Constrained regularized reconstruction of X-ray-DPCI tomograms with weighted-norm

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Learning to sample in Cartesian MRI

Magnetic Resonance Imaging (MRI) is a non-invasive, non-ionizing imaging modality with unmatched soft tissue contrast. However, compared to imaging methods like X-ray radiography, MRI suffers from long scanning times, due to its inherently sequential acqui ...

EPFL2022

Polarization-sensitive optics diffraction tomography

Demetri Psaltis, Ahmed Ayoub, Joowon Lim, Elizabeth Elena Antoine, Amirhossein Saba Shirvan

Polarization of light has been widely used as a contrast mechanism in two-dimensional (2D) microscopy and also in some three-dimensional (3D) imaging modalities. In this paper, we report the 3D tomographic reconstruction of the refractive index (RI) tensor ...

2021

3D reconstruction of weakly scattering objects from 2D intensity-only measurements using the Wolf transform

Demetri Psaltis, Ahmed Ayoub, Joowon Lim, Elizabeth Elena Antoine

A new approach to optical diffraction tomography (ODT) based on intensity measurements is presented. By applying the Wolf transform directly to intensity measurements, we observed unexpected behavior in the 3D reconstruction of the sample. Such a reconstru ...

2021

Ultrasound Imaging: From Physical Modeling to Deep Learning

Dimitris Perdios

Among the medical imaging modalities, ultrasound (US) imaging is one of the safest, most widespread, and least expensive method used in medical diagnosis. In the past decades, several technological advances enabled the advent of ultrafast US imaging, an ac ...

EPFL2021

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging

Matthias Lütolf, Michaël Unser, Nathalie Brandenberg, Thanh-An Michel Pham, Fangshu Yang

Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the comput ...

2021

In-operando dynamic visualization of flow through porous preforms based on X-ray phase contrast imaging

Véronique Michaud, Baris Çaglar, Helena Luisa Teixido Pedarros

Direct visualization is often sought to elucidate flow patterns and validate models to predict the filling kinetics during processes whereby a liquid resin infiltrates a textile porous preform. Here, X-ray phase contrast interferometry is evaluated to imag ...

2021

Weakly Supervised Deep Learning Methods for Biomicroscopy

Adrian Shajkofci

Optical microscopy, an invaluable tool in biology and medicine to observe and quantify cellular function, organ development, or disease mechanisms, requires constant trade-offs between spatial, temporal, and spectral resolution, invasiveness, acquisition t ...

EPFL2021

Quantitative Microstructural Imaging for Clinical Use

Gian Franco Piredda

Today, Magnetic Resonance Imaging (MRI) is a well-established medical imaging modality thanks to its excellent soft tissue contrast. Conventional MR image contrasts can be weighted towards one or more physical properties that discriminate different tissues ...

EPFL2021

Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods

Michaël Unser, Dimitris Perdios, Sébastien Alexandre Emery, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon

Ill-posed linear inverse problems are frequently encountered in image reconstruction tasks. Image reconstruction methods that combine the Plug-and-Play (PnP) priors framework with convolutional neural network (CNN) based denoisers have shown impressive per ...

2021

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