A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder

Heyden, Brent van der; Uray, Martin; Fonseca, Gabriel Paiva; Huber, Philipp; Us, Defne; Messner, I.M.; Law, Adam; Parii, Anastasiia; Niklas, Reisz,; Rinaldi, I.; Freixas, G. Vilches; Deutschmann, H.; Verhaegen, Frank; Steininger, Philipp

doi:10.1088/1361-6560/ab8954

Cited by 15 publications

(10 citation statements)

References 52 publications

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“…[36][37][38] In our work, 2D ASG combined with the GSS method provided better HU accuracy than the standard clinical CBCT protocol (Figs. [8][9][10][11][12], indicating that our approach has the potential to reduce CBCT-based dose calculation errors in radiation therapy.…”

Section: Discussionmentioning

confidence: 97%

“…These methods do not directly estimate and correct scatter, but they aim to correct degradation of CT number accuracy and CNR in CBCT images. Earlier image restoration methods have used heuristic approaches, 8–10 and more recently, deep learning‐based image synthesis methods have been explored 11,12 . The drawback of the latter is that they require extensive training data sets to generate CBCT images with image quality similar to gold‐standard multidetector CT (MDCT).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of scatter rejection and correction performance of 2D antiscatter grids in cone beam computed tomography

et al. 2021

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We have been investigating two-dimensional (2D) antiscatter grids (2D ASGs) to reduce scatter fluence and improve image quality in cone beam computed tomography (CBCT). In this work, two different aspects of 2D ASGs, their scatter rejection and correction capability, were investigated in CBCT experiments. To correct residual scatter transmitted through the 2D ASG, it was used as a scatter measurement device with a novel method: grid-based scatter sampling. Methods: Three focused 2D ASG prototypes with grid ratios of 8, 12, and 16 were developed for linac-mounted offset detector CBCT geometry. In the first phase, 2D ASGs were used as a scatter rejection device, and the effect of grid ratio on CT number accuracy and contrast-to-noise ratio (CNR) evaluated in CBCT images. In the second phase, a grid-based scatter sampling method which exploits the signal modulation characteristics of the 2D ASG's septal shadows to measure and correct residual scatter transmitted through the grid was implemented. To evaluate CT number accuracy, the percent change in CT numbers was measured by changing the phantom from head to pelvis size and configuration. Results: When 2D ASG was used as a scatter rejection device, CT number accuracy increased and the CT number variation due to change in phantom dimensions was reduced from 23% to 2-6%. A grid ratio of 16 yielded the lowest CT number variation. All three 2D ASGs yielded improvement in CNR, up to a factor of two in pelvis-sized phantoms. When 2D ASG prototypes were used for both scatter rejection and correction, CT number variations were reduced further, to 1.3-2.6%. In comparisons with a clinical CBCT system and a high-performance radiographic ASG, 2D ASG provided higher CT number accuracy under the same imaging conditions. Conclusions: When 2D ASG is used solely as a scatter rejection device, substantial improvement in CT number accuracy can be achieved by increasing the grid ratio. Two-dimensional ASGs also provided significant CNR improvement even at lower grid ratios. Two-dimensional ASGs used in conjunction with the grid-based scatter sampling method provided further improvement in CT number accuracy, irrespective of the grid ratio, while preserving 2D ASGs' capacity to improve CNR. The combined effect of scatter rejection and residual scatter correction by 2D ASG may accelerate implementation of new techniques in CBCT that require high quantitative accuracy, such as radiotherapy dose calculation and dual energy CBCT.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Evaluation of scatter rejection and correction performance of 2D antiscatter grids in cone beam computed tomography

et al. 2021

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“…Recent works propose to use deep convolutional networks which learn from CBCT projections simulated via Monte Carlo. They generate estimated scatter images (projections) as output based on raw projections as input [75,78,87,145]. Once trained, the network can replace the Monte Carlo simulation and be used as scatter estimator within the image reconstruction workflow.…”

