MO-FG-204-08: Optimization-Based Image Reconstruction From Unevenly Distributed Sparse Projection Views

Authors

  • Xie Huiqiao,

    1. Emory University School of Medicine, Atlanta, GA
    2. Sir RunRun Shaw Hospital, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
    3. Physics and Algorithm, Sinovision Technologies, Beijing, China
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  • Niu Tianye,

    1. Emory University School of Medicine, Atlanta, GA
    2. Sir RunRun Shaw Hospital, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
    3. Physics and Algorithm, Sinovision Technologies, Beijing, China
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  • Ren Yi,

    1. Emory University School of Medicine, Atlanta, GA
    2. Sir RunRun Shaw Hospital, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
    3. Physics and Algorithm, Sinovision Technologies, Beijing, China
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  • Yang Yi,

    1. Emory University School of Medicine, Atlanta, GA
    2. Sir RunRun Shaw Hospital, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
    3. Physics and Algorithm, Sinovision Technologies, Beijing, China
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  • Tang Xiangyang

    1. Emory University School of Medicine, Atlanta, GA
    2. Sir RunRun Shaw Hospital, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
    3. Physics and Algorithm, Sinovision Technologies, Beijing, China
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Abstract

Purpose:

Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work.

Methods:

The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, which are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively.

Results:

In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom.

Conclusion:

The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available.

Research Grants: W81XWH-12-1-0138 (DoD), Sinovision Technologies.

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