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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