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Synthetic Data in MR Spectroscopy: Current Practices, Applications, and Considerations

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	LaMaster, John T., Gudmundson, Aaron T., Abaei, Alireza, Alcicek, Seyma, Alvarado, Arturo, Andronesi, Ovidiu, Bell, Tiffany K., Bogner, Wolfgang, Bugler, Hanna, Craven, Alexander R, Cudalbu, Cristina, Davidson, Alma, Davies-Jenkins, Christopher W., Deelchand, Dinesh, Edden, Richard A. E., Esmaeili, Morteza, Fleischer, Candace C, Gad, Abdelrahman, Genovese, Guglielmo, Gurbani, Saumya, Harris, Ashley D., Henry, Pierre-Gilles, Igwe, Kay Chioma, Joy, Ajin, Julià-Sapé, Margarida, Kim, Hyeonjin, Kreis, Roland, Lam, Fan, Landheer, Karl, Lanz, Bernard, Lee, Chu-Yu, Ligneul, Clémence, Merkofer, Julian P., Miller, Jack J., Mosso, Jessie, Motyka, Stanislav, Mougel, Eloïse, Mullins, Paul G., Murali-Manohar, Saipavitra, Najac, Chloé, Nakajima, Shinichiro, Oeltzschner, Georg, Ozturk-Isik, Esin, Palombo, Marco, Pilatus, Ulrich, Platek, Justyna, Van Praagh, Emma, Qu, Xiaobo, Rizzo, Rudy, Rodgers, Christopher T., Rodriguez, Esau Poblador, Rodriguez, Yeison, Sammi, Manoj K, van de Sande, Dennis M. J., Sarma, Manoj Kumar, Saviola, Francesca, Schrantee, Anouk, Shamaei, Amirmohammad, Simicic, Dunja, Soher, Brian J, Sollmann, Nico, Song, Yulu, Stanley, Jeffrey A, Strasser, Bernhard, Susnjar, Antonia, Swanberg, Kelley M., Thomas, M. Albert, Tkáč, Ivan, Tu, Zhangren, Weiser, Paul J., Widmaier, Mark, Wilson, Martin, Wu, Christopher J., Xin, Lijing, Zöllner, Helge J., Özdemir, İpek, Group, MRS Synthetic Data Working, Kaiser, Antonia
Format:	Preprint
Publié:	2026
Sujets:	Medical Physics
Accès en ligne:	Acceder al recurso
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_version_	1866908872916074496
author	LaMaster, John T. Gudmundson, Aaron T. Abaei, Alireza Alcicek, Seyma Alvarado, Arturo Andronesi, Ovidiu Bell, Tiffany K. Bogner, Wolfgang Bugler, Hanna Craven, Alexander R Cudalbu, Cristina Davidson, Alma Davies-Jenkins, Christopher W. Deelchand, Dinesh Edden, Richard A. E. Esmaeili, Morteza Fleischer, Candace C Gad, Abdelrahman Genovese, Guglielmo Gurbani, Saumya Harris, Ashley D. Henry, Pierre-Gilles Igwe, Kay Chioma Joy, Ajin Julià-Sapé, Margarida Kim, Hyeonjin Kreis, Roland Lam, Fan Landheer, Karl Lanz, Bernard Lee, Chu-Yu Ligneul, Clémence Merkofer, Julian P. Miller, Jack J. Mosso, Jessie Motyka, Stanislav Mougel, Eloïse Mullins, Paul G. Murali-Manohar, Saipavitra Najac, Chloé Nakajima, Shinichiro Oeltzschner, Georg Ozturk-Isik, Esin Palombo, Marco Pilatus, Ulrich Platek, Justyna Van Praagh, Emma Qu, Xiaobo Rizzo, Rudy Rodgers, Christopher T. Rodriguez, Esau Poblador Rodriguez, Yeison Sammi, Manoj K van de Sande, Dennis M. J. Sarma, Manoj Kumar Saviola, Francesca Schrantee, Anouk Shamaei, Amirmohammad Simicic, Dunja Soher, Brian J Sollmann, Nico Song, Yulu Stanley, Jeffrey A Strasser, Bernhard Susnjar, Antonia Swanberg, Kelley M. Thomas, M. Albert Tkáč, Ivan Tu, Zhangren Weiser, Paul J. Widmaier, Mark Wilson, Martin Wu, Christopher J. Xin, Lijing Zöllner, Helge J. Özdemir, İpek Group, MRS Synthetic Data Working Kaiser, Antonia
author_facet	LaMaster, John T. Gudmundson, Aaron T. Abaei, Alireza Alcicek, Seyma Alvarado, Arturo Andronesi, Ovidiu Bell, Tiffany K. Bogner, Wolfgang Bugler, Hanna Craven, Alexander R Cudalbu, Cristina Davidson, Alma Davies-Jenkins, Christopher W. Deelchand, Dinesh Edden, Richard A. E. Esmaeili, Morteza Fleischer, Candace C Gad, Abdelrahman Genovese, Guglielmo Gurbani, Saumya Harris, Ashley D. Henry, Pierre-Gilles Igwe, Kay Chioma Joy, Ajin Julià-Sapé, Margarida Kim, Hyeonjin Kreis, Roland Lam, Fan Landheer, Karl Lanz, Bernard Lee, Chu-Yu Ligneul, Clémence Merkofer, Julian P. Miller, Jack J. Mosso, Jessie Motyka, Stanislav Mougel, Eloïse Mullins, Paul G. Murali-Manohar, Saipavitra Najac, Chloé Nakajima, Shinichiro Oeltzschner, Georg Ozturk-Isik, Esin Palombo, Marco Pilatus, Ulrich Platek, Justyna Van Praagh, Emma Qu, Xiaobo Rizzo, Rudy Rodgers, Christopher T. Rodriguez, Esau Poblador Rodriguez, Yeison Sammi, Manoj K van de Sande, Dennis M. J. Sarma, Manoj Kumar Saviola, Francesca Schrantee, Anouk Shamaei, Amirmohammad Simicic, Dunja Soher, Brian J Sollmann, Nico Song, Yulu Stanley, Jeffrey A Strasser, Bernhard Susnjar, Antonia Swanberg, Kelley M. Thomas, M. Albert Tkáč, Ivan Tu, Zhangren Weiser, Paul J. Widmaier, Mark Wilson, Martin Wu, Christopher J. Xin, Lijing Zöllner, Helge J. Özdemir, İpek Group, MRS Synthetic Data Working Kaiser, Antonia
