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

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.