MR electrical properties mapping using vision transformers and canny edge detectors

We developed a 3D vision transformer-based neural network to reconstruct electrical properties (EP) from magnetic resonance measurements. Our network uses the magnitude of the transmit magnetic field of a birdcage coil, the associated transceive phase, and a Canny edge mask that identifies the objec...

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Bibliographic Details
Published in:	Magnetic resonance in medicine
Main Authors:	Giannakopoulos, Ilias I, Carluccio, Giuseppe, Keerthivasan, Mahesh B, Koerzdoerfer, Gregor, Lakshmanan, Karthik, De Moura, Hector L, Cruz Serrallés, José E, Lattanzi, Riccardo
Format:	Journal Article
Language:	English
Published:	United States 16-10-2024
Subjects:	vision transformers transfer learning electrical properties mapping electromagnetic simulations
Online Access:	Get full text
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Summary:	We developed a 3D vision transformer-based neural network to reconstruct electrical properties (EP) from magnetic resonance measurements. Our network uses the magnitude of the transmit magnetic field of a birdcage coil, the associated transceive phase, and a Canny edge mask that identifies the object boundaries as inputs to compute the EP maps. We trained our network on a dataset of 10 000 synthetic tissue-mimicking phantoms and fine-tuned it on a dataset of 11 000 realistic head models. We assessed performance in-distribution simulated data and out-of-distribution head models, with and without synthetic lesions. We further evaluated our network in experiments for an inhomogeneous phantom and a volunteer. The conductivity and permittivity maps had an average peak normalized absolute error (PNAE) of 1.3% and 1.7% for the synthetic phantoms, respectively. For the realistic heads, the average PNAE for the conductivity and permittivity was 1.8% and 2.7%, respectively. The location of synthetic lesions was accurately identified, with reconstructed conductivity and permittivity values within 15% and 25% of the ground-truth, respectively. The conductivity and permittivity for the phantom experiment yielded 2.7% and 2.1% average PNAEs with respect to probe-measured values, respectively. The in vivo EP reconstruction truthfully preserved the subject's anatomy with average values over the entire head similar to the expected literature values. We introduced a new learning-based approach for reconstructing EP from MR measurements obtained with a birdcage coil, marking an important step towards the development of clinically-usable in vivo EP reconstruction protocols.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0740-3194 1522-2594 1522-2594
DOI:	10.1002/mrm.30338