Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool.
magnetic resonance imaging
software tool
synthetic imaging
Journal
Tomography (Ann Arbor, Mich.)
ISSN: 2379-139X
Titre abrégé: Tomography
Pays: Switzerland
ID NLM: 101671170
Informations de publication
Date de publication:
11 09 2023
11 09 2023
Historique:
received:
17
08
2023
revised:
07
09
2023
accepted:
08
09
2023
medline:
25
9
2023
pubmed:
22
9
2023
entrez:
22
9
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37736990
pii: tomography9050137
doi: 10.3390/tomography9050137
pmc: PMC10514862
doi:
Substances chimiques
Contrast Media
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1723-1733Références
Magn Reson Imaging. 2018 Feb;46:56-63
pubmed: 29103975
AJNR Am J Neuroradiol. 2017 Feb;38(2):257-263
pubmed: 27932506
IEEE Trans Med Imaging. 1999 Nov;18(11):1085-97
pubmed: 10661326
Acta Radiol. 2012 Dec 1;53(10):1158-63
pubmed: 23024181
J Magn Reson Imaging. 2022 Apr;55(4):1013-1025
pubmed: 33188560
Magn Reson Med. 2010 Jul;64(1):186-93
pubmed: 20577987
Br J Radiol. 1994 Dec;67(804):1258-63
pubmed: 7874427
Sci Rep. 2020 Aug 13;10(1):13769
pubmed: 32792618
Magn Reson Med. 2020 Nov;84(5):2606-2615
pubmed: 32368835
Eur Radiol. 2012 May;22(5):998-1007
pubmed: 22113264
J Magn Reson. 2005 Mar;173(1):97-115
pubmed: 15705518
PLoS One. 2014 Apr 16;9(4):e93689
pubmed: 24740285
Neuroradiology. 2014 Jul;56(7):517-23
pubmed: 24763967
J Comput Assist Tomogr. 1992 Nov-Dec;16(6):841-4
pubmed: 1430427
Med Image Anal. 2022 Apr;77:102387
pubmed: 35180675
AJNR Am J Neuroradiol. 2019 Feb;40(2):224-230
pubmed: 30630834
Neuroimage. 2010 Jan 15;49(2):1271-81
pubmed: 19819338
J Cardiovasc Magn Reson. 2014 Dec 20;16:102
pubmed: 25526880
J Neuroradiol. 2019 Jul;46(4):268-275
pubmed: 30853545
J Neuroradiol. 2020 Mar;47(2):151-160
pubmed: 30951770
Nature. 2013 Mar 14;495(7440):187-92
pubmed: 23486058
Magn Reson Imaging. 2004 Apr;22(3):315-28
pubmed: 15062927
Magn Reson Med. 1990 Jul;15(1):152-7
pubmed: 2374495
AJNR Am J Neuroradiol. 2017 Jun;38(6):1103-1110
pubmed: 28450439
IEEE Trans Med Imaging. 2017 Feb;36(2):527-537
pubmed: 28113746
J Magn Reson Imaging. 2017 Dec;46(6):1590-1600
pubmed: 28419602
Eur Radiol. 2002 Apr;12(4):920-7
pubmed: 11960249
Invest Radiol. 2017 Oct;52(10):647-657
pubmed: 28257339