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Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool

Synthetic MR Imaging allows for the reconstruction of different image contrasts from a single acquisition, reducing scan times. Commercial products that implement synthetic MRI are used in research. They rely on vendor-specific acquisitions and do not include the possibility of using custom multipar...

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Autores principales: Peretti, Luca, Donatelli, Graziella, Cencini, Matteo, Cecchi, Paolo, Buonincontri, Guido, Cosottini, Mirco, Tosetti, Michela, Costagli, Mauro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10514862/
https://www.ncbi.nlm.nih.gov/pubmed/37736990
http://dx.doi.org/10.3390/tomography9050137
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author Peretti, Luca
Donatelli, Graziella
Cencini, Matteo
Cecchi, Paolo
Buonincontri, Guido
Cosottini, Mirco
Tosetti, Michela
Costagli, Mauro
author_facet Peretti, Luca
Donatelli, Graziella
Cencini, Matteo
Cecchi, Paolo
Buonincontri, Guido
Cosottini, Mirco
Tosetti, Michela
Costagli, Mauro
author_sort Peretti, Luca
collection PubMed
description Synthetic MR Imaging allows for the reconstruction of different image contrasts from a single acquisition, reducing scan times. Commercial products that implement synthetic MRI are used in research. They rely on vendor-specific acquisitions and do not include the possibility of using custom multiparametric imaging techniques. We introduce PySynthMRI, an open-source tool with a user-friendly interface that uses a set of input images to generate synthetic images with diverse radiological contrasts by varying representative parameters of the desired target sequence, including the echo time, repetition time and inversion time(s). PySynthMRI is written in Python 3.6, and it can be executed under Linux, Windows, or MacOS as a python script or an executable. The tool is free and open source and is developed while taking into consideration the possibility of software customization by the end user. PySynthMRI generates synthetic images by calculating the pixelwise signal intensity as a function of a set of input images (e.g., T1 and T2 maps) and simulated scanner parameters chosen by the user via a graphical interface. The distribution provides a set of default synthetic contrasts, including T1w gradient echo, T2w spin echo, FLAIR and Double Inversion Recovery. The synthetic images can be exported in DICOM or NiFTI format. PySynthMRI allows for the fast synthetization of differently weighted MR images based on quantitative maps. Specialists can use the provided signal models to retrospectively generate contrasts and add custom ones. The modular architecture of the tool can be exploited to add new features without impacting the codebase.
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spelling pubmed-105148622023-09-23 Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool Peretti, Luca Donatelli, Graziella Cencini, Matteo Cecchi, Paolo Buonincontri, Guido Cosottini, Mirco Tosetti, Michela Costagli, Mauro Tomography Communication Synthetic MR Imaging allows for the reconstruction of different image contrasts from a single acquisition, reducing scan times. Commercial products that implement synthetic MRI are used in research. They rely on vendor-specific acquisitions and do not include the possibility of using custom multiparametric imaging techniques. We introduce PySynthMRI, an open-source tool with a user-friendly interface that uses a set of input images to generate synthetic images with diverse radiological contrasts by varying representative parameters of the desired target sequence, including the echo time, repetition time and inversion time(s). PySynthMRI is written in Python 3.6, and it can be executed under Linux, Windows, or MacOS as a python script or an executable. The tool is free and open source and is developed while taking into consideration the possibility of software customization by the end user. PySynthMRI generates synthetic images by calculating the pixelwise signal intensity as a function of a set of input images (e.g., T1 and T2 maps) and simulated scanner parameters chosen by the user via a graphical interface. The distribution provides a set of default synthetic contrasts, including T1w gradient echo, T2w spin echo, FLAIR and Double Inversion Recovery. The synthetic images can be exported in DICOM or NiFTI format. PySynthMRI allows for the fast synthetization of differently weighted MR images based on quantitative maps. Specialists can use the provided signal models to retrospectively generate contrasts and add custom ones. The modular architecture of the tool can be exploited to add new features without impacting the codebase. MDPI 2023-09-11 /pmc/articles/PMC10514862/ /pubmed/37736990 http://dx.doi.org/10.3390/tomography9050137 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Peretti, Luca
Donatelli, Graziella
Cencini, Matteo
Cecchi, Paolo
Buonincontri, Guido
Cosottini, Mirco
Tosetti, Michela
Costagli, Mauro
Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool
title Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool
title_full Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool
title_fullStr Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool
title_full_unstemmed Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool
title_short Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool
title_sort generating synthetic radiological images with pysynthmri: an open-source cross-platform tool
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10514862/
https://www.ncbi.nlm.nih.gov/pubmed/37736990
http://dx.doi.org/10.3390/tomography9050137
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