Robust 4D-MRI Sorting with Reduced Artifacts Based on Anatomic Feature Matching

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Purpose: Motion artifacts induced by breathing variations are common in 4D-MRI

images. This study aims to reduce the motion artifacts by developing a novel, robust 4DMRI

sorting method based on anatomic feature matching, which is applicable in both

cine and sequential acquisition.

Method: The proposed method uses the diaphragm as the anatomic feature to guide the

sorting of 4D-MRI images. Initially, both abdominal 2D sagittal cine MRI images and

axial MRI images (in both axial cine and sequential scanning modes) were acquired. The

sagittal cine MRI images were divided into 10 phases as ground truth. Next, the phase of

each axial MRI image is determined by matching the diaphragm position in the

intersection plane between the axial MRI and the ground truth cine MRI. Then, those

matched phases axial MRI images were sorted into 10-phase bins identical to the ground

truth cine images. Finally, 10-phase 4D-MRI were reconstructed from these sorted axial

MRI images. The accuracy of reconstructed 4D-MRI data was evaluated in a simulation

study using the 4D eXtended Cardiac Torso (XCAT) digital phantom with a sphere

tumor in the liver. The effects of breathing signal, including both regular (cosine

function) and irregular (patient data), on reconstruction accuracy were investigated by

calculating total relative error (TRE) of the 4D volumes, Volume-Percent-Difference

(VPD) and Center-of-Mass-Shift(COMS) of the simulated tumor between the

reconstructed and the ground truth images.

Results: In both scanning modes, reconstructed 4D-MRI images matched well with the

ground truth except minimal motion artifacts. The averaged TRE of the 4D volume, VPD

and COMS of the EOE phase in both scanning modes were 0.32%/1.20%/±0.05𝑚𝑚 for

regular breathing, and 1.13%/4.26%/±0.21𝑚𝑚 for patient irregular breathing,


Conclusion: The preliminary results illustrated the robustness of the new 4D-MRI

sorting method based on anatomic feature matching. This method improved image

quality with reduced motion artifacts in the resulting reconstructed 4D MRI is applicable

for axial MR images acquired using both cine and sequential scanning modes.





Yang, Zi (2018). Robust 4D-MRI Sorting with Reduced Artifacts Based on Anatomic Feature Matching. Master's thesis, Duke University. Retrieved from


Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.