In clinical practice, magnetic resonance imaging (MRI) with multiple contrasts is usually acquired in a single study to assess different properties of the same region of interest in human body. The whole acquisition process can be accelerated by having one or more modalities under-sampled in the k-space.
Our early research (Xiang et al.) demonstrates that, considering the redundancy between different contrasts or modalities, a target MRI modality under-sampled in the k-space can be better reconstructed with the helps from a fully-sampled sequence (i.e., the reference modality). It implies that, in the same study of the same subject, multiple sequences can be utilized together toward the purpose of highly efficient multi-modal reconstruction.
However, we find that multi-modal reconstruction can be negatively affected by subtle spatial misalignment between different sequences, which is actually common in clinical practice. Thus, we aim to integrate the spatial alignment network with reconstruction in this work, to improve the quality of the reconstructed target modality.
Specifically, the spatial alignment network estimates the spatial misalignment between the fully-sampled reference and the under-sampled target images, and warps the reference image accordingly. Then, the aligned fully-sampled reference image joins the under-sampled target image in the reconstruction network, to produce the high-quality target image. Our experiments on both clinical MRI and multi-coil k-space raw data demonstrate the superiority and robustness of our spatial alignment network.