Project

Quantitative MR-based Imaging of Physical Biomarkers (https://quiero-project.eu/)

Goal: With more than 30 million scans per year in European countries, Magnetic Resonance Imaging (MRI) is one of the most important tomographic tools adopted in clinical practice. Nevertheless, standard MRI results mostly have a qualitative nature that limits their objectivity and comparability. The project will evaluate the suitability of two complementary MR-based emerging techniques, Electrical Properties Tomography (EPT) and Magnetic Resonance Fingerprinting (MRF), to bring a “quantitative revolution” in MRI results, so that each image pixel is associated with the measurement (including uncertainty) of one or more tissue parameters.

This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme (EMPIR Grant 18HLT05 QUIERO).

Date: 1 June 2019 - 31 May 2022

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Project log

Luca Zilberti
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EPTlib is the open source, extensible, collection of C++ implementations of Electric Properties Tomography (EPT) methods developed in the framework of the QUIERO project. The new release (https://github.com/EPTlib/eptlib/releases) offers the user the possibility to: Evaluate the variance of the electric conductivity estimated with phase-based Helmholtz EPT; Use a Savitzky-Golay filter with variable degree of the interpolating polynomial; Perform the phase-based Helmholtz EPT with an automatically selected kernel. https://eptlib.github.io/
 
Luca Zilberti
added an update
Respiratory and cardiac motion can strongly impair cardiac Magnetic Resonance Finterprinting (cMRF). Ground truth motion information or T1 and T2 maps are usually not available, making the evaluation and comparison of cMRF approaches difficult. Here we present an open-source simulation framework which overcomes this challenge and provides realistic MR raw data in ISMRMRD format for cMRF and ground truth reference data.   Software available at https://github.com/johannesmayer/SIRF/tree/petmr-simulation Tutorials available at https://github.com/johannesmayer/SIRF-Exercises/tree/simulation-notebooks
 
Luca Zilberti
added an update
This is the third newsletter of our research project "Quantitative MR-based Imaging of Physical Biomarkers".
 
Luca Zilberti
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This is the second newsletter of our research project "Quantitative MR-based Imaging of Physical Biomarkers".
 
Luca Zilberti
added a project reference
Luca Zilberti
added an update
EPTlib is the first open source, extensible, collection of C++ implementations of Electric Property Tomography (EPT) methods. Currently, it includes three EPT implementations, but it will further grow over time. The EPT community is invited to exploit this new tool and to contribute to the development of this promising quantitative MR technique.
 
Luca Zilberti
added an update
This is the first newsletter of our research project "Quantitative MR-based Imaging of Physical Biomarkers".
 
Luca Zilberti
added an update
This short video has been conceived to explain our project to a general audience:
Enjoy it!
 
Luca Zilberti
added an update
This short video has been conceived to explain our project to a general audience:
 
Luca Zilberti
added a project reference
Luca Zilberti
added a project goal
With more than 30 million scans per year in European countries, Magnetic Resonance Imaging (MRI) is one of the most important tomographic tools adopted in clinical practice. Nevertheless, standard MRI results mostly have a qualitative nature that limits their objectivity and comparability. The project will evaluate the suitability of two complementary MR-based emerging techniques, Electrical Properties Tomography (EPT) and Magnetic Resonance Fingerprinting (MRF), to bring a “quantitative revolution” in MRI results, so that each image pixel is associated with the measurement (including uncertainty) of one or more tissue parameters.
This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme (EMPIR Grant 18HLT05 QUIERO).