A head motion estimation algorithm for motion artifact correction in dental CT imaging

Hernández, Daniel; Eldib, Mohamed Elsayed; Hegazy, Mohamed A. A.; Cho, Myung Hye; Cho, Min Hyoung; Lee, Soo Yeol

doi:10.1088/1361-6560/aab17e

Cited by 8 publications

(10 citation statements)

References 30 publications

(27 reference statements)

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“…For different image deblurring algorithms, our algorithm is compared with Sun et al's algorithm [5], Zhang et al's algorithm [6], Wang et al's algorithm [7], and Hernandez et al's algorithm [8]. Figure 10 shows the comparison results of the liver tumour patient of different algorithms with the blurred amplitude of 25 pixels and blurred angle of 45°.…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

“…Wang et al [7] used a sparse motion composition method to obtain an estimation of pulmonary motion which linearly combines the respiratory deformation vector field of training samples, subsequently adopting parametric control points on CT to refine the nonrigid pulmonary deformation. Hernandez et al [8] computed the cross-correlation between every two successive projection images, which estimated the motion waveform from the projection images. However, these motion compensation methods obtain the patient's motion information by means of device tracking or multiframe merging, without saving effective information by its characteristics.…”

Section: Introductionmentioning

confidence: 99%

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A Novel 3D Reconstruction Algorithm of Motion-Blurred CT Image

Zhang

Guo

et al. 2020

Computational and Mathematical Methods in Medicine

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The majority of medical workers are eager to obtain realistic and real-time CT 3D reconstruction results. However, autonomous or involuntary motion of patients can cause blurring of CT images. For the 3D reconstruction scene of motion-blurred CT image, this paper consists of two parts: firstly, a GAN image translation network deblurring algorithm is proposed to remove blurred results. This algorithm adopts the clear image to supervise the training process of the blurred image, which creates solutions that are close to the clear image. Secondly, this paper proposes a Marching Cubes (MC) algorithm based on the fusion of golden section and isosurface direction smooth (GI-MC) for 3D reconstruction of CT images. The golden section algorithm is used to calculate the equivalent points and normal vectors, which reduces the calculation numbers from four to one. The isosurface direction smooth algorithm computes the mean value of the normal vector, so as to smooth the direction of all triangular patches in spatial arrangement. The experimental results show that for different blurred angle and blurred amplitude, comparing the results of the Shannon entropy ratio and peak signal-to-noise ratio, our GAN image translation network deblurring algorithm has better restoration than other algorithms. Furthermore, for different types of liver patients, the reconstruction accuracy of our GI-MC algorithm is 9.9%, 7.7%, and 3.9% higher than that of the traditional MC algorithm, Li’s algorithm, and Pratomo’s algorithm, respectively.

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Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel 3D Reconstruction Algorithm of Motion-Blurred CT Image

Zhang

Guo

et al. 2020

Computational and Mathematical Methods in Medicine

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“…Apart from cone-beam artifacts, there are many other types of artifacts in dental CT images such as metal artifacts [ 24 , 25 , 26 ], motion artifacts [ 27 , 28 , 29 ], and limited-view-induced streak artifacts [ 30 , 31 , 32 ]. These artifacts can also induce errors in the 3D models.…”

Section: Discussionmentioning

confidence: 99%

Cone-Beam Angle Dependency of 3D Models Computed from Cone-Beam CT Images

Cho

Hegazy²,

Lee

2022

Sensors

Self Cite

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Cone-beam dental CT can provide high-precision 3D images of the teeth and surrounding bones. From the 3D CT images, 3D models, also called digital impressions, can be computed for CAD/CAM-based fabrication of dental restorations or orthodontic devices. However, the cone-beam angle-dependent artifacts, mostly caused by the incompleteness of the projection data acquired in the circular cone-beam scan geometry, can induce significant errors in the 3D models. Using a micro-CT, we acquired CT projection data of plaster cast models at several different cone-beam angles, and we investigated the dependency of the model errors on the cone-beam angle in comparison with the reference models obtained from the optical scanning of the plaster models. For the 3D CT image reconstruction, we used the conventional Feldkamp algorithm and the combined half-scan image reconstruction algorithm to investigate the dependency of the model errors on the image reconstruction algorithm. We analyzed the mean of positive deviations and the mean of negative deviations of the surface points on the CT-image-derived 3D models from the reference model, and we compared them between the two image reconstruction algorithms. It has been found that the model error increases as the cone-beam angle increases in both algorithms. However, the model errors are smaller in the combined half-scan image reconstruction when the cone-beam angle is as large as 10 degrees.

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“…Furthermore, the problem can be exacerbated by unintended motions of the patient support such as a vibrating patient support. 1,2 Unexpected motion during CT scans causes data discontinuities which in turn introduce streaks or star artifacts F I G U R E 1 Clinical images with motion artifacts originating from anatomical structures with a high gradient. As shown in Figure 1, motion-induced artifacts in head images mainly originate from the skull.…”

Section: Introductionmentioning

confidence: 99%

A deep learning method for eliminating head motion artifacts in computed tomography

Wen

Liu

et al. 2021

Medical Physics

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Purpose Involuntary patient movement results in data discontinuities during computed tomography (CT) scans which lead to a serious degradation in the image quality. In this paper, we specifically address artifacts induced by patient motion during a head scan. Method Instead of trying to solve an inverse problem, we developed a motion simulation algorithm to synthesize images with motion‐induced artifacts. The artifacts induced by rotation, translation, oscillation and any possible combination are considered. Taking advantage of the powerful learning ability of neural networks, we designed a novel 3D network structure with both a large reception field and a high image resolution to map the artifact‐free images from artifact‐contaminated images. Quantitative results of the proposed method were evaluated against the results of U‐Net and proposed networks without dilation structure. Thirty sets of motion contaminated images from two hospitals were selected to do a clinical evaluation. Result Facilitating the training dataset with artifacts induced by variable motion patterns and the neural network, the artifact can be removed with good performance. Validation dataset with simulated random motion pattern showed outperformed image correction, and quantitative results showed the proposed network had the lowest normalized root‐mean‐square error, highest peak signal‐to‐noise ratio and structure similarity, indicating our network gave the best approximation of gold standard. Clinical image processing results further confirmed the effectiveness of our method. Conclusion We proposed a novel deep learning‐based algorithm to eliminate motion artifacts. The convolutional neural networks trained with synthesized image pairs achieved promising results in artifacts reduction. The corrected images increased the diagnostic confidence compared with artifacts contaminated images. We believe that the correction method can restore the ability to successfully diagnose and avoid repeated CT scans in certain clinical circumstances.

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A head motion estimation algorithm for motion artifact correction in dental CT imaging

Cited by 8 publications

References 30 publications

A Novel 3D Reconstruction Algorithm of Motion-Blurred CT Image

A Novel 3D Reconstruction Algorithm of Motion-Blurred CT Image

Cone-Beam Angle Dependency of 3D Models Computed from Cone-Beam CT Images

A deep learning method for eliminating head motion artifacts in computed tomography

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