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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.
Afiliação
  • Peretti L; Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, 56128 Pisa, Italy.
  • Donatelli G; Imago 7 Research Foundation, 56128 Pisa, Italy.
  • Cencini M; Department of Computer Science, University of Pisa, 56127 Pisa, Italy.
  • Cecchi P; Imago 7 Research Foundation, 56128 Pisa, Italy.
  • Buonincontri G; Department of Diagnostic and Interventional Radiology and Nuclear Medicine, Azienda Ospedaliero-Universitaria Pisana, 56124 Pisa, Italy.
  • Cosottini M; Italian National Institute of Nuclear Physics (INFN), Section of Pisa, 56127 Pisa, Italy.
  • Tosetti M; Imago 7 Research Foundation, 56128 Pisa, Italy.
  • Costagli M; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.
Tomography ; 9(5): 1723-1733, 2023 09 11.
Article em En | MEDLINE | ID: mdl-37736990
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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia Tipo de estudo: Observational_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia Tipo de estudo: Observational_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article