Optimal combination of anti‐scatter grids and software correction for CBCT imaging

Stankovic, Uros; Ploeger, Lennert S.; Herk, Marcel van; Sonke, Jan‐Jakob

doi:10.1002/mp.12385

Cited by 50 publications

(67 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many methods have been proposed to cope with the shortcomings of CBCT, including antiscatter grids and software‐based solutions 10–15 . Convolution‐based methods, in which the scatter is modeled as a convolution of the projections, can be found in routine clinical use 16–19 …”

Section: Introductionmentioning

confidence: 99%

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

et al. 2018

View full text Add to dashboard Cite

Using a deep convolutional neural network for CBCT intensity correction was shown to be feasible in the pelvic region for the first time. Dose calculation accuracy on CBCT was high for VMAT, but unsatisfactory for IMPT. With respect to the reference technique (CBCT ), the neural network enabled a considerable increase in speed for intensity correction and might eventually allow for on-the-fly shading correction during CBCT acquisition.

show abstract

Section: Introductionmentioning

confidence: 99%

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Stankovic et al developed an iterative model relying on a partial-beam blocking method while simultaneously characterizing the scatter reduction for a variety of anti-scatter grids. 8 While hardware approaches to scatter correction have proven promising, they inherently reduce the system's quantum efficiency and can degrade image quality. Installation and setup of devices on the onboard imager further inhibit clinical implementation.…”

Section: Introductionmentioning

confidence: 99%

Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography

et al. 2019

View full text Add to dashboard Cite

Purpose The incorporation of cone‐beam computed tomography (CBCT) has allowed for enhanced image‐guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning‐based method for generating high quality corrected CBCT (CCBCT) images is proposed. Methods The proposed method integrates a residual block concept into a cycle‐consistent adversarial network (cycle‐GAN) framework, called res‐cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle‐GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end‐to‐end CBCT‐to‐CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), normalized cross‐correlation (NCC) indices, and spatial non‐uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning‐based CBCT correction method. Results Overall, the MAE, PSNR, NCC, and SNU were 13.0 HU, 37.5 dB, 0.99, and 0.05 in the brain, 16.1 HU, 30.7 dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning‐based method. Conclusions The authors have developed a novel deep learning‐based method to generate high‐quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.

show abstract

“…The conventional approach to dose calculation using CBCT is to transform PlanCT images to CBCT images using deformable image registration (DIR) and then calculate the dose distribution using the transformed PlanCT . Another approach to dose calculation using CBCT is to compute the dose distribution directly with the improved CBCT images . Improving the CBCT image quality while preserving the anatomical structures of CBCT should be effective for both approaches.…”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11] Another approach to dose calculation using CBCT is to compute the dose distribution directly with the improved CBCT images. 12 Improving the CBCT image quality while preserving the anatomical structures of CBCT should be effective for both approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods have been applied to remove the scatter photons from CBCT two-dimensional (2D) projection images, such as hardware suppression using an anti-scatter grid and software estimation using a scatter deconvolution technique. [12][13][14] On the other hand, a different approach has been proposed, which uses PlanCT as prior information to improve the quality of CBCT images. This approach is applicable to direct three-dimensional (3D) image reconstruction with sparse sampling, 15 histogram matching, 16 super-resolution, 17 and a deep convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Visual enhancement of Cone‐beam CT by use of CycleGAN

et al. 2020

View full text Add to dashboard Cite

Purpose Cone‐beam computed tomography (CBCT) offers advantages over conventional fan‐beam CT in that it requires a shorter time and less exposure to obtain images. However, CBCT images suffer from low soft‐tissue contrast, noise, and artifacts compared to conventional fan‐beam CT images. Therefore, it is essential to improve the image quality of CBCT. Methods In this paper, we propose a synthetic approach to translate CBCT images with deep neural networks. Our method requires only unpaired and unaligned CBCT images and planning fan‐beam CT (PlanCT) images for training. The CBCT images and PlanCT images may be obtained from other patients as long as they are acquired with the same scanner settings. Once trained, three‐dimensionally reconstructed CBCT images can be directly translated into high‐quality PlanCT‐like images. Results We demonstrate the effectiveness of our method with images obtained from 20 prostate patients, and provide a statistical and visual comparison. The image quality of the translated images shows substantial improvement in voxel values, spatial uniformity, and artifact suppression compared to those of the original CBCT. The anatomical structures of the original CBCT images were also well preserved in the translated images. Conclusions Our method produces visually PlanCT‐like images from CBCT images while preserving anatomical structures.

show abstract

Optimal combination of anti‐scatter grids and software correction for CBCT imaging

Cited by 50 publications

References 31 publications

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

ScatterNet: A convolutional neural network for cone‐beam CT intensity correction

Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography

Visual enhancement of Cone‐beam CT by use of CycleGAN

Contact Info

Product

Resources

About