An energy minimization method for the correction of cupping artifacts in cone‐beam CT

Xie, Sihong; Zhuang, Wenqin; Li, Haibo

doi:10.1120/jacmp.v17i4.6023

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…Several attempts have been made to quantify CBCT images in radiotherapy (RT) using both model‐based methods 4–6 as well as the more recent deep learning‐based methods 7,8 . CBCT imaging suffers from several types of artifacts and these methods focus on the correction of only a particular type of artifacts such as either beam hardening, 9,10 scattering, 5 metal artifacts, 6 or cupping 11 . Instead of trying to fix particular noise artifacts in CBCT images, a more recent line of research using deep learning methods attempts to directly generate higher‐quality synthetic CT (sCT) from CBCT images.…”

Section: Introductionmentioning

confidence: 99%

“…7,8 CBCT imaging suffers from several types of artifacts and these methods focus on the correction of only a particular type of artifacts such as either beam hardening, 9,10 scattering, 5 metal artifacts, 6 or cupping. 11 Instead of trying to fix particular noise artifacts in CBCT images, a more recent line of research using deep learning methods attempts to directly generate higher-quality synthetic CT (sCT) from CBCT images. One particular approach is to use cycle-consistent generative adversarial networks 12 (CycleGAN) to generate sCT from CBCT images.…”

Section: Introductionmentioning

confidence: 99%

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Multitask 3D CBCT‐to‐CT translation and organs‐at‐risk segmentation using physics‐based data augmentation

et al. 2021

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Purpose In current clinical practice, noisy and artifact‐ridden weekly cone beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is performed once at the beginning of the treatment using high‐quality planning CT (pCT) images and manual contours for organs‐at‐risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid‐treatment as well as for deriving biomarkers for treatment response. Methods Using a novel physics‐based data augmentation strategy, we synthesize a large dataset of perfectly/inherently registered pCT and synthetic‐CBCT pairs for locally advanced lung cancer patient cohort, which are then used in a multitask three‐dimensional (3D) deep learning framework to simultaneously segment and translate real weekly CBCT images to high‐quality pCT‐like images. Results We compared the synthetic CT and OAR segmentations generated by the model to real pCT and manual OAR segmentations and showed promising results. The real week 1 (baseline) CBCT images which had an average mean absolute error (MAE) of 162.77 HU compared to pCT images are translated to synthetic CT images that exhibit a drastically improved average MAE of 29.31 HU and average structural similarity of 92% with the pCT images. The average DICE scores of the 3D OARs segmentations are: lungs 0.96, heart 0.88, spinal cord 0.83, and esophagus 0.66. Conclusions We demonstrate an approach to translate artifact‐ridden CBCT images to high‐quality synthetic CT images, while simultaneously generating good quality segmentation masks for different OARs. This approach could allow clinicians to adjust treatment plans using only the routine low‐quality CBCT images, potentially improving patient outcomes. Our code, data, and pre‐trained models will be made available via our physics‐based data augmentation library, Physics‐ArX, at https://github.com/nadeemlab/Physics-ArX.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multitask 3D CBCT‐to‐CT translation and organs‐at‐risk segmentation using physics‐based data augmentation

et al. 2021

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“…In recent years, several model‐based methods have been investigated to reduce scatter, metal, cupping, and beam‐hardening artifacts in CBCT . In addition to these conventional model‐based artifact‐reduction methods, convolutional neural networks (CNNs)‐based methods have also been explored for image quality enhancement for CT .…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, several model-based methods have been investigated to reduce scatter, metal, cupping, and beam-hardening artifacts in CBCT. [6][7][8][9][10][11][12] In addition to these conventional model-based artifact-reduction methods, convolutional neural networks (CNNs)-based methods have also been explored for image quality enhancement for CT. 13,14 While these methods can improve the quality of CT to some extent, they often only focus on one source of artifacts, for example, removing scatter signal, or reducing metal artifacts only. Rather than focusing on the correction for a specific artifact, we aim to generate a synthetic CT (sCT) which has the planning CT level image quality from the on-treatment CBCT which is routinely available in the radiation therapy.…”

Section: Introductionmentioning

confidence: 99%

Synthetic CT generation from CBCT images via deep learning

et al. 2020

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Purpose Cone‐beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on‐treatment CBCT) for accurate patient setup in image‐guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as dose calculation and treatment planning. Motivated by the promising performance of deep learning in medical imaging, we propose a deep U‐net‐based approach that synthesizes CT‐like images with accurate numbers from planning CT, while keeping the same anatomical structure as on‐treatment CBCT. Methods We formulated the CT synthesis problem under a deep learning framework, where a deep U‐net architecture was used to take advantage of the anatomical structure of on‐treatment CBCT and image intensity information of planning CT. U‐net was chosen because it exploits both global and local features in the image spatial domain, matching our task to suppress global scattering artifacts and local artifacts such as noise in CBCT. To train the synthetic CT generation U‐net (sCTU‐net), we include on‐treatment CBCT and initial planning CT of 37 patients (30 for training, seven for validation) as the input. Additional replanning CT images acquired on the same day as CBCT after deformable registration are utilized as the corresponding reference. To demonstrate the effectiveness of the proposed sCTU‐net, we use another seven independent patient cases (560 slices) for testing. Results We quantitatively compared the resulting synthetic CT (sCT) with the original CBCT image using deformed same‐day pCT images as reference. The averaged accuracy measured by mean absolute error (MAE) between sCT and reference CT (rCT) on testing data is 18.98 HU, while MAE between CBCT and rCT is 44.38 HU. Conclusions The proposed sCTU‐net can synthesize CT‐quality images with accurate CT numbers from on‐treatment CBCT and planning CT. This potentially enables advanced CBCT applications for adaptive treatment planning.

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“…Several attempts have been made to quantify CBCT images in radiotherapy (RT) using both modelbased methods [4,5,6] as well as the more recent deep learning based methods [7,8]. CBCT imaging suffers from several types of artifacts and these methods focus on the correction of only a particular type of artifacts such as either beam hardening [9,10], scattering [5], metal artifacts [6], or cupping [11].…”

Section: Introductionmentioning

confidence: 99%

Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation

Dahiya,

Alam,

Zhang

et al. 2021

Preprint

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In current clinical practice, noisy and artifact-ridden weekly cone-beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment as well as for deriving biomarkers for treatment response.Methods: Using a novel physics-based data augmentation strategy, we synthesize a large dataset of perfectly/inherently registered planning CT and synthetic-CBCT pairs for locally advanced lung cancer patient cohort, which are then used in a multitask 3D deep learning framework to simultaneously segment and translate real weekly CBCT images to high-quality planning CT-like images.Results: We compared the synthetic CT and OAR segmentations generated by the model to real planning CT and manual OAR segmentations and showed promising results. The real week 1 (baseline) CBCT images which had an average MAE of 162.77 HU compared to pCT images are translated to synthetic CT images that exhibit a drastically improved average MAE of 29.31 HU and average structural similarity of 92% with the pCT images. The average DICE scores of the 3D organs-at-risk segmentations are: lungs 0.96, heart 0.88, spinal cord 0.83 and esophagus 0.66. Conclusions:We demonstrate an approach to translate artifact-ridden CBCT images to high quality

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An energy minimization method for the correction of cupping artifacts in cone‐beam CT

Cited by 10 publications

References 23 publications

Multitask 3D CBCT‐to‐CT translation and organs‐at‐risk segmentation using physics‐based data augmentation

Multitask 3D CBCT‐to‐CT translation and organs‐at‐risk segmentation using physics‐based data augmentation

Synthetic CT generation from CBCT images via deep learning

Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation

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