Advanced Techniques in Optical Diffraction Tomography

Ahmed Bassam Sayed Ayoub Mohamed Emam
EPFL, 2022
EPFL thesis

Abstract

In this thesis, we study the 3 challenges described above. First, we study different reconstruction techniques and assess the fidelity of each reconstruction results by means of structured illumination and phase conjugation. By reconstructing the 3D refractive index of the sample using different algorithms (i.e. Born, Rytov, and Radon) and then perform a numerical back-propagation of experimentally measured structured illumination pattern we are able to assess the fidelity of each reconstruction algorithms without prior information about the 3D RI distribution of the sample.The second part of the thesis is concerned with the 3D reconstruction of samples using intensity-only measurements which the need to holographically acquire them. We show that using intensity-only measurements, we could still be able to reconstruct the 3D volume of the sample with edge-enhanced effects which was proven useful for drug delivery applications in which nano-particles were identified on the cell membrane of immune T-cells in a drug delivery studies. Such reconstruction technique would result in more robust imaging system where the commercial imaging microscope systems can be incorporated with LEDs for high-quality speckle noise-free imaging systems. In addition, we show that under certain conditions, we can be able to reconstruct the 3D refractive index distribution of different samples.The third part of the thesis is contributing to high-speed complex wave-front shaping using DMDs. In that part, new modulation technique is demonstrated that can boost the speed of the current time-multiplexing techniques by a factor of 32. The modulation technique is based on amplitude modulation where an amplitude modulator is synchronized withvthe DMD to modulate the intensity of each bit-plane of an 8-bit image and then all the modulated bit-planes are linearly added on the detector. Such modulation technique can be used not only for structured illumination microscopy but also for high-speed 3D printing applications as well as projectors.The last part is concerned with using deep learning approaches to solve the missing cone problem usually accompanied with optical imaging due to the limited numerical aperture of the imaging system. Two techniques are discussed; the first is based on using a physical model to enhance the quality of the 3D RI reconstruction and the second is based on using deep neural network to solve the missing cone problem.

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https://infoscience.epfl.ch/record/295389?ln=en

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Ahmed Bassam Sayed Ayoub Mohamed Emam
EPFL, 2022
EPFL thesis

Abstract

Official source

https://infoscience.epfl.ch/record/295389?ln=en

About this result

Related concepts (26)

Related concepts

Related publications (30)

Related publications

Related publications (30)

3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

Morteza Hasani Shoreh

Optical tomography has been widely investigated for biomedical imaging applications. In recent years, it has been combined with digital holography and has been employed to produce high quality images of phase objects such as cells. In this Thesis, we look into some of the newest optical Diffraction Tomography (DT) based techniques to solve Three-Dimensional (3D) reconstruction problems and discuss and compare some of the leading ideas and papers. Then we propose a neural-network-based algorithm to solve this problem and apply it on both synthetic and biological samples. Conventional phase tomography with coherent light and off axis recording is performed. The Beam Propagation Method (BPM) is used to model scattering and each x-y plane is modeled by a layer of neurons in the BPM. The network's output (simulated data) is compared to the experimental measurements and the error is used for correcting the weights of the neurons (the refractive indices of the nodes) using standard error back-propagation techniques. The proposed algorithm is detailed and investigated. Then, we look into resolution-conserving regularization and discuss a method for selecting regularizing parameters. In addition, the local minima and phase unwrapping problems are discussed and ways of avoiding them are investigated. It is shown that the proposed learning tomography (LT) achieves better performance than other techniques such as, DT especially when insufficient number or incomplete set of measurements is available. We also explore the role of regularization in obtaining higher fidelity images without losing resolution. It is experimentally shown that due to overcoming multiple scattering, the LT reconstruction greatly outperforms the DT when the sample contains two or more layers of cells or beads. Then, reconstruction using intensity measurements is investigated. 3D reconstruction of a live cell during apoptosis is presented in a time-lapse format. At the end, we present a final comparison with leading papers and commercially available systems. It is shown that -compared to other existing algorithms- the results of the proposed method have better quality. In particular, parasitic granular structures and the missing cone artifact are improved. Overall, the perspectives of our approach are pretty rich for high-resolution tomographic imaging in a range of practical applications.

