Improvements in CBCT Image Quality Using a Novel Iterative Reconstruction Algorithm: A Clinical Evaluation

Gardner, Stephen; Mao, Weihua; Liu, Chang; Aref, Ibrahim; Elshaikh, Mohamed A.; Lee, Joon K; Pradhan, D.; Movsas, Benjamin; Chetty, Indrin J.; Siddiqui, Farzan

doi:10.1016/j.adro.2018.12.003

Cited by 57 publications

(55 citation statements)

References 40 publications

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“…) that reduced the accuracy of DIR. Gardner et al developed a novel iterative reconstruction algorithm to improve CBCT image qualitiy and showed improvements in image uniformity, noise levels, and overall image quality for IGRT for prostate and head and neck cancer . Tests of the accuracy of DIR for CBCT indicate ongoing room for improvement.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the accuracy of deformable image registration for cone‐beam computed tomography with a physical phantom

Liu

Williamson

et al. 2019

J Applied Clin Med Phys

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Purpose: Kilo-voltage cone-beam computed tomography (CBCT) is widely used for patient alignment, contour propagation, and adaptive treatment planning in radiation therapy. In this study, we evaluated the accuracy of deformable image registration (DIR) for CBCT under various imaging protocols with different noise and patient dose levels.Methods: A physical phantom previously developed to facilitate end-to-end testing of the DIR accuracy was used with Varian Velocity v4.0 software to evaluate the performance of image registration from CT to CT, CBCT to CT, and CBCT to CBCT. The phantom is acrylic and includes several inserts that simulate different tissue shapes and properties. Deformations and anatomic changes were simulated by changing the rotations of both the phantom and the inserts. CT images (from a head and neck protocol) and CBCT images (from pelvis, head and "Image Gently" protocols) were obtained with different image noise and dose levels. Large inserts were filled with Mobil DTE oil to simulate soft tissue, and small inserts were filled with bone materials. All inserts were contoured before the DIR process to provide a ground truth contour size and shape for comparison. After the DIR process, all deformed contours were compared with the originals using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). Both large and small volume of interests (VOIs) for DIR volume selection were tested by simulating a DIR process that included whole patient image volume and clinical target volumes (CTV) only (for CTVs propagation). Results:For cross-modality DIR registration (CT to CBCT), the DSC were >0.8 and the MDA were <3 mm for CBCT pelvis, and CBCT head protocols. For CBCT to CBCT and CT to CT, the DIR accuracy was improved relative to the cross-modality tests. For smaller VOIs, the DSC were >0.8 and MDA <2 mm for all modalities. Conclusions:The accuracy of DIR depends on the quality of the CBCT image at different dose and noise levels.Wu and Liu contributed equally.---

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Section: Discussionmentioning

confidence: 99%

Quantifying the accuracy of deformable image registration for cone‐beam computed tomography with a physical phantom

Liu

Williamson

et al. 2019

J Applied Clin Med Phys

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“…Daily CBCT raw projections were reconstructed in several ways using various combinations of reconstruction algorithms (iterative 40 and standard), convolution filters (sharp, standard, smooth), and noise suppression filters. The reconstruction parameters used in this study are summarized in Table III.…”

Section: Methodsmentioning

confidence: 99%

“…Standard CBCT (sCBCT) reconstruction algorithm refers to traditionally used filtered back projection reconstruction while iterative CBCT (iCBCT) reconstruction employs scatter correction using an algorithm that estimates scatter in x-ray projection images by solving the linear Boltzmann transport equation and statistical iterative reconstruction for final-pass image reconstruction. 40 The iterative reconstructions in iTools have five options for noise suppression from Very High to Very Low. Noise suppression controls the degree to which the iterative algorithm aims to minimize variation in voxel intensity in the final reconstruction.…”

