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. 2015 Feb;42(2):534-41.
doi: 10.1118/1.4905044.

Four dimensional magnetic resonance imaging with retrospective k-space reordering: a feasibility study

Yilin Liu  1 , Fang-Fang Yin  1 , Nan-kuei Chen  2 , Mei-Lan Chu  3 , Jing Cai  1
Affiliations

Four dimensional magnetic resonance imaging with retrospective k-space reordering: a feasibility study

Yilin Liu et al. Med Phys. 2015 Feb.

Abstract

Purpose: Current four dimensional magnetic resonance imaging (4D-MRI) techniques lack sufficient temporal/spatial resolution and consistent tumor contrast. To overcome these limitations, this study presents the development and initial evaluation of a new strategy for 4D-MRI which is based on retrospective k-space reordering.

Methods: We simulated a k-space reordered 4D-MRI on a 4D digital extended cardiac-torso (XCAT) human phantom. A 2D echo planar imaging MRI sequence [frame rate (F) = 0.448 Hz; image resolution (R) = 256 × 256; number of k-space segments (NKS) = 4] with sequential image acquisition mode was assumed for the simulation. Image quality of the simulated "4D-MRI" acquired from the XCAT phantom was qualitatively evaluated, and tumor motion trajectories were compared to input signals. In particular, mean absolute amplitude differences (D) and cross correlation coefficients (CC) were calculated. Furthermore, to evaluate the data sufficient condition for the new 4D-MRI technique, a comprehensive simulation study was performed using 30 cancer patients' respiratory profiles to study the relationships between data completeness (Cp) and a number of impacting factors: the number of repeated scans (NR), number of slices (NS), number of respiratory phase bins (NP), NKS, F, R, and initial respiratory phase at image acquisition (P0). As a proof-of-concept, we implemented the proposed k-space reordering 4D-MRI technique on a T2-weighted fast spin echo MR sequence and tested it on a healthy volunteer.

Results: The simulated 4D-MRI acquired from the XCAT phantom matched closely to the original XCAT images. Tumor motion trajectories measured from the simulated 4D-MRI matched well with input signals (D = 0.83 and 0.83 mm, and CC = 0.998 and 0.992 in superior-inferior and anterior-posterior directions, respectively). The relationship between Cp and NR was found best represented by an exponential function (CP=1001-e(-0.18NR) , when NS = 30, NP = 6). At a CP value of 95%, the relative error in tumor volume was 0.66%, indicating that NR at a CP value of 95% (NR,95%) is sufficient. It was found that NR,95% is approximately linearly proportional to NP (r = 0.99), and nearly independent of all other factors. The 4D-MRI images of the healthy volunteer clearly demonstrated respiratory motion in the diaphragm region with minimal motion induced noise or aliasing.

Conclusions: It is feasible to generate respiratory correlated 4D-MRI by retrospectively reordering k-space based on respiratory phase. This new technology may lead to the next generation 4D-MRI with high spatiotemporal resolution and optimal tumor contrast, holding great promises to improve the motion management in radiotherapy of mobile cancers.

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Figures

FIG. 1.
FIG. 1.
Illustration of the process of retrospective 4D-MRI based on k-space reordering. Respiratory phase of each k-space segment is determined from the breathing signal that is acquired synchronously during image acquisition. K-space reordering is performed so that segments of the same respiratory phases are grouped together. Respiratory correlated 4D MR images are generated by reconstructing each phase-specific k-space using iFFT.
FIG. 2.
FIG. 2.
Results of study on data competition condition and its relationships with influencing factors for retrospective k-space reordering 4D-MRI: (a) relationship between CP and NR, (b) relative error in tumor motion measurement as a function of CP.
FIG. 3.
FIG. 3.
The relationship between NR,95% and the following: NP (a), P0 (b), F (c), NS (d), R (e), and NKS (f). NR,95% was found to be approximately linearly proportional to NP (r = 0.99) and independent of all other factors.
FIG. 4.
FIG. 4.
(a) Ten-phase k-space reordering 4D-MRI images of the XCAT phantom. Dashed lines are added to assist the visualization of tumor motion. (b) Comparison of tumor motion trajectories between the 4D-MRI and the input signals. (c) Coronal images of the XCAT phantom illustrating the differences between the k-space reordered 4D-MRI and the original XCAT. Background noise is observed in the k-space reordered 4D-MRI (indicated by arrows).
FIG. 5.
FIG. 5.
Representative 6-phase 4D-MRI images of the healthy volunteer in the axial, sagittal, and coronal views. All images are anatomically near the center of the liver.
FIG. 6.
FIG. 6.
Coronal and sagittal images of the 4D-MRI reconstructed using image-based phase sorting and the k-space reordering methods. Apparent tissue discontinuity on the dome of the diaphragm (indicated by white arrows) are observed on 4D-MRI reconstructed using image-based phase sorting, while 4D-MRI reconstructed using k-space reordering demonstrated much smoother edges.
FIG. 7.
FIG. 7.
Simulated 6-phase 4D-MRI images (top) using the same k-space acquisition scheme and 2D image acquisition mode (interleaves) as in the healthy volunteer study, along with the corresponding motion ranges in amplitude (bottom) for each phase bin.
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