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Abstract #3816

Cross-frequencies Coil Sensitivity Profile Prediction Using Convolutional Neural Networks: Explorative study

Jiying Dai1,2, Ruben Stoffijn3, Mark Gosselink1, Martijn Froeling1, Alexander J. E. Raaijmakers1,3, and Dennis W. J. Klomp1
1UMC Utrecht, Utrecht, Netherlands, 2Tesla Dynamic Coils B.V., Zaltbommel, Netherlands, 3Eindhoven University of Technology, Eindhoven, Netherlands

Synopsis

Keywords: AI/ML Image Reconstruction, Image Reconstruction, cross-frequency B1 prediction

Motivation: Coil sensitivity profiles are essential for multi-channel MRI/MRSI data processing, yet not acquirable within reasonable scan time for X-nuclei with low natural abundance.

Goal(s): Predict sensitivity patterns of lowly abundant X-nuclear species based on sensitivities of highly abundant nuclei acquired by the same multi-tuned coil array.

Approach: We scanned 8 subjects at 1.5T and 3T using similar commercial head arrays. A 3D patch-based convolutional neural network is used to predict 3T sensitivity patterns from 1.5T sensitivities.

Results: Predicted 3T sensitivity patterns show high similarity to the ground truth. 3T signal-combination is feasible using the 1.5T-based predicted sensitivities, despite subject repositioning and hardware deviation.

Impact: An adequate prediction of coil sensitivity profiles at 128MHz based on 64MHz sensitivity profiles using highly similar receiver arrays was achieved. It opens up new possibilities for combining multi-channel signals acquired by multi-tuned (e.g., 31P-23Na, 19F-1H, etc.) receiver arrays.

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Keywords