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
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

1723-1733

Références

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

Auteurs

Luca Peretti (L)

Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, 56128 Pisa, Italy.
Imago 7 Research Foundation, 56128 Pisa, Italy.
Department of Computer Science, University of Pisa, 56127 Pisa, Italy.

Graziella Donatelli (G)

Imago 7 Research Foundation, 56128 Pisa, Italy.
Department of Diagnostic and Interventional Radiology and Nuclear Medicine, Azienda Ospedaliero-Universitaria Pisana, 56124 Pisa, Italy.

Matteo Cencini (M)

Italian National Institute of Nuclear Physics (INFN), Section of Pisa, 56127 Pisa, Italy.

Paolo Cecchi (P)

Imago 7 Research Foundation, 56128 Pisa, Italy.
Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Guido Buonincontri (G)

Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, 56128 Pisa, Italy.

Mirco Cosottini (M)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Michela Tosetti (M)

Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, 56128 Pisa, Italy.

Mauro Costagli (M)

Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, 56128 Pisa, Italy.
Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, 16132 Genoa, Italy.

Articles similaires

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Vancomycin-associated DRESS demonstrates delay in AST abnormalities.

Ahmed Hussein, Kateri L Schoettinger, Jourdan Hydol-Smith et al.
1.00
Humans Drug Hypersensitivity Syndrome Vancomycin Female Male
Humans Immune Checkpoint Inhibitors Lung Neoplasms Prognosis Inflammation

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Classifications MeSH