Deformable Registration-Based Super-resolution for Isotropic Reconstruction of 4-D MRI Volumes

Multi-plane super-resolution (SR) has been widely employed for resolution improvement of MR images. However, this has mostly been limited to MRI acquisitions with rigid motion. In cases of non-rigid motion, volumes are usually pre-registered using deformable registration methods before SR reconstruction. The pre-registered images are then used as input for the SR reconstruction. Since deformable registration involves smoothening of the inputs, using pre-registered inputs could lead to loss in information in SR reconstructions. Additionally, any registration errors present in pre-registered inputs could propagate throughout SR reconstructions leading to error accumulation. To address these limitations, in this study, we propose a deformable registration-based super-resolution reconstruction (DIRSR) reconstruction, which handles deformable registration as part of super-resolution. This approach has been demonstrated using 12 synthetic 4-D MRI lung datasets created using single plane (coronal) datasets of six patients and multi-plane (coronal and axial) 4-D lung MRI dataset of one patient. From our evaluation, DIRSR reconstructions are sharper and well aligned compared to reconstructions using SR of pre-registered inputs and rigid-registration SR. MSE, SNR and SSIM evaluations also indicate better reconstruction quality from DIRSR compared to reconstructions from SR of pre-registered inputs (p-value less than 0.0001). In conclusion, we found superior isotropic reconstructions of 4-D MR datasets from DIRSR reconstructions, which could benefit volumetric MR analyses.