Section: E Image Reconstructionmentioning

confidence: 99%

Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer

et al. 2021

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Purpose: Radiomic features of cone-beam CT (CBCT) images have potential as biomarkers to predict treatment response and prognosis for patients of prostate cancer. Previous studies of radiomic feature analysis for prostate cancer were assessed in a variety of imaging modalities, including MRI, PET, and CT, but usually limited to a pretreatment setting. However, CBCT images may provide an opportunity to capture early morphological changes to the tumor during treatment that could lead to timely treatment adaptation. This work investigated the quality of CBCT-based radiomic features and their relationship with reconstruction methods applied to the CBCT projections and the preprocessing methods used in feature extraction. Moreover, CBCT features were correlated with planning CT (pCT) features to further assess the viability of CBCT radiomic features. Methods: The quality of 42 CBCT-based radiomic features was assessed according to their repeatability and reproducibility. Repeatability was quantified by correlating radiomic features between 20 CBCT scans that also had repeated scans within 15 minutes. Reproducibility was quantified by correlating radiomic features between the planning CT (pCT) and the first fraction CBCT for 20 patients. Concordance correlation coefficients (CCC) of radiomic features were used to estimate the repeatability and reproducibility of radiomic features. The same patient dataset was assessed using different reconstruction methods applied to the CBCT projections. CBCT images were generated using 18 reconstruction methods using iterative (iCBCT) and standard (sCBCT) reconstructions, three convolution filters, and five noise suppression filters. Eighteen preprocessing settings were also considered. Results: Overall, CBCT radiomic features were more repeatable than reproducible. Five radiomic features are repeatable in > 97% of the reconstruction and preprocessing methods, and come from the gray-level size zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and gray-level-run length matrix (GLRLM) radiomic feature classes. These radiomic features were reproducible in > 9.8% of the reconstruction and preprocessing methods. Noise suppression and convolution filter smoothing increased radiomic features repeatability, but decreased reproducibility. The top-repeatable iCBCT method (iCBCT-Sharp-VeryHigh) is more repeatable than the top-repeatable sCBCT method (sCBCT-Smooth) in 64% of the radiomic features. Conclusion: Methods for reconstruction and preprocessing that improve CBCT radiomic feature repeatability often decrease reproducibility. The best approach may be to use methods that strike a balance repeatability and reproducibility such as iCBCT-Sharp-VeryLow-1-Lloyd-256 that has 17 repeatable and eight reproducible radiomic features. Previous radiomic studies that only used pCT radiomic features have generated prognostic models of prostate cancer outcome. Since our study indicates that CBCT radiomic features correlated well with a subset of pCT radiomic features, one may expect ...

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“…During treatment of HNSCC, CBCT imaging is commonly obtained daily or weekly for setup verification and to monitor for changes in patient anatomy due to tumor response or weight loss. Over the past decade, innovations including Varian’s Iterative CBCT have tremendously improved the resolution and quality of cone beam computed tomography (CBCT) such that discrete masses and lymph node conglomerates are readily discernable [ 5 – 7 ]. However, this large dataset of CT information has not been routinely used to monitor treatment responses largely because of the low resolution of CBCT in the past.…”

Section: Introductionmentioning

confidence: 99%

End of treatment cone-beam computed tomography (CBCT) is predictive of radiation response and overall survival in oropharyngeal squamous cell carcinoma

et al. 2021

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Background Image guidance in radiation oncology has resulted in significant improvements in the accuracy and precision of radiation therapy (RT). Recently, the resolution and quality of cone beam computed tomography (CBCT) for image guidance has increased so that tumor masses and lymph nodes are readily detectable and measurable. During treatment of head and neck squamous cell carcinoma (HNSCC), on-board CBCT setup imaging is routinely obtained; however, this CBCT imaging data is not utilized to predict patient outcomes. Here, we analyzed whether changes in CBCT measurements obtained during a course of radiation therapy correlate with responses on routine 3-month follow-up diagnostic imaging and overall survival (OS). Materials/methods Patients with oropharyngeal primary tumors who received radiation therapy between 2015 and 2018 were included. Anatomical measurements were collected of largest nodal conglomerate (LNC) at CT simulation, end of radiation treatment (EOT CBCT), and routine 3-month post-RT imaging. At each timepoint anteroposterior (AP), mediolateral (ML) and craniocaudal (CC) measurements were obtained and used to create a 2-dimensional (2D) maximum. Results CBCT data from 64 node positive patients were analyzed. The largest nodal 2D maximum and CC measurements on EOT CBCT showed a statistically significant correlation with complete response on 3-month post-RT imaging (r = 0.313, p = 0.02 and r = 0.318, p = 0.02, respectively). Furthermore, patients who experienced a 30% or greater reduction in the CC dimension had improved OS (Binary Chi-Square HR 4.85, p = 0.028). Conclusion Decreased size of pathologic lymph nodes measured using CBCT setup imaging during a radiation course correlates with long term therapeutic response and overall survival of HNSCC patients. These results indicate that CBCT setup imaging may have utility as an early predictor of treatment response in oropharyngeal HNSCC.

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Improvements in CBCT Image Quality Using a Novel Iterative Reconstruction Algorithm: A Clinical Evaluation

Cited by 57 publications

References 40 publications

Quantifying the accuracy of deformable image registration for cone‐beam computed tomography with a physical phantom

Quantifying the accuracy of deformable image registration for cone‐beam computed tomography with a physical phantom

Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer

End of treatment cone-beam computed tomography (CBCT) is predictive of radiation response and overall survival in oropharyngeal squamous cell carcinoma

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