{"title":"PyEPRI:一个CPU和GPU兼容python包电子顺磁共振成像","authors":"Rémy Abergel , Sylvain Durand , Yves-Michel Frapart","doi":"10.1016/j.jmr.2025.107891","DOIUrl":null,"url":null,"abstract":"<div><div>This work presents the PyEPRI package, an open-source Python package for Electron Paramagnetic Resonance Imaging. The PyEPRI package implements low-level operators, like projection and backprojection, involved in Electron Paramagnetic Resonance (EPR) and also high-level advanced algorithms, like total variation based EPR image reconstruction, for end-users. The package is fully implemented in Python and provides both CPU and GPU computation capabilities, through the libraries Numpy, PyTorch and Cupy. This package comes with a detailed documentation, including precise mathematical definitions and many reproducible demonstration examples and tutorials, making it easy for users with no particular expertise on coding image processing algorithms to get started. This package is also highly modular and only relies on standard data types, as such, it can also be easily used by advanced users to develop new algorithms while benefiting from an optimized computing environment and some rigorously tested operators.</div></div>","PeriodicalId":16267,"journal":{"name":"Journal of magnetic resonance","volume":"376 ","pages":"Article 107891"},"PeriodicalIF":1.9000,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PyEPRI: A CPU & GPU compatible python package for electron paramagnetic resonance imaging\",\"authors\":\"Rémy Abergel , Sylvain Durand , Yves-Michel Frapart\",\"doi\":\"10.1016/j.jmr.2025.107891\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This work presents the PyEPRI package, an open-source Python package for Electron Paramagnetic Resonance Imaging. The PyEPRI package implements low-level operators, like projection and backprojection, involved in Electron Paramagnetic Resonance (EPR) and also high-level advanced algorithms, like total variation based EPR image reconstruction, for end-users. The package is fully implemented in Python and provides both CPU and GPU computation capabilities, through the libraries Numpy, PyTorch and Cupy. This package comes with a detailed documentation, including precise mathematical definitions and many reproducible demonstration examples and tutorials, making it easy for users with no particular expertise on coding image processing algorithms to get started. This package is also highly modular and only relies on standard data types, as such, it can also be easily used by advanced users to develop new algorithms while benefiting from an optimized computing environment and some rigorously tested operators.</div></div>\",\"PeriodicalId\":16267,\"journal\":{\"name\":\"Journal of magnetic resonance\",\"volume\":\"376 \",\"pages\":\"Article 107891\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of magnetic resonance\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1090780725000631\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of magnetic resonance","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1090780725000631","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
PyEPRI: A CPU & GPU compatible python package for electron paramagnetic resonance imaging
This work presents the PyEPRI package, an open-source Python package for Electron Paramagnetic Resonance Imaging. The PyEPRI package implements low-level operators, like projection and backprojection, involved in Electron Paramagnetic Resonance (EPR) and also high-level advanced algorithms, like total variation based EPR image reconstruction, for end-users. The package is fully implemented in Python and provides both CPU and GPU computation capabilities, through the libraries Numpy, PyTorch and Cupy. This package comes with a detailed documentation, including precise mathematical definitions and many reproducible demonstration examples and tutorials, making it easy for users with no particular expertise on coding image processing algorithms to get started. This package is also highly modular and only relies on standard data types, as such, it can also be easily used by advanced users to develop new algorithms while benefiting from an optimized computing environment and some rigorously tested operators.
期刊介绍:
The Journal of Magnetic Resonance presents original technical and scientific papers in all aspects of magnetic resonance, including nuclear magnetic resonance spectroscopy (NMR) of solids and liquids, electron spin/paramagnetic resonance (EPR), in vivo magnetic resonance imaging (MRI) and spectroscopy (MRS), nuclear quadrupole resonance (NQR) and magnetic resonance phenomena at nearly zero fields or in combination with optics. The Journal''s main aims include deepening the physical principles underlying all these spectroscopies, publishing significant theoretical and experimental results leading to spectral and spatial progress in these areas, and opening new MR-based applications in chemistry, biology and medicine. The Journal also seeks descriptions of novel apparatuses, new experimental protocols, and new procedures of data analysis and interpretation - including computational and quantum-mechanical methods - capable of advancing MR spectroscopy and imaging.