Reduction of beam hardening artifacts in cone-beam CT imaging via SMART-RECON algorithm

Li, Yinsheng; Garrett, John W.; Chen, Guang-Hong

doi:10.1117/12.2216882

Cited by 14 publications

(14 citation statements)

References 4 publications

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“…Many methods for correcting CBCT images with high quality have been proposed to produce quantitative CBCTs in the radiation therapy field, which do not require a calibration phantom during an object scan. These methods can be classified as hardware corrections such as anti-scatter grids, and model-based methods using Monte Carlo techniques to model the scatter to CBCT projections 29 – 34 . Recently, the generative adversarial network (GAN), a deep neural network model, has shown state-of-the-art performance in many image processing tasks 28 , 35 , 36 .…”

Section: Introductionmentioning

confidence: 99%

QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

Yong

Yang

Lee

et al. 2021

Sci Rep

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The purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net, and to compare the bone images enhanced by the QCBCT-NET with those by Cycle-GAN and U-Net. We used two phantoms of human skulls encased in acrylic, one for the training and validation datasets, and the other for the test dataset. We proposed the QCBCT-NET consisting of Cycle-GAN with residual blocks and a multi-channel U-Net using paired training data of quantitative CT (QCT) and CBCT images. The BMD images produced by QCBCT-NET significantly outperformed the images produced by the Cycle-GAN or the U-Net in mean absolute difference (MAD), peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity (SSIM), and linearity when compared to the original QCT image. The QCBCT-NET improved the contrast of the bone images by reflecting the original BMD distribution of the QCT image locally using the Cycle-GAN, and also spatial uniformity of the bone images by globally suppressing image artifacts and noise using the two-channel U-Net. The QCBCT-NET substantially enhanced the linearity, uniformity, and contrast as well as the anatomical and quantitative accuracy of the bone images, and demonstrated more accuracy than the Cycle-GAN and the U-Net for quantitatively measuring BMD in CBCT.

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Section: Introductionmentioning

confidence: 99%

QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

Yong

Yang

Lee

et al. 2021

Sci Rep

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“…Cracks often occur in teeth with long-standing restorations. Algorithmic correction during reconstruction is one correction route for these defects[31,32], but post-reconstruction correction is also common, especially in CT. Artifacts have been corrected in CT using different analytical approaches such as morphological operations[33,34], level set methods, registration[34] or, geometrical methods[35]. Specifically, geometrical methods have been successful in the past to trace the artifact rays.…”

Section: Discussionmentioning

confidence: 99%

Automatic quantification framework to detect cracks in teeth

Shah

Hernández

Budin

et al. 2018

Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Studies show that cracked teeth are the third most common cause for tooth loss in industrialized countries. If detected early and accurately, patients can retain their teeth for a longer time. Most cracks are not detected early because of the discontinuous symptoms and lack of good diagnostic tools. Currently used imaging modalities like Cone Beam Computed Tomography (CBCT) and intraoral radiography often have low sensitivity and do not show cracks clearly. This paper introduces a novel method that can detect, quantify, and localize cracks automatically in high resolution CBCT (hr-CBCT) scans of teeth using steerable wavelets and learning methods. These initial results were created using hr-CBCT scans of a set of healthy teeth and of teeth with simulated longitudinal cracks. The cracks were simulated using multiple orientations. The crack detection was trained on the most significant wavelet coefficients at each scale using a bagged classifier of Support Vector Machines. Our results show high discriminative specificity and sensitivity of this method. The framework aims to be automatic, reproducible, and open-source. Future work will focus on the clinical validation of the proposed techniques on different types of cracks ex-vivo. We believe that this work will ultimately lead to improved tracking and detection of cracks allowing for longer lasting healthy teeth.

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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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Reduction of beam hardening artifacts in cone-beam CT imaging via SMART-RECON algorithm

Cited by 14 publications

References 4 publications

QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

QCBCT-NET for direct measurement of bone mineral density from quantitative cone-beam CT: a human skull phantom study

Automatic quantification framework to detect cracks in teeth

Synthetic CT generation from CBCT images via deep learning

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