Tomographic reconstruction

Science de la nature
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Optique
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Publications associées (27)

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Proton reconstruction with the CMS-TOTEM Precision Proton Spectrometer

Matthias Finger, Konstantin Androsov, Jan Steggemann, Qian Wang, Yiming Li, Anna Mascellani, Varun Sharma, Xin Chen, Arvind Shah, Rakesh Chawla

The Precision Proton Spectrometer (PPS) of the CMS and TOTEM experiments collected 107.7 fb 1 in proton-proton (pp) collisions at the LHC at 13 TeV(Run 2). This paper describes the key features of the PPS alignment and optics calibrations, the proton recon ...

Iop Publishing Ltd2023

Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures

Marianne Liebi, Manuel Guizar Sicairos

The development of small-angle scattering tensor tomography has enabled the study of anisotropic nanostructures in a volume-resolved manner. It is of great value to have reconstruction methods that can handle many different nanostructural symmetries. For s ...

Int Union Crystallography2023

Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits

Manuel Guizar Sicairos, Yi Jiang

Three-dimensional inspection of nanostructures such as integrated circuits is important for security and reliability assurance. Two scanning operations are required: ptychographic to recover the complex transmissivity of the specimen, and rotation of the s ...

2023

LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry Pi

Martin Vetterli, Eric Bezzam, Sepand Kashani, Matthieu Martin Jean-André Simeoni

This toolkit has everything you need to perform imaging with a lensless camera. We make use of a low-cost implementation of DiffuserCam, namely replacing the lens with a piece of tape and using the Raspberry Pi HQ camera sensor. Similar principles and meth ...

Zenodo2023

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Michaël Unser, Thanh-An Michel Pham, Jonathan Yuelin Dong

Phase retrieval consists in the recovery of a complex-valued signal from intensity-only measurements. As it pervades a broad variety of applications, many researchers have striven to develop phase-retrieval algorithms. Classical approaches involve techniqu ...

2023

Towards ‘Fourth Paradigm’ Spectral Sensing

Marilyne Andersen, Forrest Simon Webler, Manuel Spitschan

Reconstruction algorithms are at the forefront of accessible and compact data collection. In this paper, we present a novel reconstruction algorithm, SpecRA, that adapts based on the relative rarity of a signal compared to previous observations. We leverag ...

2022

2.5 Hz sample rate time-domain near-infrared optical tomography based on SPAD-camera image tissue hemodynamics

Edoardo Charbon, Scott Anthony Lindner, Martin Wolf, Jingjing Jiang

Tim-domain near-infrared optical tomography (TD NIROT) techniques based on diffuse light were gaining performance over the last years. They are capable of imaging tissue at several centimeters depth and reveal clinically relevant information, such as tissu ...

OPTICAL SOC AMER2022

Advanced Techniques in Optical Diffraction Tomography

Ahmed Bassam Sayed Ayoub Mohamed Emam

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 refrac ...

EPFL2022

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima’s Replica Formula)

Florent Gérard Krzakala, Alia Abbara

There has been a recent surge of interest in the study of asymptotic reconstruction performance in various cases of generalized linear estimation problems in the teacher-student setting, especially for the case of i.i.d standard normal matrices. Here, we g ...

2022

Physics-informed neural networks for diffraction tomography

Demetri Psaltis, Carlo Gigli, Amirhossein Saba Shirvan, Ahmed Ayoub

We propose a physics-informed neural network (PINN) as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field ...

SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS2022

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