Scatter distribution estimated and corrected by using Convolutional Neural Network for multi-slice CT system

Wang, Yang; Zhang, Ruikang; Chen, Kai; Tian, Yi

doi:10.1117/12.2581707

Cited by 2 publications

(3 citation statements)

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“…Since then, the application of deep learning for scatter correction is a field of growing interest. [42][43][44][45][46][47][48][49][50][51] A limitation of learning-based algorithms is that they require large datasets for training the neural network. Such datasets may not always be available for a scatter correction on a specific scanner type and many Monte Carlo simulations for different settings are required.…”

Section: Introductionmentioning

confidence: 99%

“…proposed the deep scatter estimation, 40,41 a method that uses a deep convolutional neural network to estimate scatter intensities from input projections. Since then, the application of deep learning for scatter correction is a field of growing interest 42–51 . A limitation of learning‐based algorithms is that they require large datasets for training the neural network.…”

Section: Introductionmentioning

confidence: 99%

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Empirical scatter correction: CBCT scatter artifact reduction without prior information

et al. 2022

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Background The image quality of cone beam CT (CBCT) scans severely suffers from scattered radiation if no countermeasures are taken. Scatter artifacts may induce cupping and streak artifacts and lead to a reduced image contrast and wrong CT values of the reconstructed volumes. Established software‐based approaches for a correction of scattered radiation typically rely on prior knowledge of the CT system, scan parameters, the scanned object, or all of the aforementioned. Purpose This study proposes a simple and effective postprocessing software‐based correction method of scatter artifacts in CBCT scans without specific prior knowledge. Methods We propose the empirical scatter correction (ESC), which generates scatter‐like basis images from each projection image by convolution operations. A linear combination of these basis images is subtracted from the original projection image. The logarithm is taken and an FDK reconstruction is performed. The coefficients needed for the linear combination are determined automatically by a downhill simplex algorithm such that the resulting reconstructed images show no scatter artifacts. We demonstrate the potential of ESC by correcting simulated volumes with Monte Carlo scatter artifacts, a head phantom scan performed on our table‐top CBCT, and a pelvis scan from a Varian Edge CBCT scanner. Results ESC is able to improve the image quality of CBCT scans, which is shown on the basis of our simulations and on measured data. For a simulated head CT, the CT value difference to the scatter‐free reference image was as low as −6 HU after using ESC, whereas the uncorrected data deviated by more than −200 HU from the reference data. Simulations of thorax and abdomen CT scans show that although scatter artifacts are not fully removed, anatomical features which were hard to discover prior to the correction become clearly visible and better segmentable with ESC. Similar results are obtained in the phantom measurement, where a comparison to a slit scan of our head phantom shows only small differences. The CT values in soft tissue are improved in this measurement, as well. In soft tissue areas with severe scatter artifacts, the CT values agree well with those of the slit scan (difference to slit scan: 35 HU corrected and −289 HU uncorrected). Scatter artifacts in measured patient data can also be reduced using the proposed ESC. The results are comparable to those achieved with designated correction algorithms installed on the Varian Edge CBCT system. Conclusions ESC allows to reduce artifacts caused by patient scatter solely based on the projection data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical scatter correction: CBCT scatter artifact reduction without prior information

et al. 2022

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“…A correction algorithm can be based on real time Monte Carlo simulations (Poludniowski et al 2009, Xu et al 2015, which are accurate but require the support of high performance hardware. Recently, with the progress in the field of artificial intelligence, deep learning based correction models for scatter signals have been investigated by many researchers across the world as they can reach a similar level of accuracy as Monte Carlo simulations and have better performances but require large amount of data to train the learning models , Lee and Lee 2019, Jiang et al 2019, Wang et al 2021, Erath et al 2021. Nowadays, most widely used correction algorithms are convolution kernel based due to the high performance and ability to approximately retrieve scatter signals (Li et al 2008, Star-Lack et al 2009, Kim et al 2015, Bhatia et al 2017.…”

Section: Introductionmentioning

confidence: 99%

Correction of Bowtie filter induced scatter signals based on air scan data and object scan data

Zhang¹,

Wang²,

Chen³

et al. 2022

Biomed. Phys. Eng. Express

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In a cone beam CT system, a bowtie filter brings in additional scatter signals with respect to object induced scatter signals, which can degrade image quality and sometimes result in artifacts. This work aims to improve the image quality of CT scans by analyzing the contribution of bowtie filter induced scatter signals and removing them from projection data. Air calibration is a very useful preprocessing step to eliminate the response variations of detector pixels. Bowtie filter induced scattered X-ray signals of air scans are recorded in air calibration tables and therefore considered as a part of primary signals. However, scattered X-rays behave differently in scanned objects compared to primary X-rays. The difference should be corrected to eliminate the impact of bowtie filter induced scatter signals. A kernel based correction algorithm based on air scan data, named bowtie filter scatter correction algorithm, is applied to estimate and to eliminate the bowtie filter induced scatter signals in object scans. The scatter signals of air scans can be measured with air scans or retrieved from air calibration tables of a CT system, and can be used as input of the correction algorithm to estimate the change of scatter signals caused by the scanned objects in the scan field. Based on the assumption that the scatter signals in the projection data scanned with narrow collimation can be neglected, the difference signals between narrow and broad collimations can be used to estimate bowtie filter induced scatter signals for air scans with the correction of extra-focal radiations (EFRs). The calculated bowtie filter induced scatter signals have been compared with the results of Monte Carlo simulations, and the parameters of correction algorithm have been determined by fitting the measured scatter signal curves of phantom scans with calculated curves. Projection data have been reconstructed using Filtered BackProjection (FBP) method with and without bowtie filter correction to check whether the image quality is improved. Scatter signals can be well approximated with the bowtie filter scatter correction algorithm together with an existing object scatter correction algorithm. After removing the bowtie filter induced scatter signals, the dark bands in reconstructed images in the regions near the edges of scanned objects can be mostly eliminated. The difference signals of air scan data between narrow and broad collimations can be used to estimate the bowtie filter induced scatter for air scans. The proposed bowtie filter scatter correction algorithm using air scan data can be applied to estimate and to remove most of the bowtie filter induced scatter signals in

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Scatter distribution estimated and corrected by using Convolutional Neural Network for multi-slice CT system

Cited by 2 publications

References 0 publications

Empirical scatter correction: CBCT scatter artifact reduction without prior information

Empirical scatter correction: CBCT scatter artifact reduction without prior information

Correction of Bowtie filter induced scatter signals based on air scan data and object scan data

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