System and method for real-time image registration during radiotherapy using deep learning

Abstract

This invention provides a deep learning (DL) model for fast deformable image registration using 2D sagittal cine MRI acquired during radiation therapy. A DL model for fast deformable image registration is trained using cine MRI scans acquired during MR-Linac treatments of thoracic and abdominal tumors. The model uses a pair of cine MRI images as inputs and outputs a dense motion vector field (MVF) which aligns the images. The trained model is applied to predict frame by frame motion from cine MRIs in which both cardiac and respiratory motion are visible. The number of respirations and heart beats is automatically extracted by performing peak detection on high-frequency and low-frequency components of the MVF displacements corresponding to the chest wall and cardiac regions.

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