Subtraction images are an important part of routine multi-phase contrast-enhanced MRI for characterizing enhancement of lesions which are intrinsically hyperintense on T1-weighted imaging. Successful subtraction MRI is dependent on precise 3D co-registration of the pre- and contrast-enhanced source data. However, there still lack a robust, convenient, time efficient and labor free method for automatically image subtraction. This study developed a deep learning based nonrigid registration algorithm, measure the improvement in displacement after registration using anatomic landmarks and automatically generate the subtraction liver images.
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