High-Resolution Self-Gated Dynamic Abdominal MRI Using Manifold Alignment

TY - JOUR

T1 - High-Resolution Self-Gated Dynamic Abdominal MRI Using Manifold Alignment

AU - Chen, Xin

AU - Usman, Muhammad

AU - Baumgartner, Christian

AU - Balfour, Daniel

AU - Marsden, Paul

AU - Reader, Andrew

AU - Prieto, Claudia

AU - King, Andrew Peter

PY - 2017/1/20

Y1 - 2017/1/20

N2 - We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free-breathing, high spatial and temporal resolution abdominal MRI sequences. Based on a radial golden-angle (RGA) acquisition trajectory, our method enables a multi-dimensional self-gating signal to be extracted from the k-space data for more accurate motion representation. The k-space radial profiles are evenly divided into a number of overlapping groups based on their radial angles. MA is then used to simultaneously learn and align the low dimensional manifolds of all groups, and embed them into a common manifold. In the manifold, k-space profiles that represent similar respiratory positions are close to each other. Image reconstruction is performed by combining radial profiles with evenly distributed angles that are close in the manifold. Our method was evaluated on both 2D and 3D synthetic and in vivo datasets. On the synthetic datasets, our method achieved high correlation with the ground truth in terms of image intensity and virtual navigator values. Using the in vivo data, compared to a state-of-the-art approach based on centre of k-space gating, our method was able to make use of much richer profile data for self-gating, resulting in statistically significantly better quantitative measurements in terms of organ sharpness and image gradient entropy.

AB - We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free-breathing, high spatial and temporal resolution abdominal MRI sequences. Based on a radial golden-angle (RGA) acquisition trajectory, our method enables a multi-dimensional self-gating signal to be extracted from the k-space data for more accurate motion representation. The k-space radial profiles are evenly divided into a number of overlapping groups based on their radial angles. MA is then used to simultaneously learn and align the low dimensional manifolds of all groups, and embed them into a common manifold. In the manifold, k-space profiles that represent similar respiratory positions are close to each other. Image reconstruction is performed by combining radial profiles with evenly distributed angles that are close in the manifold. Our method was evaluated on both 2D and 3D synthetic and in vivo datasets. On the synthetic datasets, our method achieved high correlation with the ground truth in terms of image intensity and virtual navigator values. Using the in vivo data, compared to a state-of-the-art approach based on centre of k-space gating, our method was able to make use of much richer profile data for self-gating, resulting in statistically significantly better quantitative measurements in terms of organ sharpness and image gradient entropy.

U2 - 10.1109/TMI.2016.2636449

DO - 10.1109/TMI.2016.2636449

M3 - Article

C2 - 28113339

SN - 0278-0062

VL - 36

SP - 960

EP - 971

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

IS - 4

M1 - 7828136

ER -