Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm
Abstract In this research, we predict the output signal generated by iron oxide-based nanoparticles in Magnetic Resonance Imaging (MRI) using the physical properties of the nanoparticles and the MRI machine. The parameters considered include the size of the magnetic core of the nanoparticles, their...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01994-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849763590152650752 |
|---|---|
| author | Fatemeh Hataminia Anahita Azinfar |
| author_facet | Fatemeh Hataminia Anahita Azinfar |
| author_sort | Fatemeh Hataminia |
| collection | DOAJ |
| description | Abstract In this research, we predict the output signal generated by iron oxide-based nanoparticles in Magnetic Resonance Imaging (MRI) using the physical properties of the nanoparticles and the MRI machine. The parameters considered include the size of the magnetic core of the nanoparticles, their magnetic saturation (Ms), the concentration of the nanoparticles (C), and the magnetic field (MF) strength of the MRI device. These parameters serve as input variables for the model, while the relaxation rate R2 (s-1) is taken as the output variable. To develop this model, we employed a machine learning approach based on a neural network known as SA-LOOCV-GRBF (SLG). In this study, we compared two different random selection patterns: SLG disperse random selection (DSLG) and SLG parallel random selection (PSLG). The sensitivity to neuron number in the hidden layers for DSLG was more pronounced compared to the PSLG pattern, and the mean square error (MSE) was calculated for this evaluation. It appears that the PSLG method demonstrated strong performance while maintaining less sensitivity to increasing neuron numbers. Consequently, the new pattern, PSLG, was selected for predicting MRI behavior. |
| format | Article |
| id | doaj-art-94b03d73233545ef870dcb45e6790b52 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-94b03d73233545ef870dcb45e6790b522025-08-20T03:05:22ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-01994-0Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithmFatemeh Hataminia0Anahita Azinfar1Department of Radiology, School of Medicine, Mashhad University of Medical SciencesSchool of Medicine, Mashhad University of Medical SciencesAbstract In this research, we predict the output signal generated by iron oxide-based nanoparticles in Magnetic Resonance Imaging (MRI) using the physical properties of the nanoparticles and the MRI machine. The parameters considered include the size of the magnetic core of the nanoparticles, their magnetic saturation (Ms), the concentration of the nanoparticles (C), and the magnetic field (MF) strength of the MRI device. These parameters serve as input variables for the model, while the relaxation rate R2 (s-1) is taken as the output variable. To develop this model, we employed a machine learning approach based on a neural network known as SA-LOOCV-GRBF (SLG). In this study, we compared two different random selection patterns: SLG disperse random selection (DSLG) and SLG parallel random selection (PSLG). The sensitivity to neuron number in the hidden layers for DSLG was more pronounced compared to the PSLG pattern, and the mean square error (MSE) was calculated for this evaluation. It appears that the PSLG method demonstrated strong performance while maintaining less sensitivity to increasing neuron numbers. Consequently, the new pattern, PSLG, was selected for predicting MRI behavior.https://doi.org/10.1038/s41598-025-01994-0Iron oxide nanoparticlesMagnetic resonance imaging (MRI)Machine learningRBF neural network |
| spellingShingle | Fatemeh Hataminia Anahita Azinfar Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm Scientific Reports Iron oxide nanoparticles Magnetic resonance imaging (MRI) Machine learning RBF neural network |
| title | Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm |
| title_full | Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm |
| title_fullStr | Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm |
| title_full_unstemmed | Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm |
| title_short | Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm |
| title_sort | utilizing machine learning to predict mri signal outputs from iron oxide nanoparticles through the pslg algorithm |
| topic | Iron oxide nanoparticles Magnetic resonance imaging (MRI) Machine learning RBF neural network |
| url | https://doi.org/10.1038/s41598-025-01994-0 |
| work_keys_str_mv | AT fatemehhataminia utilizingmachinelearningtopredictmrisignaloutputsfromironoxidenanoparticlesthroughthepslgalgorithm AT anahitaazinfar utilizingmachinelearningtopredictmrisignaloutputsfromironoxidenanoparticlesthroughthepslgalgorithm |