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. 2021 Feb 11;66(4):045023.
doi: 10.1088/1361-6560/abd668.

4D radiomics: impact of 4D-CBCT image quality on radiomic analysis

Zeyu Zhang  1   2 , Mi Huang  1 , Zhuoran Jiang  1   3 , Yushi Chang  1   2 , Jordan Torok  4 , Fang-Fang Yin  1   2   5 , Lei Ren  1   2
Affiliations

4D radiomics: impact of 4D-CBCT image quality on radiomic analysis

Zeyu Zhang et al. Phys Med Biol. .

Abstract

Purpose: To investigate the impact of 4D-CBCT image quality on radiomic analysis and the efficacy of using deep learning based image enhancement to improve the accuracy of radiomic features of 4D-CBCT.

Material and methods: In this study, 4D-CT data from 16 lung cancer patients were obtained. Digitally reconstructed radiographs (DRRs) were simulated from the 4D-CT, and then used to reconstruct 4D CBCT using the conventional FDK (Feldkamp et al 1984 J. Opt. Soc. Am. A 1 612-9) algorithm. Different projection numbers (i.e. 72, 120, 144, 180) and projection angle distributions (i.e. evenly distributed and unevenly distributed using angles from real 4D-CBCT scans) were simulated to generate the corresponding 4D-CBCT. A deep learning model (TecoGAN) was trained on 10 patients and validated on 3 patients to enhance the 4D-CBCT image quality to match with the corresponding ground-truth 4D-CT. The remaining 3 patients with different tumor sizes were used for testing. The radiomic features in 6 different categories, including histogram, GLCM, GLRLM, GLSZM, NGTDM, and wavelet, were extracted from the gross tumor volumes of each phase of original 4D-CBCT, enhanced 4D-CBCT, and 4D-CT. The radiomic features in 4D-CT were used as the ground-truth to evaluate the errors of the radiomic features in the original 4D-CBCT and enhanced 4D-CBCT. Errors in the original 4D-CBCT demonstrated the impact of image quality on radiomic features. Comparison between errors in the original 4D-CBCT and enhanced 4D-CBCT demonstrated the efficacy of using deep learning to improve the radiomic feature accuracy.

Results: 4D-CBCT image quality can substantially affect the accuracy of the radiomic features, and the degree of impact is feature-dependent. The deep learning model was able to enhance the anatomical details and edge information in the 4D-CBCT as well as removing other image artifacts. This enhancement of image quality resulted in reduced errors for most radiomic features. The average reduction of radiomics errors for 3 patients are 20.0%, 31.4%, 36.7%, 50.0%, 33.6% and 11.3% for histogram, GLCM, GLRLM, GLSZM, NGTDM and Wavelet features. And the error reduction was more significant for patients with larger tumors. The findings were consistent across different respiratory phases, projection numbers, and angle distributions.

Conclusions: The study demonstrated that 4D-CBCT image quality has a significant impact on the radiomic analysis. The deep learning-based augmentation technique proved to be an effective approach to enhance 4D-CBCT image quality to improve the accuracy of radiomic analysis.

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Figures

Figure 1.
Figure 1.
Workflow of the study.
Figure 2.
Figure 2.
GTV of patient 1 in phase 5.
Figure 3.
Figure 3.
Simulated 4D-CBCT images from the different number of projections.
Figure 4.
Figure 4.
Simulated 4D-CBCT, enhanced 4D-CBCT with corresponding real 4D-CT images of patient 2.
Figure 5.
Figure 5.
GTV and whole body intensity difference of patient 2, the spectrums represent the value difference of images.
Figure 6.
Figure 6.
GTV and whole body intensity histogram of patient 2.
Figure 7.
Figure 7.
Intensity and texture features of patient 3, phase 1, 72 projections.
Figure 8.
Figure 8.
Wavelet features errors of patient 3, phase 1, 72 projections.
Figure 9.
Figure 9.
Histogram and GLCM radiomics errors across 10 phases of patient 3.
Figure 10.
Figure 10.
Average radiomics features of the histogram, GLCM, GLRLM, GLSZM, NGTDM and wavelet of patient 3.
Figure 11.
Figure 11.
Linear Regression of skewness features from 4D-CBCT to 4D-CT.
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