Dynamic three‐dimensional undersampled data reconstruction employing temporal registration

Dynamic 3D imaging is needed for many applications such as imaging of the heart, joints, and abdomen. For these, the contrast and resolution that magnetic resonance imaging (MRI) offers are desirable. Unfortunately, the long acquisition time of MRI limits its application. Several techniques have been proposed to shorten the scan time by undersampling the k-space. To recover the missing data they make assumptions about the object's motion, restricting it in space, spatial frequency, temporal frequency, or a combination of space and temporal frequency. These assumptions limit the applicability of each technique. In this work we propose a reconstruction technique based on a weaker complementary assumption that restricts the motion in time. The technique exploits the redundancy of information in the object domain by predicting time frames from frames where there is little motion. The proposed method is well suited for several applications, in particular for cardiac imaging, considering that the heart remains relatively still during an important fraction of the cardiac cycle, or joint imaging where the motion can easily be controlled. This paper presents the new technique and the results of applying it to knee and cardiac imaging. The results show that the new technique can effectively reconstruct dynamic images acquired with an undersampling factor of 5. The resulting images suffer from little temporal and spatial blurring, significantly better than a sliding window reconstruction. An important attraction of the technique is that it combines reconstruction and registration, thus providing not only the 3D images but also its motion quantification. The method can be adapted to non-Cartesian k-space trajectories and nonuniform undersampling patterns.