Probability-Driven K-Space Based Multi-Cycle 4D-MRI Reconstruction

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Sun, Duohua


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Purpose: Current 4D-MRI techniques are prone to motion artifacts caused by irregular breathing. This study aims to develop and evaluate a novel, motion-robust multi-cycle 4D-MRI technique to overcome this deficiency.

Materials/Methods: The breathing signal was first analyzed to determine the main breathing cycles, providing tumor motion probability information for 4D-MRI reconstruction. 4D-MRI was reconstructed for each main breathing cycle using an in-house developed result-driven k-space reordering method. The new method was tested on the 4D-XCAT phantom. For comparison, conventional phase sorting method is also applied to generate a single-cycle 4D-MRI. Tumor and liver SNRs, tumor volume consistency, and AIP accuracy were determined and compared between the two methods. The original XCAT images were used as reference for the evaluations.

Results: Three-cycle 4D-MRI images were generated using the new method, presenting less noise and higher tumor and liver SNRs (30.41 and 15.28, 30.07 and 15.17, 28.63 and 15.25 for cycle 1, 2, and 3 respectively) than those of 4D-MRI images generated using phase sorting (17.33 and 12.04). These images have reduced motion artifacts, reflected by the improved inter-phase tumor volume consistency: the coefficients of variation in tumor volume were lower in the new method (0.027, 0.033 and 0.042 for cycle 1, 2, 3 respective) than that of the phase-sorting method (0.072). In addition, the AIP generated from the new method was more similar to the reference AIP than that from the phase sorting method; both the image intensity difference (0.21) and standard deviation of the difference map (6.4296e-8) were lower than those from the phase sorting method (0.46 and 1.1562e-7, respectively).

Conclusion: These results demonstrated the feasibility of the motion-robust, multi-cycle 4D-MRI technique through probability-driven k-space reordering. This new technique holds great promises to improve the image quality of 4D-MRI and the accuracy of its clinical applications.





Sun, Duohua (2017). Probability-Driven K-Space Based Multi-Cycle 4D-MRI Reconstruction. Master's thesis, Duke University. Retrieved from


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