Implementation of an efficient Monte Carlo calculation for CBCT scatter correction: phantom study

Watson, Peter; Mainegra-Hing, Ernesto; Tomic, Nada; Seuntjens, J

doi:10.1120/jacmp.v16i4.5393

Cited by 19 publications

(16 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MC simulation technique overcomes the inaccuracy caused by the degradation model, but it requires time-consuming statistical experiment. Reportedly, the MC-based iterative scatter correction can accurately reproduce the attenuation coefficients of a mathematical phantom, and has been clinically verified [10][11][12]. Because this type of iterative approach can be directly applied to the primitive projection data from CBCT, and the patient's prior information is not necessary, it is useful with respect to clinical implementation.…”

Section: Scatter Correction Methodsmentioning

confidence: 97%

“…Then, the average score and variance over all 360 images for each index were taken, as listed in Table 3. We compared the proposed method with the MC-based iterative scatter correction [10]. In the iterative method, we performed the MC simulation in such a way that 1000 photons on average were collected per pixel, which amounted to 32,768,000 photons for an image size of 256 × 128.…”

Section: Performance Evaluation With Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Learning-Based Scatter Correction of Simulated X-ray Images

Lee

2019

Electronics

View full text Add to dashboard Cite

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.

show abstract

Section: Scatter Correction Methodsmentioning

confidence: 97%

Section: Performance Evaluation With Comparisonmentioning

confidence: 99%

A Deep Learning-Based Scatter Correction of Simulated X-ray Images

Lee

2019

Electronics

View full text Add to dashboard Cite

show abstract

“…The aforementioned methods reduce the scatter during data acquisition but do not eliminate it completely. Monte Carlo methods estimate the scatter distribution after data acquisition and subtract it from the projections [21].…”

Section: Cone-beam Ct Scatter Reductionmentioning

confidence: 99%

Improving soft tissue imaging with volume-of-interest cone-beam CT

Huynh¹

2021

Preprint

View full text Add to dashboard Cite

Current cone-beam CT systems acquire full field-of-view projections in which x-ray scatter degrades the contrast of soft-tissue in the reconstructed images. The objective of this work was to simulate volume-of-interest (VOI) imaging, which reduces scatter and dose to the patient through beam collimation, to investigate the improvements in soft-tissue visibility on the Gamma Knife Icon. The results indicated that as field size decreased, contrast and noise increased, leading to only modest improvements in the contrast-to-noise ratio when using the same initial photon fluence. A reconstruction framework called the interior virtual method was adapted to suppress truncation-induced artifacts and noise in the VOI image. In this framework the projection data were extrapolated using a cosine function, an intermediate image was reconstructed analytically, and virtual projections of the intermediate image were created for iterative reconstruction. The framework supports high quality VOI reconstruction and can allow clinicians to optimize dose for soft-tissue visualization.

show abstract

“…Numerous hardware‐ and/or software‐based approaches have been proposed to estimate and correct for the remaining scatter signal. While there are too many of these techniques to enumerate, they can broadly be classified as belonging to one of several categories including: (a) source intensity modulation or blocking, (b) scatter deconvolution, (c) utilizing a priori knowledge of the object, (d) Monte Carlo modeling, or (e) other empirical approaches …”

Section: Introductionmentioning

confidence: 99%