Presentation + Paper
9 March 2018 Artifacts reduction in low-contrast neurological imaging with C-arm system
Dan Xia, Yu-Bing Chang, Adnan H. Siddiqui, Zheng Zhang, Joe Manak, Emil Y. Sidky, Xiaochuan Pan
Author Affiliations +
Abstract
C-arm cone-beam CT (CBCT) is adopted rapidly for imaging-guidance in interventional and surgical procedures. However, measured CBCT data are truncated often due to the limited detector size especially in the presence of additional interventional devices outside the imaging field of view (FOV). In our previous work, it has been demonstrated that a constrained optimization-based reconstruction with an additional data-derivative fidelity term can effectively suppress the truncation artifacts. In this work, in attempt to evaluate the optimization-based reconstruction, two task-relevant metrics, are proposed for characterization of the recovery of the low-contrast objects and the reduction of streak artifacts. Results demonstrate that the optimization program and the associated CP algorithms can significantly reduce streak artifacts, leading to improved visualization of lowcontrast structures in the reconstruction relative to clinical FDK reconstruction.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dan Xia, Yu-Bing Chang, Adnan H. Siddiqui, Zheng Zhang, Joe Manak, Emil Y. Sidky, and Xiaochuan Pan "Artifacts reduction in low-contrast neurological imaging with C-arm system", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105731S (9 March 2018); https://doi.org/10.1117/12.2293629
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KEYWORDS
Reconstruction algorithms

Imaging systems

Data modeling

Sensors

Image restoration

Head

Hough transforms

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