Bloch-informed neural network for multi-scanner MR Harmonization

For multi-site research in MR studies, a harmonization process is necessary. Due to the lack of paired dataset for harmonization, the unsupervised-based learning has been suggested. However, this method is susceptible to causing the error in anatomical information and performing well in the unseen domain. Thus, we suggest a deep neural network for multi-site harmonization based on MR signal physics with the paired traveling dataset. We implemented Quantitative Maps Generators and Denoising Network with the Bloch Equation module. We proved that using the Bloch Equation enhances the accuracy of harmonization.