Experiment-based scatter correction for cone-beam computed tomography using the statistical method

Aootaphao, Sorapong; Thongvigitmanee, Saowapak S.; Rajruangrabin, Jatuwat; Junhunee, Parinya; Thajchayapong, P.

doi:10.1109/embc.2013.6610692

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…This commercially available iterative_CBCT algorithm presented here is different from other approaches focused on improving image quality of low-dose CBCT scans 17,28,31,44 in that it uses the same raw projection data from standard Truebeam CBCT acquisitions and applies software-only methods to improve low-contrast detection and soft-tissue visibility. 40,45,46 Consistent with results in the literature, 7 -10,13,17,23,30 -33,47 -49 iterative reconstruction uses physical constraints, such as regularization penalty terms to improve image quality by reducing image noise and enhancing CNR. This iterative reconstruction algorithm reduces noise and enhances low-contrast detection while maintaining spatial resolution relative to the standard (FDK-based) approach.…”

Section: Discussionsupporting

confidence: 62%

“…[4][5][6] In addition to hardware approaches that physically reduce detected levels of scatter, many different software approaches have been developed to compensate for CBCT-based scatter. 5,[7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] Recently, graphics processor unit (GPU)-based parallel computation methods have become popular, [27][28][29] and along with advanced CBCT reconstruction techniques, fast computation has become available in the clinic. 17,[30][31][32][33][34][35] Here, we report on evaluation of a new commercially available iterative reconstruction system, to determine whether a software-only method improves the quality of CBCT images, for the purposes of more accurate CBCTbased image-guided radiotherapy.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT

Mao

Liu

Gardner

et al. 2019

Technol Cancer Res Treat

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Purpose:We have quantitatively evaluated the image quality of a new commercially available iterative cone-beam computed tomography reconstruction algorithm over standard cone-beam computed tomography image reconstruction results.Methods:This iterative cone-beam computed tomography reconstruction pipeline uses a finite element solver (AcurosCTS)-based scatter correction and a statistical (iterative) reconstruction in addition to a standard kernel-based correction followed by filtered back-projection-based Feldkamp-Davis-Kress cone-beam computed tomography reconstruction. Standard full-fan half-rotation Head, half-fan full-rotation Head, and standard Pelvis cone-beam computed tomography protocols have been investigated to scan a quality assurance phantom via the following image quality metrics: uniformity, HU constancy, spatial resolution, low contrast detection, noise level, and contrast-to-noise ratio. An anthropomorphic head phantom was scanned for verification of noise reduction. Clinical patient image data sets for 5 head/neck patients and 5 prostate patients were qualitatively evaluated.Results:Quality assurance phantom study results showed that relative to filtered back-projection-based cone-beam computed tomography, noise was reduced from 28.8 ± 0.3 HU to a range between 18.3 ± 0.2 and 5.9 ± 0.2 HU for Full-Fan Head scans, from 14.4 ± 0.2 HU to a range between 12.8 ± 0.3 and 5.2 ± 0.3 HU for Half-Fan Head scans, and from 6.2 ± 0.1 HU to a range between 3.8 ± 0.1 and 2.0 ± 0.2 HU for Pelvis scans, with the iterative cone-beam computed tomography algorithm. Spatial resolution was marginally improved while results for uniformity and HU constancy were similar. For the head phantom study, noise was reduced from 43.6 HU to a range between 24.8 and 13.0 HU for a Full-Fan Head and from 35.1 HU to a range between 22.9 and 14.0 HU for a Half-Fan Head scan. The patient data study showed that artifacts due to photon starvation and streak artifacts were all reduced, and image noise in specified target regions were reduced to 62% ± 15% for 10 patients.Conclusion:Noise and contrast-to-noise ratio image quality characteristics were significantly improved using the iterative cone-beam computed tomography reconstruction algorithm relative to the filtered back-projection-based cone-beam computed tomography method. These improvements will enhance the accuracy of cone-beam computed tomography-based image-guided applications.

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Section: Discussionsupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT

Mao

Liu

Gardner

et al. 2019

Technol Cancer Res Treat

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“…The average value of the statistical method is considered by the X-ray photons behind an object as Poisson distribution [ 13 ]. The maximum likelihood is used to estimate the primary signal, I p , and can be written in the form of the MLEM [ 13 , 16 , 17 ] algorithm as follows:

where I p n is the estimated primary signal at the n th iteration. The kernel measurement at different thickness, K t , is obtained according to the thickness of the PMMA sheet, so I p n is divided according to thickness, I p , t n .…”

Section: Methodsmentioning

confidence: 99%

“…Since our work aims to reduce the X-ray scatter signals in the patient's head only, symmetric kernels can be assumed. Several iterative techniques were proposed for scatter correction including subtraction from measured data [ 14 , 15 ] and deconvolution by the statistical method [ 13 , 16 , 17 ]. The problem of the subtraction method is that the corrected value can become negative as discussed in [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we propose the X-ray scattering correction method for improving soft tissue images on the large flat panel detector of portable CBCT. We apply the deconvolution technique based on the maximum likelihood expectation maximization (MLEM) method [ 13 , 16 , 17 ] for estimating the primary signal. First, the MCS software is used to form the shape and the amplitude of the X-ray scatter signals as a point spread function (PSF) and a scatter fraction (SF) function according to actual parameters of the CBCT system.…”

