MRI Imputation based on Fused Index- and Intensity-Registration

3D MRI imaging is based on a number of imaging sequences such as T1, T2, T1ce, and Flair, and each of them is performed by a group of two-dimensional scans. In practical MRI, some scans are often missing while many medical applications require a full set of scans. An MRI imputation method is presented, which synthesizes such missing scans. Key components in this method are the index registration and the intensity registration. The index registration models anatomical differences between two different scans in the same imaging sequence, and the intensity registration reflects the image contrast differences between two different scans of the same index. Two registration fields are learned to be invariant, and accordingly, allow two estimates of a missing scan, one within corresponding imaging sequence and another along scan index; the two estimates are combined to yield the final synthesized scan. Experimental results highlight that the proposed method improves prevalent limitations existing in previous synthesis methods, blending both structural and contrast aspects and capturing subtle parts of the brain. Quantitative results also show the superiority in various data sets, transitions, and measures.