The Feasibility Study of Megavoltage Computed Tomographic (MVCT) Image for Texture Feature Analysis

Gu, Jinyan; Zhu, Jian; Qiu, Qingtao; Wang, Yungang; Bai, Tong; Duan, Jinghao; Yin, Yong

doi:10.3389/fonc.2018.00586

Cited by 9 publications

(13 citation statements)

References 37 publications

(32 reference statements)

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“…Radiomic features showed sensitivity and variability related to differences in manufacturers, 16 scanners, 17 and acquisition and reconstruction parameters, 18–20 including pitch value, tube voltage, tube current, slice thickness, resolution, field of view (FOV), reconstruction method, 20 reconstruction kernel, and radiation dose. Radiomic features also showed variability in test-retest datasets 21 and various radiomics software 22 . Many efforts have been made to investigate and solve this problem 16–19,22,23 .…”

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confidence: 99%

Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules

et al. 2021

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Background Radiomics can yield minable information from medical images, which can facilitate computer-aided diagnosis. However, the lack of repeatability and reproducibility of radiomic features (RFs) may hinder their generalizability in clinical applications. Objectives The aims of this study were to explore 3 main sources of variability in RFs, investigate their influencing magnitudes and patterns, and identify a subset of robust RFs for further studies. Materials and Methods A chest phantom with nodules was scanned with different computed tomography (CT) scanners repeatedly with varying acquisition and reconstruction parameters (April-May 2019) to evaluate 3 sources of variability: test-retest, inter-CT, and intra-CT protocol variability. The robustness of the RFs was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient (ICC). The influencing magnitudes and patterns were analyzed using the Friedman test and Spearman rank correlation coefficient. Stable and informative RFs were selected, and their redundancy was eliminated using hierarchical clustering. Clinical validation was also performed to verify the clinical effectiveness and potential enhancement of the generalizability of radiomics research. Results A total of 1295 RFs that showed all 3 sources of variability were included. The reconstruction kernel and the iteration level showed the greatest (ICC, 0.35 ± 0.31) and the least (ICC, 0.63 ± 0.27) influence on magnitudes. The different sources of variability showed relatively consistent patterns of influence (false discovery rate <0.001). Finally, we obtained a subset of 19 stable, informative, and nonredundant RFs under all 3 sources of variability. These RFs exhibited clinical effectiveness and showed better prediction performance than unstable RFs in the validation dataset ( P = 0.017, Delong test). Conclusions The stability of RFs was affected to different degrees by test-retest and differences in CT manufacturers and models and CT acquisition and reconstruction parameters, but the influences of these factors showed relatively consistent patterns. We also obtained a subset of 19 stable, informative, and nonredundant RFs that should be preferably used to enhance the generalizability of further radiomics research.

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confidence: 99%

Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules

et al. 2021

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“…Yet for this application where an intricate volumetric distribution of different radio‐densities is desired. Future direction is to explore the most advanced 3D‐printing technology to print multi‐densities base materials 32 at a single layer to achieve spatial distribution of varied X‐ray attenuation. Regarding texture analysis, a fixed bin size of 25 HU was used for texture metrics calculation which could be another source of variation.…”

Section: Discussionmentioning

confidence: 99%

The impact of phantom design and material‐dependence on repeatability and reproducibility of CT‐based radiomics features