Section: Introductionmentioning

confidence: 99%

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X-Ray Scatter Correction on Soft Tissue Images for Portable Cone Beam CT

Aootaphao

Thongvigitmanee

Rajruangrabin

et al. 2016

BioMed Research International

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Soft tissue images from portable cone beam computed tomography (CBCT) scanners can be used for diagnosis and detection of tumor, cancer, intracerebral hemorrhage, and so forth. Due to large field of view, X-ray scattering which is the main cause of artifacts degrades image quality, such as cupping artifacts, CT number inaccuracy, and low contrast, especially on soft tissue images. In this work, we propose the X-ray scatter correction method for improving soft tissue images. The X-ray scatter correction scheme to estimate X-ray scatter signals is based on the deconvolution technique using the maximum likelihood estimation maximization (MLEM) method. The scatter kernels are obtained by simulating the PMMA sheet on the Monte Carlo simulation (MCS) software. In the experiment, we used the QRM phantom to quantitatively compare with fan-beam CT (FBCT) data in terms of CT number values, contrast to noise ratio, cupping artifacts, and low contrast detectability. Moreover, the PH3 angiography phantom was also used to mimic human soft tissues in the brain. The reconstructed images with our proposed scatter correction show significant improvement on image quality. Thus the proposed scatter correction technique has high potential to detect soft tissues in the brain.

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Pseudo‐CT synthesis in adaptive radiotherapy based on a stacked coarse‐to‐fine model: Combing diffusion process and spatial‐frequency convolutions

Sun,

et al. 2024

Medical Physics

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BackgroundCone beam computed tomography (CBCT) provides critical anatomical information for adaptive radiotherapy (ART), especially for tumors in the pelvic region that undergo significant deformation. However, CBCT suffers from inaccurate Hounsfield Unit (HU) values and lower soft tissue contrast. These issues affect the accuracy of pelvic treatment plans and implementation of the treatment, hence requiring correction.PurposeA novel stacked coarse‐to‐fine model combining Denoising Diffusion Probabilistic Model (DDPM) and spatial‐frequency domain convolution modules is proposed to enhance the imaging quality of CBCT images.MethodsThe enhancement of low‐quality CBCT images is divided into two stages. In the coarse stage, the improved DDPM with U‐ConvNeXt architecture is used to complete the denoising task of CBCT images. In the fine stage, the deep convolutional network model jointly constructed by fast Fourier and dilated convolution modules is used to further enhance the image quality in local details and global imaging. Finally, the accurate pseudo‐CT (pCT) images consistent with the size of the original data are obtained. Two hundred fifty paired CBCT‐CT images from cervical and rectal cancer, combined with 200 public dataset cases, were used collectively for training, validation, and testing.ResultsTo evaluate the anatomical consistency between pCT and real CT, we have used the mean(std) of structure similarity index measure (SSIM), peak signal to noise ratio (PSNR), and normalized cross‐correlation (NCC). The numerical results for the above three metrics comparing the pCT synthesized by the proposed model against real CT for cervical cancer cases were 87.14% (2.91%), 34.02 dB (1.35 dB), and 88.01% (1.82%), respectively. For rectal cancer cases, the corresponding results were 86.06% (2.70%), 33.50 dB (1.41 dB), and 87.44% (1.95%). The paired t‐test analysis between the proposed model and the comparative models (ResUnet, CycleGAN, DDPM, and DDIM) for these metrics revealed statistically significant differences (p < 0.05). The visual results also showed that the anatomical structures between the real CT and the pCT synthesized by the proposed model were closer. For the dosimetric verification, mean absolute error of dosimetry (MAEdoes) values for the maximum dose (Dmax), the minimum dose (Dmin), and the mean dose (Dmean) in the planning target volume (PTV) were analyzed, with results presented as mean (lower quartile, upper quartile). The experimental results show that the values of the above three dosimetry indexes (Dmin, Dmax, and Dmean) for the pCT images synthesized by the proposed model were 0.90% (0.48%, 1.29%), 0.82% (0.47%, 1.17%), and 0.57% (0.44%, 0.67%). Compared with 10 cases of the original CBCT image by Mann–Whitney test (p < 0.05), it also proved that pCT can significantly improve the accuracy of HU values for the dose calculation.ConclusionThe pCT synthesized by the proposed model outperforms the comparative models in numerical accuracy and visualization, promising for ART of pelvic cancers.

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Experiment-based scatter correction for cone-beam computed tomography using the statistical method

Cited by 8 publications

References 9 publications

Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT

Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT

X-Ray Scatter Correction on Soft Tissue Images for Portable Cone Beam CT

Pseudo‐CT synthesis in adaptive radiotherapy based on a stacked coarse‐to‐fine model: Combing diffusion process and spatial‐frequency convolutions

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