4D-MRI
Respiratory motion-resolved, self-gated 4D-MRI using Rotating Cartesian K-space (ROCK): Initial clinical experience on an MRI-guided radiotherapy system

https://doi.org/10.1016/j.radonc.2018.04.029Get rights and content

Abstract

Purpose

To optimize and evaluate the respiratory motion-resolved, self-gated 4D-MRI using Rotating Cartesian K-space (ROCK-4D-MRI) method in a 0.35 T MRI-guided radiotherapy (MRgRT) system.

Methods and materials

The study included seven patients with abdominal tumors treated on the MRgRT system. ROCK-4D-MRI and 2D-CINE, was performed immediately after one of the treatment fractions. Motion quantification based on 4D-MRI was compared with those based on 2D-CINE. The image quality of 4D-MRI was evaluated against 4D-CT. The gross tumor volumes (GTV) were defined based on individual respiratory phases of both 4D-MRI and 4D-CT and compared for their variability over the respiratory cycle.

Result

The motion measurements based on 4D-MRI matched well with 2D-CINE, with differences of 1.04 ± 0.52 mm in the superior–inferior and 0.54 ± 0.21 mm in the anterior–posterior directions. The image quality scores of 4D-MRI were significantly higher than 4D-CT, with better tumor contrast (3.29 ± 0.76 vs. 1.86 ± 0.90) and less motion artifacts (3.57 ± 0.53 vs. 2.29 ± 0.95). The GTVs were more consistent in 4D-MRI than in 4D-CT, with significantly smaller GTV variability (9.31 ± 4.58% vs. 34.27 ± 23.33%).

Conclusion

Our study demonstrated the clinical feasibility of using the ROCK-4D-MRI to acquire high quality, respiratory motion-resolved 4D-MRI in a low-field MRgRT system. The 4D-MRI image could provide accurate dynamic information for radiotherapy treatment planning.

Section snippets

MRI pulse sequence and image reconstruction

We modified a standard bSSFP sequence using the ROCK 3D Cartesian k-space reordering method and incorporated repetitively sampled k-space centerline for SG and for retrospective data binning. The acquired k-space data were binned into eight evenly-spaced, individual respiratory positions based on the amplitude of the derived SG surrogate [13]. 4D images were reconstructed using a compressed sensing algorithm that exploits sparsity in both spatial and respiratory motion dimensions [12], [17],

Results

Fig. 1a–c shows selected static phantom images in both field strengths using different parameters. The SNR in 0.35 T was 4.6-folds lower than in the 1.5 T (6.9 vs. 31.5), when using identical sequence parameters. However, our optimized sequence using higher flip angle, longer TR, and lower bandwidth could increase the SNR in the low-field from 6.9 to 10.4. In addition, the removal of fat saturation module and the longer acquisition time could further improve the SNR efficiency in the low-field.

Discussion

In this study, we demonstrated for the first time the clinical feasibility of a 4D-MRI technique on a commercially available MRgRT system. Experiments using static and dynamic phantoms validated the spatial integrity and motion quantification of the method. Our initial clinical experience in patients with abdominal tumors suggests that the ROCK-4D-MRI could provide high-resolution, respiratory motion-resolved 4D images to improve tumor and normal tissue discrimination than 4D-CT—the current

Conclusion

Our study demonstrated the clinical feasibility of using the ROCK-4D-MRI technique to acquire high quality, respiratory motion-resolved 4D-MRI in a low-field MRgRT system. The 4D-MRI image could provide accurate spatial and dynamic information of the abdominal organs and tumors. The near-isotropic high resolution and superior soft-tissue contrast to 4D-CT could potentially translate to significant benefit for MRI-based radiotherapy, particularly within the abdominal cavity.

Conflict of interest

Dr. Cao reports personal fees from ViewRay Inc., outside the submitted work. Dr. Low reports grants from Siemens Medical, during the conduct of the study. Drs. Lee and Yang report speaking honorarium from Viewray Inc.

Acknowledgement

The authors acknowledge funding support from the National Institutes of Health under the award numbers R01HL127153 and R01CA188300.

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