| Autor: |
Meerbothe, T. G., Meliado, E. F., Stijnman, P. R. S., van den Berg, C. A. T., Mandija, S. |
| Předmět: |
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| Zdroj: |
Magnetic Resonance in Medicine; Mar2024, Vol. 91 Issue 3, p1190-1199, 10p |
| Abstrakt: |
Purpose: Several reconstruction methods for MR‐based electrical properties tomography (EPT) have been developed. However, the lack of common data makes it difficult to objectively compare their performances. This is, however, a necessary precursor for standardizing and introducing this technique in the clinical setting. To enable objective comparison of the performances of reconstruction methods and provide common data for their training and testing, we created ADEPT, a database of simulated data for brain MR‐EPT reconstructions. Methods: ADEPT is a database containing in silico data for brain EPT reconstructions. This database was created from 25 different brain models, with and without tumors. Rigid geometric augmentations were applied, and different electrical properties were assigned to white matter, gray matter, CSF, and tumors to generate 120 different brain models. These models were used as input for finite‐difference time‐domain simulations in Sim4Life, used to compute the electromagnetic fields needed for MR‐EPT reconstructions. Results: Electromagnetic fields from 84 healthy and 36 tumor brain models were simulated. The simulated fields relevant for MR‐EPT reconstructions (transmit and receive RF fields and transceive phase) and their ground‐truth electrical properties are made publicly available through ADEPT. Additionally, nonattainable fields such as the total magnetic field and the electric field are available upon request. Conclusion: ADEPT will serve as reference database for objective comparisons of reconstruction methods and will be a first step toward standardization of MR‐EPT reconstructions. Furthermore, it provides a large amount of data that can be exploited to train data‐driven methods. It can be accessed from https://doi.org/10.34894/V0HBJ8. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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