Section: Ai For Modelling Scattermentioning

confidence: 99%

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

et al. 2021

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Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as 1) patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for radio-protection investigations of ionizing radiation-based imaging systems (CT, nuclear imaging), 2) development of numerous imaging detectors, in X-ray imaging (conventional CT, dual-energy, multi-spectral, phase contrast … ), nuclear imaging (PET, SPECT, Compton Camera) or even advanced specific imaging methods such as proton/ion imaging, or prompt-gamma emission distribution estimation in hadrontherapy monitoring. Monte Carlo simulation is a key tool both in academic research labs as well as industrial research and development services. Because of the very nature of the Monte Carlo method, involving iterative and stochastic estimation of numerous probability density functions, the computation time is high. Despite the continuous and significant progress on computer hardware and the (relative) easiness of using code parallelisms, the computation time is still an issue for highly demanding and complex simulations. Hence, since decades, Variance Reduction Techniques have been proposed to accelerate the processes in a specific configuration. In this article, we review the recent use of Artificial Intelligence methods for Monte Carlo simulation in medical physics and their main associated challenges. In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. In particular, we will focus on dose estimation with convolutional neural networks, dose denoising from low statistics Monte Carlo simulations, detector modelling and event selection with neural networks, generative networks for source and phase space modelling. The expected interests of those approaches are discussed. In the second section, we focus on the current challenges that still arise in this promising field.

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“…van der Heyden et al ( 31 ) presented the first projection-based scatter removal algorithm for isocentric and nonisocentric CBCT imaging using a deep convolutional autoencoder trained on MC-composed datasets. The algorithm was successfully applied to real patient data.…”

Section: Discussionmentioning

confidence: 99%

Feasibility evaluation of kilovoltage cone-beam computed tomography dose calculation following scatter correction: investigations of phantom and representative tumor sites

Meng¹,

Qiu²,

Zhang³

et al. 2021

Transl Cancer Res TCR

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Background: To study the feasibility of kilovoltage cone-beam computed tomography (KV-CBCT) dose calculation following scatter correction.Methods: CIRS 062 and Catphan 504 phantoms were used in this study, and 40 randomly selected subjects representing a variety of cases (ten head cancer cases, ten chest cancer cases, ten abdominal cancer cases and ten pelvic cavity cancer cases) were enrolled. We developed in-house software called the cone-beam CT imaging toolkit (CITK) to improve the quality of CBCT images. We first aligned each planning computed tomography (pCT) image with the corresponding CBCT image using rigid registration after scatter correction. Hounsfield unit-relative electron density (HU-RED) calibration was applied to the CBCT images. The pCT plan was then recalculated on CBCT images. Finally, the dosimetric differences between the two plans were evaluated. The dosimetric parameters included the D98, D2, Dmean, conformity index (CI), homogeneity index (HI) and other organ at risk (OAR) dose parameters of the planning target volume (PTV). The dose distribution index (DDI) and the gamma index were also assessed. Paired Student's t-tests or Wilcoxon rank tests were used to evaluate differences. P<0.05 was considered significant.Results: In the phantom and patient cases, the average dosimetric difference was less than 1% in the PTV and OARs. There was no significant difference in the CI or HI between the two plans. The gamma pass rate of 2%/2 mm was greater than 95% in both plans. There was a significant difference in the DDI between the two plans in the chest group but not in the other groups. Conclusions:The results suggest that CBCT has high accuracy in dose calculation via scatter correction and HU-RED calibration.

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A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder

Cited by 15 publications

References 52 publications

Evaluation of scatter rejection and correction performance of 2D antiscatter grids in cone beam computed tomography

Evaluation of scatter rejection and correction performance of 2D antiscatter grids in cone beam computed tomography

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

Feasibility evaluation of kilovoltage cone-beam computed tomography dose calculation following scatter correction: investigations of phantom and representative tumor sites

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