contents	The use of synthetic data has emerged as an essential tool in Magnetic Resonance Spectroscopy (MRS) research and applications, providing advantages for optimization of acquisition, software validation, deep learning applications, and enhanced reproducibility. Importantly, synthetic data addresses challenges of limited training data availability, particularly for clinical populations, and offers controlled solutions for investigating uncertainties and unexplained variance with in vivo data. This work provides a review and evaluation of current practices in the use and generation of synthetic data within the MRS field. Conducted by the MRS Synthetic Data Working Group under the Code & Data Sharing Committee of the MRS Study Group of the International Society for Magnetic Resonance in Medicine (ISMRM), this manuscript encompasses existing literature, supplemented by collective experience and in-house methodologies.
format	Preprint
id	arxiv_https___arxiv_org_abs_2602_23463
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Synthetic Data in MR Spectroscopy: Current Practices, Applications, and Considerations LaMaster, John T. Gudmundson, Aaron T. Abaei, Alireza Alcicek, Seyma Alvarado, Arturo Andronesi, Ovidiu Bell, Tiffany K. Bogner, Wolfgang Bugler, Hanna Craven, Alexander R Cudalbu, Cristina Davidson, Alma Davies-Jenkins, Christopher W. Deelchand, Dinesh Edden, Richard A. E. Esmaeili, Morteza Fleischer, Candace C Gad, Abdelrahman Genovese, Guglielmo Gurbani, Saumya Harris, Ashley D. Henry, Pierre-Gilles Igwe, Kay Chioma Joy, Ajin Julià-Sapé, Margarida Kim, Hyeonjin Kreis, Roland Lam, Fan Landheer, Karl Lanz, Bernard Lee, Chu-Yu Ligneul, Clémence Merkofer, Julian P. Miller, Jack J. Mosso, Jessie Motyka, Stanislav Mougel, Eloïse Mullins, Paul G. Murali-Manohar, Saipavitra Najac, Chloé Nakajima, Shinichiro Oeltzschner, Georg Ozturk-Isik, Esin Palombo, Marco Pilatus, Ulrich Platek, Justyna Van Praagh, Emma Qu, Xiaobo Rizzo, Rudy Rodgers, Christopher T. Rodriguez, Esau Poblador Rodriguez, Yeison Sammi, Manoj K van de Sande, Dennis M. J. Sarma, Manoj Kumar Saviola, Francesca Schrantee, Anouk Shamaei, Amirmohammad Simicic, Dunja Soher, Brian J Sollmann, Nico Song, Yulu Stanley, Jeffrey A Strasser, Bernhard Susnjar, Antonia Swanberg, Kelley M. Thomas, M. Albert Tkáč, Ivan Tu, Zhangren Weiser, Paul J. Widmaier, Mark Wilson, Martin Wu, Christopher J. Xin, Lijing Zöllner, Helge J. Özdemir, İpek Group, MRS Synthetic Data Working Kaiser, Antonia Medical Physics The use of synthetic data has emerged as an essential tool in Magnetic Resonance Spectroscopy (MRS) research and applications, providing advantages for optimization of acquisition, software validation, deep learning applications, and enhanced reproducibility. Importantly, synthetic data addresses challenges of limited training data availability, particularly for clinical populations, and offers controlled solutions for investigating uncertainties and unexplained variance with in vivo data. This work provides a review and evaluation of current practices in the use and generation of synthetic data within the MRS field. Conducted by the MRS Synthetic Data Working Group under the Code & Data Sharing Committee of the MRS Study Group of the International Society for Magnetic Resonance in Medicine (ISMRM), this manuscript encompasses existing literature, supplemented by collective experience and in-house methodologies.
title	Synthetic Data in MR Spectroscopy: Current Practices, Applications, and Considerations
topic	Medical Physics
url	https://arxiv.org/abs/2602.23463