Abstract
Introduction
The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depends upon the size of training dataset and generalization capabilities of the trained neural network. When there is a mismatch between the training and testing datasets, retraining of the neural networks is required from a scratch which is costly and time consuming.
Materials and methods
A transfer learning approach, i.e., end-to-end fine-tuning is proposed to address the data scarcity and generalization problems of deep learning-based receiver coil sensitivity map estimation. First, generalization capabilities of a pre-trained U-Net (initially trained on 1.5T receiver coil sensitivity maps) are thoroughly assessed for 3T receiver coil sensitivity map estimation. Later, end-to-end fine-tuning is performed on the pre-trained U-Net to estimate the 3T receiver coil sensitivity maps.
Result and conclusion
Peak Signal-to-Noise Ratio, Root Mean Square Error and central line profiles (of the SENSE reconstructed images) show a successful SENSE reconstruction by utilizing the receiver coil sensitivity maps estimated by the proposed method.
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Madiha Arshad declares that she has no conflict of interest. Mahmood Qureshi declares that he has no conflict of interest. Omair Inam declares that he has no conflict of interest. Hammad Omer declares that he has no conflict of interest.
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Arshad, M., Qureshi, M., Inam, O. et al. Transfer learning in deep neural network-based receiver coil sensitivity map estimation. Magn Reson Mater Phy 34, 717–728 (2021). https://doi.org/10.1007/s10334-021-00919-y
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DOI: https://doi.org/10.1007/s10334-021-00919-y