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. 2019 Mar 13;64(6):065015.
doi: 10.1088/1361-6560/ab0475.

Fast shading correction for cone-beam CT via partitioned tissue classification

Linxi Shi  1 , Adam Wang, Jikun Wei, Lei Zhu
Affiliations

Fast shading correction for cone-beam CT via partitioned tissue classification

Linxi Shi et al. Phys Med Biol. .

Abstract

The quantitative use of cone beam computed tomography (CBCT) in radiation therapy is limited by severe shading artifacts, even with system embedded correction. We recently proposed effective shading correction methods, using planning CT (pCT) as prior information to estimate low-frequency errors in either the projection domain or image domain. In this work, we further improve the clinical practicality of our previous methods by removing the requirement of prior pCT images. Clinical CBCT images are typically composed of a limited number of tissues. By utilizing the low frequency characteristic of shading distribution, we first generate a 'shading-free' template image by enforcing uniformity on CBCT voxels of the same tissue type via a technique named partitioned tissue classification. Only a small subset of voxels in the template image are used in the correction process to generate sparse samples of shading artifacts. Local filtration, a Fourier transform based algorithm, is employed to efficiently process the sparse errors to compute a full-field distribution of shading artifacts for CBCT correction. We evaluate the method's performance using an anthropomorphic pelvis phantom and 6 pelvis patients. The proposed method improves the image quality of CBCT for both phantom and patients to a level matching that of pCT. On the pelvis phantom, the signal non-uniformity (SNU) is reduced from 12.11% to 3.11% and 8.40% to 2.21% on fat and muscle, respectively. The maximum CT number error is reduced from 70 to 10 HU and 73 to 11 HU on fat and muscle, respectively. On patients, the average SNU is reduced from 9.22% to 1.06% and 11.41% to 1.67% on fat and muscle, respectively. The maximum CT number error is reduced from 95 to 9 HU and 88 to 8 HU on fat and muscle, respectively. The typical processing time for one CBCT dataset is about 45 s on a standard PC.

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Figures

Figure 1.
Figure 1.
Workflow of the proposed shading correction.
Figure 2.
Figure 2.
Diagram of the block division scheme for the proposed partitioned tissue classification.
Figure 3.
Figure 3.
Shading correction results for the pelvis phantom. Images on the top, middle and bottom row show an axial, coronal and sagittal view, respectively. From left to right column: uncorrected CBCT, pCT, corrected CBCT obtained using the proposed method, the pCT-based method (Shi et al 2017a), and the template image after sparse sampling of the proposed method respectively. The white and red squares placed on the axial view of uncorrected CBCT are the selected ROIs for SNU calculation, where the bold square contains the maximum shading artifacts.
Figure 4.
Figure 4.
Line profiles on axial images (i.e. uncorrected CBCT, pCT, corrected CBCT using the proposed and the pCT-based method), taken along the dashed line shown in figure 3.
Figure 5.
Figure 5.
Shading correction results on one pelvis patient. The images in the top, middle and bottom row are axial, coronal and sagittal views, respectively. From left to right column: uncorrected CBCT, pCT, corrected CBCT using the proposed method, the pCT based method (Shi et al 2017a) and the template image after sparse sampling of the proposed method respectively. The white and red squares placed on the axial view of left column are the selected ROIs for SNU calculation, where the bold square contains the maximum shading artifacts. Display window: (−200 200) HU.
Figure 6.
Figure 6.
Shading correction results on five additional pelvis patients. For each patient, the image is displayed the same way as figure 5. Display window: (−200 200) HU.
Figure 7.
Figure 7.
Effect of block number on the performance of CBCT shading correction. (a) Uncorrected CBCT, and corrected CBCT (b) without using the block number selection, (c) with 2 × 4 blocks and (d) with 2 × 6 blocks. Display window: (−200 200) HU.
Figure 8.
Figure 8.
CBCT shading correction without (upper row) and with (bottom row) the high-probability and low-frequency constraints. The uncorrected CBCT image is shown in figure 7(a). From left to right are the template image I0, first-pass shading map S0 before local filtration, final shading map St after local filtration and corrected CBCT. Display window for I0 and CBCT images: (−200 200) HU; for the shading images: (−100 100) HU.
Figure 9.
Figure 9.
Shading correction results for the case of a manually inserted tumor with spatially-varying CT numbers. (a) uncorrected CBCT, (b) corrected CBCT, and (c) shading map obtained using the proposed algorithm.
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