TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research

Du, Yi; Wang, Ruoxi; Biguri, Ander; Zhao, Xuzhi; Peng, Yahui; Wu, Hao

doi:10.1016/j.ejmp.2022.08.013

Cited by 4 publications

(11 citation statements)

References 27 publications

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“…The raw CBCT projections from each dataset, exported in Varian's XIM file format, were loaded into TIGRE and preprocessed using the VarianDataLoader.m module. 20 This module can perform standard preprocessing operations on the projection data such as scatter correction, air normalization, logarithmic normalization, beam hardening correction, and ring artifact suppression. These corrections were designed to closely replicate the procedures utilized by Varian's proprietary iTools Reconstruction software, which has been used for similar VM-CBCT image production in previous studies.…”

Section: Preprocessingmentioning

confidence: 99%

“…The resulting set of VM projections and gantry angles can be reconstructed into a 3D volumetric image using From there, the rest of the workflow follows the reconstruction and post-processing procedures described in past works using TIGRE. 19,20 Note that the two projection sets require calling the VarianDataLoader.m function twice.…”

Section: Reconstruction and Hu Mappingmentioning

confidence: 99%

“…In this case, the calibration was performed using the various material inserts of the Catphan 604 phantom. 20 Since the HU are energy-dependent, a different HU calibration was made for each VM energy, with the theoretical HU values for each insert and VM energy being computed from the manufacturer-reported attenuation coefficient values. Any deviations from perfect linearity between the pixel values and HU are expected to be within the 50 HU tolerance reported for the TrueBeam system.…”

Section: Reconstruction and Hu Mappingmentioning

confidence: 99%

“…Recent work on the TIGRE toolkit, for example, has added modules specifically designed to carry out pre-processing and post-processing methods. 20 Our work develops and introduces the TIGRE-DE module, an accessible, opensource module for performing the construction of VM CBCT images from DE-CBCT. This module is implemented within the wider TIGRE toolkit, furthering the ability of researchers and clinicians to access cuttingedge work in CBCT image production and processing.…”

Section: Introductionmentioning

confidence: 99%

“…While these toolkits focus primarily on the image reconstruction process, the implementation of additional CBCT functionality represents a natural extension. Recent work on the TIGRE toolkit, for example, has added modules specifically designed to carry out pre‐processing and post‐processing methods 20 . Our work develops and introduces the TIGRE‐DE module, an accessible, open‐source module for performing the construction of VM CBCT images from DE‐CBCT.…”

Section: Introductionmentioning

confidence: 99%

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Technical note: TIGRE‐DE for the creation of virtual monoenergetic images from dual‐energy cone‐beam CT

Keeler,

Lehmann,

Luce

et al. 2024

Medical Physics

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BackgroundDual‐energy (DE)‐CBCT represents a promising imaging modality that can produce virtual monoenergetic (VM) CBCT images. VM images, which provide enhanced contrast and reduced imaging artifacts, can be used to assist in soft‐tissue visualization during image‐guided radiotherapy.PurposeThis work reports the development of TIGRE‐DE, a module in the open‐source TIGRE toolkit for the performance of DE‐CBCT and the production of VM CBCT images. This module is created to make DE‐CBCT tools accessible in a wider range of clinical and research settings.MethodsWe developed an add‐on (TIGRE‐DE) to the TIGRE toolkit that performs DE material decomposition. To verify its performance, sequential CBCT scans at 80 and 140 kV of a Catphan 604 phantom were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl‐methylacrylate (PMMA) basis materials. These basis material projections were used to synthesize VM projections for a range of x‐ray energies, which were then reconstructed using the Feldkamp‐Davis‐Kress (FDK) algorithm. Image quality was assessed by computing Hounsfield units (HU) and contrast‐to‐noise ratios (CNR) for the material inserts of the phantom and comparing with the constituent 80 and 140 kV images.ResultsAll VM images generated using TIGRE‐DE showed good general agreement with the theoretical HU values of the material inserts of the phantom. Apart from the highest‐density inserts imaged at the extremes of the energy range, the measured HU values agree with theoretical HUs within the clinical tolerance of ±50 HU. CNR measurements for the various inserts showed that, of the energies selected, 60 keV provided the highest CNR values. Moreover, 60 keV VM images showed average CNR enhancements of 63% and 66% compared to the 80 and 140 kV full‐fan protocols.ConclusionsTIGRE‐DE successfully implements DE‐CBCT material decomposition and VM image creation in an accessible, open‐source platform.

