A novel iterative reconstruction algorithm for improving CBCT image quality.
Recommended Citation
Mao W, Liu C, Snyder K, Kumarasiri A, Zhao B, Gardner S, Kim J, Wen N, Chetty I, and Siddiqui F. A novel iterative reconstruction algorithm for improving CBCT image quality. Med Phys 2017; 44(6):3148.
Document Type
Conference Proceeding
Publication Date
2017
Publication Title
Med Phys
Abstract
Purpose: We have assessed the image quality of a novel, iterative reconstruction algorithm to determine the potential to improve image quality, ultimately to enhance the accuracy of CBCT-based localization over the standard reconstruction algorithm. Methods: The current TrueBeam CBCT reconstruction removes scatter using a kernel-based correction followed by filtered back-projection-based reconstruction (FDK). In the prototype CBCT reconstruction pipeline these steps have been replaced by a finite element solver (AcurosCTS)-based scatter correction and a statistical (iterative) reconstruction. Image quality improvements due to the prototype reconstruction pipeline have been quantitatively analyzed on scans of a standard phantom. Standard full-fan Head, half-fan full-rotation Head, and standard Pelvis CBCT protocols have been quantitatively investigated, including evaluation of noise level, uniformity, constancy, spatial resolution, and modulation transfer function (MTF), using a commercially available software package. Results: Image quality analysis results show that noise level is reduced to from 28.9 HU, 17.3 HU, and 7.2 HU to 19.1 HU, 7.9 HU, and 2.7 HU, for full-fan Head, half-fan Head, and Pelvis scans, respectively, while MTF measurements indicate that spatial resolution is maintained. HU uniformity improved from 8.6 ± 2.0 to 3.7 ± 2.3 for full-fan Head CBCT and from 7.4 ± 4.0 to 4.3 ± 2.4 for Pelvis CBCT. Contrast to noise ratio (CNR) was analyzed based on a 1% contrast insertion with a diameter of 15 mm. CNR was improved from 0.6 to 0.9, from 1.5 to 5.0, and from 1.7 to 3.8, for the full-fan, half-fan Head, and Pelvis CBCT, respectively. This is mainly due to the significant reduction in image noise. Conclusion: Noise and other image quality characteristics are significantly improved using the iterative reconstruction algorithm, over the current (FDK-based) method. This suggests the potential for the iterative algorithm to improve image quality, ultimately to enhance the accuracy of image-guided applications using CBCT.
Volume
44
Issue
6
First Page
3148