Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver
© 2020 International Society for Magnetic Resonance in Medicine Purpose: We propose a fast, patient-specific workflow for on-line specific absorption rate (SAR) supervision. An individualized electromagnetic model is created while the subject is on the table, followed by rapid SAR estimates for that...
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| Format: | Article |
| Language: | English |
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Wiley
2022
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| Online Access: | https://hdl.handle.net/1721.1/138811 |
| _version_ | 1826192938633265152 |
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| author | Milshteyn, Eugene Guryev, Georgy Torrado-Carvajal, Angel Adalsteinsson, Elfar White, Jacob K Wald, Lawrence L Guerin, Bastien |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Milshteyn, Eugene Guryev, Georgy Torrado-Carvajal, Angel Adalsteinsson, Elfar White, Jacob K Wald, Lawrence L Guerin, Bastien |
| author_sort | Milshteyn, Eugene |
| collection | MIT |
| description | © 2020 International Society for Magnetic Resonance in Medicine Purpose: We propose a fast, patient-specific workflow for on-line specific absorption rate (SAR) supervision. An individualized electromagnetic model is created while the subject is on the table, followed by rapid SAR estimates for that individual. Our goal is an improved correspondence between the patient and model, reducing reliance on general anatomical body models. Methods: A 3D fat-water 3T acquisition (~2 minutes) is automatically segmented using a computer vision algorithm (~1 minute) into what we found to be the most important electromagnetic tissue classes: air, bone, fat, and soft tissues. We then compute the individual’s EM field exposure and global and local SAR matrices using a fast electromagnetic integral equation solver. We assess the approach in 10 volunteers and compare to the SAR seen in a standard generic body model (Duke). Results: The on-the-table workflow averaged 7′44″. Simulation of the simplified Duke models confirmed that only air, bone, fat, and soft tissue classes are needed to estimate global and local SAR with an error of 6.7% and 2.7%, respectively, compared to the full model. In contrast, our volunteers showed a 16.0% and 20.3% population variability in global and local SAR, respectively, which was mostly underestimated by the Duke model. Conclusion: Timely construction and deployment of a patient-specific model is computationally feasible. The benefit of resolving the population heterogeneity compared favorably to the modest modeling error incurred. This suggests that individualized SAR estimates can improve electromagnetic safety in MRI and possibly reduce conservative safety margins that account for patient-model mismatch, especially in non-standard patients. |
| first_indexed | 2024-09-23T09:31:10Z |
| format | Article |
| id | mit-1721.1/138811 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:31:10Z |
| publishDate | 2022 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1388112024-03-19T17:44:03Z Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver Milshteyn, Eugene Guryev, Georgy Torrado-Carvajal, Angel Adalsteinsson, Elfar White, Jacob K Wald, Lawrence L Guerin, Bastien Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Institute for Medical Engineering & Science Harvard University--MIT Division of Health Sciences and Technology © 2020 International Society for Magnetic Resonance in Medicine Purpose: We propose a fast, patient-specific workflow for on-line specific absorption rate (SAR) supervision. An individualized electromagnetic model is created while the subject is on the table, followed by rapid SAR estimates for that individual. Our goal is an improved correspondence between the patient and model, reducing reliance on general anatomical body models. Methods: A 3D fat-water 3T acquisition (~2 minutes) is automatically segmented using a computer vision algorithm (~1 minute) into what we found to be the most important electromagnetic tissue classes: air, bone, fat, and soft tissues. We then compute the individual’s EM field exposure and global and local SAR matrices using a fast electromagnetic integral equation solver. We assess the approach in 10 volunteers and compare to the SAR seen in a standard generic body model (Duke). Results: The on-the-table workflow averaged 7′44″. Simulation of the simplified Duke models confirmed that only air, bone, fat, and soft tissue classes are needed to estimate global and local SAR with an error of 6.7% and 2.7%, respectively, compared to the full model. In contrast, our volunteers showed a 16.0% and 20.3% population variability in global and local SAR, respectively, which was mostly underestimated by the Duke model. Conclusion: Timely construction and deployment of a patient-specific model is computationally feasible. The benefit of resolving the population heterogeneity compared favorably to the modest modeling error incurred. This suggests that individualized SAR estimates can improve electromagnetic safety in MRI and possibly reduce conservative safety margins that account for patient-model mismatch, especially in non-standard patients. 2022-01-04T19:51:57Z 2022-01-04T19:51:57Z 2021 2022-01-04T19:45:25Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/138811 Milshteyn, Eugene, Guryev, Georgy, Torrado-Carvajal, Angel, Adalsteinsson, Elfar, White, Jacob K et al. 2021. "Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver." Magnetic Resonance in Medicine, 85 (1). en 10.1002/MRM.28398 Magnetic Resonance in Medicine Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley PMC |
| spellingShingle | Milshteyn, Eugene Guryev, Georgy Torrado-Carvajal, Angel Adalsteinsson, Elfar White, Jacob K Wald, Lawrence L Guerin, Bastien Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver |
| title | Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver |
| title_full | Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver |
| title_fullStr | Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver |
| title_full_unstemmed | Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver |
| title_short | Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver |
| title_sort | individualized sar calculations using computer vision based mr segmentation and a fast electromagnetic solver |
| url | https://hdl.handle.net/1721.1/138811 |
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