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Recent quantitative parameter mapping methods including MR fingerprinting(MRF) collect a time series of images that capture the evolution ofmagnetization. The focus of this work is to introduce a novel approach termedas Deep Factor Model(DFM), which offers an efficient representation of themulti-contrast image time series. The higher efficiency of the representationenables the acquisition of the images in a highly undersampled fashion, whichtranslates to reduced scan time in 3D high-resolution multi-contrastapplications. The approach integrates motion estimation and compensation,making the approach robust to subject motion during the scan.

Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI