Free-breathing abdominal MRI improved by Repeated k-t-subsampling and artifact-minimization (ReKAM)

Abstract

Purpose: We report an approach, termed Repeated k-t-subsampling and artifact-minimization (ReKAM), for removing motion artifacts in free-breathing abdominal MRI. The method is particularly valuable for challenging patients who may not hold their breath for a long time or have irregular respiratory rate. Methods: The ReKAM framework comprises one acquisition module and two reconstruction modules. A fast MRI sequence is used to repeatedly acquire multiple sets of k-t space data. Motion artifacts are then minimized by two reconstruction modules: (a) a bootstrapping module in k-t-space is used to identify a low-artifact image; (b) a constrained reconstruction module that integrates projection onto convex set (POCS) and multiplexed sensitivity encoding (MUSE), termed POCSMUSE, is applied to further remove residual artifact. The ReKAM framework is compatible with different pulse sequences, and generally applicable to irregular data sampling patterns in k-space. Free-breathing fast spin-echo MRI data, acquired from healthy volunteers and patients, were used to evaluate the developed ReKAM method. Results: Experimental results show that the ReKAM technique can produce high-quality free-breathing images with the artifact levels comparable to that of breath-holding MRI. Conclusion: The ReKAM framework improves the quality of free-breathing abdominal MRI data, and is compatible with various MRI pulse sequences. © 2017 American Association of Physicists in Medicine.