EPFL2018

Learning approaches to high-fidelity optical diffraction tomography

Joowon Lim

Optical diffraction tomography (ODT) provides us 3D refractive index (RI) distributions of transparent samples. Since RI values differ across different materials, they serve as endogenous contrasts. It, therefore, enables us to image without pre-processing of labeling which can disturb samples during measurement. It has been utilized in various applications to study hematology, morphological parameters, biochemical information, and so on. The fundamental principle of ODT reconstruction is to recover the 3D information from multiple 2D measurements. While we require 2D measurements acquired by fully scanning a sample, there exist missing measurements that we are not able to access due to the limited numerical apertures (NAs) in the optical system. This is called the missing cone problem since the parts which are not covered by the NAs form cone shapes. The missing cone problem degrades the final reconstruction by underestimating RI values and more severely elongating images along the optical axis. Another challenge in ODT reconstruction is to model the nonlinear relationship between a sample and the measurements. The first order of scattering is commonly considered while neglecting the other higher orders to linearize the relationship, however, this results in degradation of the final reconstruction as the higher orders of scattering become more pronounced. In this thesis, we aim at solving the challenges in ODT reconstruction to provide more accurate quantitative information, namely, RI distributions. The first approach is based on model-based iterative reconstruction schemes. We choose the beam propagation method (BPM) for the forward model in order to capture the high orders of scattering. Due to the similarity of the multi-layer structure of the BPM with that of neural networks used in deep learning, we call this scheme learning tomography (LT). We rigorously investigate the performance of LT over the conventional linear model-based reconstruction scheme. Furthermore, by applying a more advanced BPM for the forward model, we even improve the LT and demonstrate the dramatically improved performance by both simulations and experiments. The second approach is based on statistically learning artifacts present in final reconstructions using a deep neural network (DNN) from a large dataset. Unlike the previous approaches which require iterations, the DNN approach instantly reconstructs RI distributions. We demonstrate the use of DNN using red blood cells which are highly distorted by the missing cone problem. In order to overcome the lack of ground truth in 3D ODT reconstruction, we digitally generate a synthetic dataset. The reconstruction results from the network present highly accurate results for the synthetic test set. Most importantly, we obtain high-fidelity reconstructions of experimental data using the network trained only on the synthetic data. Unlike other imaging modalities, ODT provides 3D quantitative information without labeling. To fully benefit from the capacity of quantitative imaging, it is critical to solve the existing challenges in ODT reconstruction to produce high-fidelity reconstructions. In this contribution, we aim to resolve the major challenges in ODT reconstruction using various learning approaches, and we believe that it can further improve ODT as a powerful tool for various applications.

EPFL2020

Fiber endoscopy plays an important role in the clinical diagnosis and treatment processes involved in modern medicine. Thin fiber probes can relay information from confined places in the human body that are inaccessible for conventional bulky microscopes. Therefore, they can provide a minimally invasive platform for optical diagnosis assisting the fast recovery of patients. Aiming for more compact fiber endoscope devices, we investigate different types of fibers that can integrate not only superior imaging modalities but also microsurgery capabilities while maintaining an ultrathin size (less than 400um). In our experiments, the imaging potential of multimode and multicore fibers is assessed. On the one hand, multimode fibers provide high information capacity for small core sizes but scramble the input field after its propagation through the fiber length, while on the other hand, multicore fibers are the most common choice for endoscopic probes since they are able to directly relay images from the inspection location to the observation side. However, the final images produced by multicore fibers are of poor quality because of the discretization effect induced by the individual core sampling and the final resolution is related to the core spacing. We show that each fiber type can be selected based on the desired application and the imaging result can be significantly improved. Two approaches for imaging through the different fibers are investigated. In the first part of the present thesis, wavefront shaping using the transmission matrix approach to generate a focus spot at the distal fiber side is presented. The limitations concerning the maximum peak intensity guided through the different endoscopes is investigated for high power femtosecond pulses where nonlinear optical phenomena can hinder the overall performance of the system. Femtosecond laser ablation is demonstrated through multimode fibers for a range of materials. Furthermore, laser ablation is combined with two-photon fluorescence imaging in the same multimode fiber endoscope showing for the first time selective tissue modifications at a cellular level. In the second part of this thesis, deep learning of light propagation through the two fiber types is studied. Datasets of known input images and their respective fiber output images are generated to train deep neural network algorithms to map the fiber output to the fiber input for either classification or image reconstruction purposes. The deep neural networks show impressive performance to recover the information from intensity-only images of the speckle patterns emerging from multimode fibers, removing the need to record the full field information. Moreover, deep learning for multimode fiber imaging proved to be resilient to perturbations related to mechanical, thermal and even wavelength drifts introduced during the measurements. In the case of multicore fibers, deep neural networks are trained to remove pixelation artifacts from the final image and resolve features with an improved resolution. Finally, deep learning is employed to transform a bright field imaging-based commercial endoscope to a phase contrast imaging probe by training deep neural networks to map the intensity-only recorded image to its corresponding phase map.

EPFL2020

3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

Morteza Hasani Shoreh

EPFL2018

Learning approaches to high-fidelity optical diffraction tomography

Joowon Lim

EPFL2020