The Experts below are selected from a list of 183 Experts worldwide ranked by ideXlab platform
J Williamson - One of the best experts on this subject based on the ideXlab platform.
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SU‐FF‐J‐170: Impact of 4D Cone Beam CT View‐Aliasing Artifact On Nonrigid Registration Accuracy
Medical Physics, 2009Co-Authors: Geoffrey D. Hugo, W. Sleeman, J WilliamsonAbstract:Purpose: To evaluate the effect of 4D cone beam CT view‐Aliasing Artifact on nonrigid registration accuracy. Method and Materials: End exhalation (EE) and end inhalation (EI) volumes from a 4D multi‐slice CTimage (4DCT) of a research subject were registered using a small deformation inverse consistent linear elastic (SICLE) registration algorithm to produce a reference displacement vector field (DVF) between the two images. Artificial cone beam CT projections were generated for the EE and EI volumes at several different angular sampling patterns, mimicking 4D cone beam CT (4DCBCT) acquisition in a free‐breathing patient. The projections were reconstructed into EE and EI 4DCBCT phase images containing view‐Aliasing Artifact due to angular undersampling, but without CBCT scatter and beam hardening Artifacts. The EE and EI 4DCBCT images were registered using the SICLE algorithm to produce a test DVF. Test DVFs were generated at a range of angular frequencies from 0.17 projections per degree to 1.83 projections per degree (full angular sampling on a commercial cone beam CT system). The norm of the voxel by voxel vector difference between the test DVF and reference DVF within the patient's delineated lung volume was calculated. Results: The mean absolute error was significantly associated with the projection angular frequency (p < 0.05, exponential regression, R2=0.996). The median error was below 0.2 cm and 90% of the error was below 0.3 cm for the lowest frequency evaluated here of 60 projections per phase. However, above a sampling frequency of 0.33 projections per degree (120 projections per phase), 90% of the error was less than 0.1 cm in relation to the 4DCT registration. Conclusion: Using at least 120 projections per phase, view‐Aliasing Artifact had a minimal effect on the registration accuracy for the SICLE algorithm due to inherent elastic and smoothing constraints in the algorithm.
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su ff j 170 impact of 4d cone beam ct view Aliasing Artifact on nonrigid registration accuracy
Medical Physics, 2009Co-Authors: G Hugo, W. Sleeman, J WilliamsonAbstract:Purpose: To evaluate the effect of 4D cone beam CT view‐Aliasing Artifact on nonrigid registration accuracy. Method and Materials: End exhalation (EE) and end inhalation (EI) volumes from a 4D multi‐slice CTimage (4DCT) of a research subject were registered using a small deformation inverse consistent linear elastic (SICLE) registration algorithm to produce a reference displacement vector field (DVF) between the two images. Artificial cone beam CT projections were generated for the EE and EI volumes at several different angular sampling patterns, mimicking 4D cone beam CT (4DCBCT) acquisition in a free‐breathing patient. The projections were reconstructed into EE and EI 4DCBCT phase images containing view‐Aliasing Artifact due to angular undersampling, but without CBCT scatter and beam hardening Artifacts. The EE and EI 4DCBCT images were registered using the SICLE algorithm to produce a test DVF. Test DVFs were generated at a range of angular frequencies from 0.17 projections per degree to 1.83 projections per degree (full angular sampling on a commercial cone beam CT system). The norm of the voxel by voxel vector difference between the test DVF and reference DVF within the patient's delineated lung volume was calculated. Results: The mean absolute error was significantly associated with the projection angular frequency (p < 0.05, exponential regression, R2=0.996). The median error was below 0.2 cm and 90% of the error was below 0.3 cm for the lowest frequency evaluated here of 60 projections per phase. However, above a sampling frequency of 0.33 projections per degree (120 projections per phase), 90% of the error was less than 0.1 cm in relation to the 4DCT registration. Conclusion: Using at least 120 projections per phase, view‐Aliasing Artifact had a minimal effect on the registration accuracy for the SICLE algorithm due to inherent elastic and smoothing constraints in the algorithm.
Joonki Paik - One of the best experts on this subject based on the ideXlab platform.
