristina Guerra and Barth, Erhardt},
  booktitle = 	 {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning},
  pages = 	 {219--227},
  year = 	 {2021},
  editor = 	 {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris},
  volume = 	 {143},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {07--09 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v143/gruening21a/gruening21a.pdf},
  url = 	 {https://proceedings.mlr.press/v143/gruening21a.html},
  abstract = 	 {With in-line holography, it is possible to record biological cells over time in a three-dimensional hydrogel without the need for staining, providing the capability of observing cell behavior in a minimally invasive manner. However, this setup currently requires computationally intensive image-reconstruction algorithms to determine the required cell statistics. In this work, we directly extract cell positions from the holographic data by using deep neural networks and thus avoid several reconstruction steps. We show that our method is capable of substantially decreasing the time needed to extract information from the raw data without loss in quality.}
}