Publications related to Multi-modal Latent-Space Self-alignment for Super-Resolution Cardiac MR Segmentation

Related publications (40)

Live-cell imaging powered by computation

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Nature Portfolio2024

Event-based sampled ECG morphology reconstruction through self-similarity

David Atienza Alonso, Giovanni Ansaloni, Tomas Teijeiro Campo, Silvio Zanoli

Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with ...

2023

OSSCAR, an open platform for collaborative development of computational tools for education in science

Giovanni Pizzi, Sara Bonella, Dou Du

In this paper we present the Open Software Services for Classrooms and Research (OSSCAR) platform. OSSCAR provides an open collaborative environment to develop and access educational resources in the form of web applications, for which various deployment m ...

ELSEVIER2023

A comprehensive and biophysically detailed computational model of the whole human heart electromechanics

Alfio Quarteroni

While ventricular electromechanics is extensively studied in both physiological and pathological conditions, four-chamber heart models have only been addressed recently; most of these works however neglect atrial contraction. Indeed, as atria are character ...

ELSEVIER SCIENCE SA2023

Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI

Erick Jorge Canales Rodriguez, Marco Pizzolato

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherica ...

ELSEVIER2023

On the logic of collapsibility for causal effect measures

Mats Julius Stensrud

WILEY2022

Learning to sample in Cartesian MRI

Thomas Sanchez

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

Prospective motion correction in kidney MRI using FID navigators

Tobias Kober

Purpose Abdominal MRI scans may require breath-holding to prevent image quality degradation, which can be challenging for patients, especially children. In this study, we evaluate whether FID navigators can be used to measure and correct for motion prospec ...

WILEY2022

3-Dimensional Fluid and White Matter Suppression Magnetic Resonance Imaging Sequence Accelerated With Compressed Sensing Improves Multiple Sclerosis Cervical Spinal Cord Lesion Detection Compared With Standard 2-Dimensional Imaging

Tobias Kober

Objectives Fluid and white matter suppression (FLAWS) is a recently proposed magnetic resonance sequence derived from magnetization-prepared 2 rapid acquisition gradient-echo providing 2 coregistered datasets with white matter- and cerebrospinal fluid-supp ...

LIPPINCOTT WILLIAMS & WILKINS2022

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Thomas Yu

Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...

EPFL2022

Medipix4, a high granularity four sides buttable pixel readout chip for high resolution spectroscopic X-ray imaging at rates compatible with medical CT scans

Viros Sriskaran

Medipix4 is the latest member in the Medipix/Timepix family of pixel detector chips aimed at high rate spectroscopic X-ray imaging using high-Z materials. The chip address the limitations of conventional hybrid pixel detectors for X-ray imaging. Its predec ...

EPFL2022

Diamond-Based Nanoscale Quantum Relaxometry for Sensing Free Radical Production in Cells

Mayeul Sylvain Chipaux, Hoda Shirzad

Diamond magnetometry makes use of fluorescent defects in diamonds to convert magnetic resonance signals into fluorescence. Because optical photons can be detected much more sensitively, this technique currently holds several sensitivity world records for r ...

WILEY-V C H VERLAG GMBH2022

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Alfio Quarteroni

In order to simulate the cardiac function for a patient-specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry o ...

Wiley2021

Micrometer-resolution X-ray tomographic full-volume reconstruction of an intact post-mortem juvenile rat lung

Goran Lovric

In this article, we present an X-ray tomographic imaging method that is well suited for pulmonary disease studies in animal models to resolve the full pathway from gas intake to gas exchange. Current state-of-the-art synchrotron-based tomographic phase-con ...

SPRINGER2021

Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction

Tobias Kober, Meritxell Bach Cuadra, Patric Hagmann, Reto Meuli, Tom Hilbert, Thomas Yu

Accurate characterization of in utero human brain maturation is critical as it involves complex interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool complementary to the u ...

SPRINGER INTERNATIONAL PUBLISHING AG2021

Homogeneous multifocal excitation for high-throughput super-resolution imaging

Davide Gambarotto, Pierre Gönczy, Suliana Manley, Paul Guichard, Dora Mahecic, Kyle Michael Douglass, Niccolo Banterle, Virginie Hamel, Khalid Ashraf Mohie Ibrahim

Super-resolution microscopies have become an established tool in biological research. However, imaging throughput remains a main bottleneck in acquiring large datasets required for quantitative biology. Here we describe multifocal flat illumination for fie ...

Nature Publishing Group2020

Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Dimitri Nestor Alice Van De Ville, Maxime Descoteaux, Nawal Noëlle Kinany

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject da ...

NATURE PORTFOLIO2021

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data

Eilif Benjamin Muller, Werner Alfons Hilda Van Geit, Armando Romani

Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophys ...

2021

Model-informed machine learning for multi-component T2 relaxometry

Jean-Philippe Thiran, Tobias Kober, Meritxell Bach Cuadra, Tom Hilbert, Erick Jorge Canales Rodriguez, Cristina Granziera, Muhamed Barakovic, Marco Pizzolato, Elda Fischi Gomez, Thomas Yu, Gian Franco Piredda

Recovering the distribution from multi-echo magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to comb ...

2020

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Guillaume Marc Georges Vray, Devavrat Tomar, Manana Lortkipanidze

Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality m ...

Institute of Electrical and Electronics Engineers2021