Reyhan

Zhang

et al. 2022

Medical Physics

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Purpose To understand the design of radiomics phantom and material‐dependence on repeatability and reproducibility of computed tomography (CT) radiomics features. Methods A radiomics phantom consisting of various materials with uniformity, textural, and biological components, was constructed. The phantom was scanned with different manufacturer CT scanners and the scans were repeated multiple times on the same scanner with different acquisition settings as kVp, mAs, orientation, field of view (FOV), slice thickness, pitch, reconstruction kernels, and acquisition mode. A total of 72 phantom scans were included. For each scan, 18 different regions of interest (ROI) were contoured and 708 radiomics features were extracted from each ROI via an open source radiomics tool, IBEX. To relate the phantom data to patient data, the radiomics features from different phantom materials were compared with those extracted from 50 patients’ images of five disease sites as brain, head‐and‐neck, breast, liver, and lung cases using box‐plots comparison and principal component analysis (PCA). The temporal stability of imaging features was then evaluated with respect to a controlled scenario (test–retest) via the intra‐class correlation coefficient (ICC). The reproducibility of radiomics features with respect to different scanners or acquisition settings were further evaluated with concordance correlation coefficients (CCC). Results Among all phantom materials, the biological component had feature values closest to human tissues, especially for tumors in brain and liver. The textural component showed similar ranges of variation to lung lesions, particularly for cartridges of rice, cereal, and the 3D‐printed textural phantom with fine and rough grid. It also showed that certain materials, such as polystyrene foam, plaster, and peanuts, did not have comparable values to human tissue and could be excluded for future phantom design. High repeatability was observed in the test–retest study as indicated by an ICC value of 0.998 ± 0.020. All materials were used for feature stability analysis. For the inter‐scanner study, shape‐related features were the most‐reliable category with 94% of features having CCC ≥ 0.9, while gradient orientation histogram (GOH) were the least‐reliable with only 14.6% meeting the criteria. For the intra‐scanner study, the reproducibility of CT‐based radiomics features showed material‐dependence. In general, the instability of radiomics features introduced by kVp, mAs, pitch, acquisition mode, and orientation were relatively mild. However, the homogeneous materials were more vulnerable to those changes compared to materials with textural patterns. Regardless of material compositions, resolution parameters like FOV and slice thickness, could have large impact on feature stability. Switching between standard and bone reconstruction kernels could also result significant changes to feature reproducibility. Conclusion We have built a radiomics phantom using materials that cover a wide span of tumor textures seen i...

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“…Our previous study (22, 23) proposed that, with texture features extracted by Radiomic techniques, online cone beam CT images may be used to predict radiotherapy prognosis, whose prediction accuracy was higher than the existing clinical standards like RECIST. The effectiveness of radiomics techniques has also been demonstrated on predicting radiotherapy efficacy, complications, and prognosis (24–26).…”

Section: Discussionmentioning

confidence: 99%

“…Considering that post-processed MVCT images offer a more accurate tumor and soft tissue delineation, with the MVCT-KVCT registration technique, the Radiomics prediction on CT may be more specific with certain tumors or organs. Furthermore, considering that our recent research has proven that the MVCT has higher texture feature reproducibility than CBCT (23), even the MVCT may be employed to extract radiomics texture features at prognosis and toxicity prediction in the future.…”

Section: Discussionmentioning

confidence: 99%

A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy

et al. 2019

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Purpose: To propose a novel method to improve the mega-voltage CT (MVCT) image quality for helical TomoTherapy while maintaining the stability on dose calculation. Materials and Methods: The Block-Matching 3D-transform (BM3D) and Discriminative Feature Representation (DFR) methods were combined into a novel BM3D + DFR method for their respective advantages. A phantom (Catphan504) and three serials of clinical (head & neck, chest, and pelvis) MVCT images from 30 patients were acquired using the helical TomoTherapy system. The contrast-to-noise ratio (CNR) and edge detection algorithm (canny) was employed for image quality comparisons between the original and BM3D + DFR enhanced MVCT. A simulated rectangular field of 6 MV X-ray beams were vertically delivered on the original and post-processed MVCT serials of the same CT density phantom, and the dose curves on both serials were compared to test the effects of image enhancement on dose calculation accuracy. Results: In total, 466 transversal MVCT slices were acquired and processed by both BM3D and the proposed BM3D + DFR methods. Compared to the original MVCT image, the BM3D + DFR method presented a remarkable improvement in terms of the soft tissue contrast and noise reduction. For the phantom image, the CNR of the region of interest (ROI) was improved from 1.70 to 4.03. The average CNR of ROIs for 10 patients from each anatomical group, were increased significantly from 1.45 ± 1.51 to 2.09 ± 1.68 for the head & neck ( p < 0.001), from 0.92 ± 0.78 to 1.36 ± 0.85 for the chest ( p < 0.001), and from 1.12 ± 1.22 to 1.76 ± 1.31 for the pelvis ( p < 0.001), respectively. The canny edge detection operator showed that BM3D + DFR provided clearer organ boundaries with less chaos. The root-mean-square of the dosimetry difference on the iso-center passed horizontal dose profile curves and vertical percentage depth dose curves were only 0.09% and 0.06%, respectively. Conclusions: The proposed BM3D + DFR method is feasible to improve the soft tissue contrast for the original MVCT images with coincidence in dose calculation and without compromising resolution. After integration in clinical workflow, the post-processed MVCT may be better applied on image-guided and adaptive helical TomoTherapy.

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The Feasibility Study of Megavoltage Computed Tomographic (MVCT) Image for Texture Feature Analysis

Cited by 9 publications

References 37 publications

Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules

Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules

The impact of phantom design and material‐dependence on repeatability and reproducibility of CT‐based radiomics features

A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy

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