Incorporating Untrained Neural Network Prior in PROPELLER Imaging

Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) MRI technique enables the correction of motion artifacts resulted from patient motions in a scanner. Undersampling the blades can increase data acquisition speed and reduce potential motions caused by pains in a short time but may degrade image quality. Deep neural networks may support the blade reconstruction with undersampled data but motion patterns are difficult to be acquired for building a training dataset. To avoid the acquisition of training data, this abstract proposes an untrained neural network-based PROPELLER reconstruction technique to enhance image quality with undersampled blades.