Context and problematics:
Tomographic Diffractive Microscopy (TDM) is a quantitative optical phase imaging technique, using coherent illumination, that enables high-resolution observation of microscopic unlabeled samples [1]. Using an image reconstruction process, it provides a 3-D map of the complex optical index (refraction and absorption) of the observed sample, providing morphological information complementary to the metabolic phenomena observed using fluorescence techniques. Measurements consist in recording interferograms (diffraction patterns) while varying the illuminations (by rotating the object, illumination sweeping, or combining both approaches) to perform tomographic reconstructions. Holographic microscopy can be considered as a simplified version of TDM, using only one illumination. Although less resolving than recent superresolution approaches (STED, PALM/STORM), TDM enables a wider field of view to be imaged at high resolution (twice better than holographic microscopy), with much lower data acquisition and processing times. Moreover, its ability to observe and characterize unlabeled samples makes it a promising technique for studying in 3D living cells and tissues in the context of biomedical research.

The IRIMAS laboratory (Mulhouse) has built such a 3D microscope [2, 3], which has demonstrated its ability to reach
an isotropic 3-D resolution in the 100 nm range. A project involving a collaboration between IRIMAS, the Hubert Curien laboratory (Saint-Étienne) aimed at improving this imaging technique in terms of instrumentation and image processing for reconstruction. In this context, the Hubert Curien laboratory’s team managed to develop an advanced iterative reconstruction algorithm involving accurate image formation models and automatic tuning of the algorithms’s parameters [4]. The code has been implemented in Matlab®. The algorithm has shown proof of concept and has been compared with state-of-the-art methods, from pre-processed data provided by IRIMAS. It now requires in-depth studies to evaluate quantitatively its performances, for example regarding the accuracy of the used image formation model, or depending on the number and the distribution of the multi-angle views. The objective is that our algorithm can be deployed for practical use by the researchers at IRIMAS. Moreover, for a better efficiency, the reconstruction method has to be adapted to process the raw data (without a pre-processing step) from the IRIMAS microscope.

This internship proposes to address these above mentioned objectives. The recruited trainee will have to perform studies exploiting the existing Matlab® code. The objectives are to :

• validate the existing model and reconstruction method on in-depth simulation studies :
  • define the varying parameters of the studies ;
  • create the simulation datasets ;
  • develop and implement the reconstruction scripts in Matlab® and save results ;
  • analyze the reconstructions by defining and calculating evaluation metrics, and plot graphs
    and curves of these metrics as a function of the varying simulation parameters ;
• adapt the reconstruction method to process the raw data from the IRIMAS microscope ;
• validate the reconstruction method on experimental data provided by IRIMAS.

This work will be a milestone for the next step of the collaboration between IRIMAS and Hubert Curien laboratory: build a hyperspectral TDM microscope to develop a highly resolved, highly specific, label-free 3D hyperspectral optical imaging technique.

We are looking for a candidate in master 2 or in last year of an engineering school in computer science and/or computer vision and/or signal and image processing, comfortable with programming in the targeted language (knowledges in Python would also be appreciated), and interested in imaging problems (reconstruction, simulation), particularly for scientific research.

The proposed internship is particularely adapted for a student who plans to pursue a PhD thesis in
the domain of biomedical imaging.

Required skills: signal and image processing, computer vision, 2D/3D image reconstruction (tomography), Matlab
® programming.
Appreciated knowledge and interests: Inverse problems, optics, imaging, Python programming, scientific research
/ project to pursue a PhD thesis.

References
[1] O. Haeberl´e, K. Belkebir, H. Giovaninni, and A. Sentenac. Tomographic diffractive microscopy: basics, techniques and perspectives. Journal of Modern Optics, 57(9):686–699, May 2010.
[2] Matthieu Debailleul, Bertrand Simon, Vincent Georges, Olivier Haeberl´e, and Vincent Lauer. Holographic
microscopy and diffractive microtomography of transparent samples. Measurement Science and Technology, 19(7):074009, 2008.
[3] Matthieu DEBAILLEUL and Bruno COLICCHIO. Imagerie microscopique 3d de phase m´ethode d’imagerie sans marquage. Techniques de l’Ing´enieur, ref. article : p955, 2018.
[4] L. Denneulin, F. Momey, D. Brault, M. Debailleul, A. M. Taddese, N. Verrier, and O. Haeberl´e. Gsure criterion for
unsupervised regularized reconstruction in tomographic diffractive microscopy. J. Opt. Soc. Am. A, 39(2):A52–A61, Feb 2022.