EPTlib: An Open-Source Extensible Collection of Electric Properties Tomography Techniques

Arduino, Alessandro

doi:10.3390/app11073237

Cited by 12 publications

(10 citation statements)

References 47 publications

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“…An additional advantage of ADEPT is that, by providing ready‐to‐use data, it will lower the threshold for new research groups to start with research on MR‐EPT. This works in great synergy with the availability of reconstruction algorithms such as the ones provided in EPTlib 36 …”

Section: Discussionmentioning

confidence: 99%

A database for MR‐based electrical properties tomography with in silico brain data—ADEPT

Meerbothe,

Meliado,

Stijnman

et al. 2023

Magnetic Resonance in Med

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PurposeSeveral 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.MethodsADEPT 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.ResultsElectromagnetic 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.ConclusionADEPT 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.

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Section: Discussionmentioning

confidence: 99%

A database for MR‐based electrical properties tomography with in silico brain data—ADEPT

Meerbothe,

Meliado,

Stijnman

et al. 2023

Magnetic Resonance in Med

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“…Here we provide a brief overview of two standard ones, namely the Helmholtz EPT [18] and the Convection-Reaction EPT [20]. Open-source software implementations of these methods can be found in EPTlib [44].…”

Section: B Standard Ept Methodsmentioning

confidence: 99%

“…For this example, we compared the proposed PIFON-EPT with two standard EPT methods widely used in similar MRI setups involving transmission and reception with birdcage coils, namely the Helmholtz-EPT (H-EPT) and the Convection-Reaction EPT (CR-EPT). We used EPTlib [44] to perform H-EPT and CR-EPT and reconstruct the conductivity maps using the transceive phase.…”

Section: B Concentric Cylindrical Phantommentioning

confidence: 99%

PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks

Yu¹,

Serralles²,

Giannakopoulos³

et al. 2023

Preprint

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In this paper, we introduce Physics-Informed Fourier Networks (PIFONs) for Electrical Properties (EP) Tomography (EPT). Our novel deep learning-based method is capable of learning EPs globally by solving an inverse scattering problem based on noisy and/or incomplete magnetic resonance (MR) measurements. Methods: We use two separate fully-connected neural networks, namely B + 1 Net and EP Net, to learn the B + 1 field and EPs at any location. A random Fourier features mapping is embedded into B + 1 Net, which allows it to learn the B + 1 field more efficiently. These two neural networks are trained jointly by minimizing the combination of a physicsinformed loss and a data mismatch loss via gradient descent. Results: We showed that PIFON-EPT could provide physically consistent reconstructions of EPs and transmit field in the whole domain of interest even when half of the noisy MR measurements of the entire volume was missing. The average error was 2.49%, 4.09% and 0.32% for the relative permittivity, conductivity and B + 1 , respectively, over the entire volume of the phantom. In experiments that admitted a zero assumption of Bz, PIFON-EPT could yield accurate EP predictions near the interface between regions of different EP values without requiring any boundary conditions. Conclusion: This work demonstrated the feasibility of PIFON-EPT, suggesting it could be an accurate and effective method for electrical properties estimation. Significance: PIFON-EPT can efficiently de-noise MR measurements, which shows the potential to improve other MR-based EPT techniques. Furthermore, it is the first time that MR-based EPT methods can reconstruct the EPs and B + 1 field simultaneously from incomplete simulated noisy MR measurements.Index terms-Electrical Property Mapping, Physics Informed Neural Networks, Fourier Features Mapping. I. INTRODUCTIONElectrical properties (EP), namely relative permittivity and electric conductivity, determine the interactions between electromagnetic waves and biological tissue [1], [2]. EPs have the potential to be employed as biomarkers for pathologies

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“…Voxel-wise reconstructed electrical conductivity values were obtained from the phase of B + 1 using the convection-reaction EPT (CR-EPT) approach implemented in EPTlib [19], [20]. For all the models, the reconstruction was performed using athreedimensional cross-shaped Savitzky-Golay filter with size (1,1,1, 7 voxels in total), Dirichlet boundary condition of 0.1 S/m, and artificial diffusion coefficient of 0.001 rd.…”

Section: A Electrical Conductivity Based On Eptmentioning

confidence: 99%

Evaluation and Correction of $B_{1}^+$-Based Brain Subject-Specific SAR Maps Using Electrical Properties Tomography

Arduino

Bottauscio

et al. 2023

IEEE J. Electromagn. RF Microw. Med. Biol.

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The specific absorption rate (SAR) estimates the amount of power absorbed by the tissue and is determined by the electrical conductivity and the E-field. Conductivity can be estimated using Electric Properties Tomography (EPT) but only the E-field component associated with B + 1 can be deduced from B 1mapping. Herein, a correction factor was calculated to compensate for the differences between the actual SAR and the one obtained with B + 1 . Numerical simulations were performed for 27 head models at 128 MHz. Ground-truth local-SAR and 10g-SAR (SAR GT ) were computed using the exact electrical conductivity and the E-field. Estimated local-SAR and 10g-SAR (SAR EST ) were computed using the electrical conductivity obtained with a convectionreaction EPT and the E-field obtained from B + 1 . Correction factors (CFs) were estimated for gray matter, white matter, and cerebrospinal fluid (CSF). A comparison was performed for different levels of signal-to-noise ratios (SNR). Local-SAR/10g-SAR CF was 3.08 ± 0/06 / 2.11 ± 0.04 for gray matter, 1.79 ± 0/05 / 2.06 ± 0.04 for white matter, and 2.59 ± 0/05 / 1.95 ± 0.03 for CSF. SAR EST without CF were underestimated (ratio across [∞ -25] SNRs: 0.52 ± 0.02 for local-SAR; 0.55 ± 0.01 for 10g-SAR). After correction, SAR EST was equivalent to SAR GT (ratio across [∞ -25] SNRs: 0.97 ± 0.02 for local-SAR; 1.06 ± 0.01 for 10g-SAR). SAR maps based on B + 1 can be corrected with a correction factor to compensate for potential differences between the actual SAR and the SAR calculated with the E-field derived from B + 1 .

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EPTlib: An Open-Source Extensible Collection of Electric Properties Tomography Techniques

Cited by 12 publications

References 47 publications

A database for MR‐based electrical properties tomography with in silico brain data—ADEPT

A database for MR‐based electrical properties tomography with in silico brain data—ADEPT

PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks

Evaluation and Correction of $B_{1}^+$-Based Brain Subject-Specific SAR Maps Using Electrical Properties Tomography

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