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Section: Preprocessingmentioning

confidence: 99%

Section: Reconstruction and Hu Mappingmentioning

confidence: 99%

Section: Reconstruction and Hu Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Technical note: TIGRE‐DE for the creation of virtual monoenergetic images from dual‐energy cone‐beam CT

Keeler,

Lehmann,

Luce

et al. 2024

Medical Physics

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VVBPNet: Deep learning model in view-by-view backprojection (VVBP) domain for sparse-view CBCT reconstruction

Zhao,

Du,

Peng

2025

Biomedical Signal Processing and Control

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Deep learning-based projection synthesis for low-dose cone-beam computed tomography imaging in image-guided radiotherapy

Zhao,

Du,

Yue

et al. 2024

Quant Imaging Med Surg

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Background The imaging dose of cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) poses adverse effects on patient health. To improve the quality of sparse-view low-dose CBCT images, a projection synthesis convolutional neural network (SynCNN) model is proposed. Methods Included in this retrospective, single-center study were 223 patients diagnosed with brain tumours from Beijing Cancer Hospital. The proposed SynCNN model estimated two pairs of orthogonally direction-separable spatial kernels to synthesize the missing projection in between the input neighboring sparse-view projections via local convolution operations. The SynCNN model was trained on 150 real patients to learn patterns for inter-view projection synthesis. CBCT data from 30 real patients were used to validate the SynCNN, while data from a phantom and 43 real patients were used to test the SynCNN externally. Sparse-view projection datasets with 1/2, 1/4, and 1/8 of the original sampling rate were simulated, and the corresponding full-view projection datasets were restored using the SynCNN model. The tomographic images were then reconstructed with the Feldkamp-Davis-Kress algorithm. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) metrics were measured in both the projection and image domains. Five experts were invited to grade the image quality blindly for 40 randomly selected evaluation groups with a four-level rubric, where a score greater than or equal to 2 was considered acceptable image quality. The running time of the SynCNN model was recorded. The SynCNN model was directly compared with the three other methods on 1/4 sparse-view reconstructions. Results The phantom and patient studies showed that the missing projections were accurately synthesized. In the image domain, for the phantom study, compared with images reconstructed from sparse-view projections, images with SynCNN synthesis exhibited significantly improved qualities with decreased values in RMSE and increased values in PSNR and SSIM. For the patient study, between the results with and without the SynCNN synthesis, the averaged RMSE decreased by 3.4×10 −4 , 10.3×10 −4 , and 21.7×10 −4 , the averaged PSNR increased by 3.4, 6.6, and 9.4 dB, and the averaged SSIM increased by 5.2×10 −2 , 18.9×10 −2 and 33.9×10 −2 , for the 1/2, 1/4, and 1/8 sparse-view reconstructions, respectively. In expert subjective evaluation, both the median scores and acceptance rates of the images with SynCNN synthesis were higher than those reconstructed from sparse-view projections. It took the model less than 0.01 s to synthesize an inter-view projection. Compared with the three other methods, the SynCNN model obtained the best scores in terms of the three metrics in both domains. Conc...

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TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research

Cited by 4 publications

References 27 publications

Technical note: TIGRE‐DE for the creation of virtual monoenergetic images from dual‐energy cone‐beam CT

Technical note: TIGRE‐DE for the creation of virtual monoenergetic images from dual‐energy cone‐beam CT

VVBPNet: Deep learning model in view-by-view backprojection (VVBP) domain for sparse-view CBCT reconstruction

Deep learning-based projection synthesis for low-dose cone-beam computed tomography imaging in image-guided radiotherapy

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