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Region-based Super-Resolution Reconstruction Using Parallel Programming
2014Co-Authors: Stefanos P. Belekos, Joonki Paik, Jaehwan Jeon, Jinhee Lee, Aggelos K. KatsaggelosAbstract:This paper presents region-based super-resolution reconstruction approach using a parallel programming for minimizing interpolation Artifacts. In the proposed algorithm of regularized iterative SR, two different types of constraints are incorporated, namely (i) edge reconnection and (ii) orientation adaptive filtering. By combining these two constraints the proposed SR algorithm can successfully remove Aliasing Artifact and, as a result, produce edge-preserving high-resolution (HR) images. Experimental results show that the proposed algorithm is more effective than traditional methods
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Extended fisheye lens model for practical geometric correction and image enhancement
Optics Letters, 2014Co-Authors: Jinho Park, Joonki PaikAbstract:An extended fisheye lens model is presented to control the size ratio between the distorted and virtually undistorted images based on orthographic projection. The optimum size ratio is derived to correct the barrel distortion of a fisheye lens so that the maximum amount of the peripheral region is reconstructed with the minimum visual distortion. The geometric correction generates an Aliasing Artifact in the central region and a jagging Artifact in the peripheral region. Based on the proposed lens model, a novel image enhancement algorithm is also presented to remove the Aliasing and jagging Artifacts in the geometrically corrected image. Experimental results demonstrate that the proposed enhancement method outperforms existing methods in the sense of objective and subjective measures.
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Extended fisheye lens model for practical geometric correction and image enhancement
Optics Letters, 2014Co-Authors: Jinho Park, Joonki PaikAbstract:An extended fisheye lens model is presented to control the size ratio between the distorted and virtually undistorted images based on orthographic projection. The optimum size ratio is derived to correct the barrel distortion of a fisheye lens so that the maximum amount of the peripheral region is reconstructed with the minimum visual distortion. The geometric correction generates an Aliasing Artifact in the central region and a jagging Artifact in the peripheral region. Based on the proposed lens model, a novel image enhancement algorithm is also presented to remove the Aliasing and jagging Artifacts in the geometrically corrected image. Experimental results demonstrate that the proposed enhancement method outperforms existing methods in the sense of objective and subjective measures.
Jinho Park - One of the best experts on this subject based on the ideXlab platform.
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Extended fisheye lens model for practical geometric correction and image enhancement
Optics Letters, 2014Co-Authors: Jinho Park, Joonki PaikAbstract:An extended fisheye lens model is presented to control the size ratio between the distorted and virtually undistorted images based on orthographic projection. The optimum size ratio is derived to correct the barrel distortion of a fisheye lens so that the maximum amount of the peripheral region is reconstructed with the minimum visual distortion. The geometric correction generates an Aliasing Artifact in the central region and a jagging Artifact in the peripheral region. Based on the proposed lens model, a novel image enhancement algorithm is also presented to remove the Aliasing and jagging Artifacts in the geometrically corrected image. Experimental results demonstrate that the proposed enhancement method outperforms existing methods in the sense of objective and subjective measures.
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Extended fisheye lens model for practical geometric correction and image enhancement
Optics Letters, 2014Co-Authors: Jinho Park, Joonki PaikAbstract:An extended fisheye lens model is presented to control the size ratio between the distorted and virtually undistorted images based on orthographic projection. The optimum size ratio is derived to correct the barrel distortion of a fisheye lens so that the maximum amount of the peripheral region is reconstructed with the minimum visual distortion. The geometric correction generates an Aliasing Artifact in the central region and a jagging Artifact in the peripheral region. Based on the proposed lens model, a novel image enhancement algorithm is also presented to remove the Aliasing and jagging Artifacts in the geometrically corrected image. Experimental results demonstrate that the proposed enhancement method outperforms existing methods in the sense of objective and subjective measures.
Krishna S. Nayak - One of the best experts on this subject based on the ideXlab platform.
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Aliasing Artifact reduction in spiral real-time MRI.
Magnetic resonance in medicine, 2021Co-Authors: Ye Tian, Yongwan Lim, Ziwei Zhao, Dani Byrd, Shrikanth S. Narayanan, Krishna S. NayakAbstract:PURPOSE To mitigate a common Artifact in spiral real-time MRI, caused by Aliasing of signal outside the desired FOV. This Artifact frequently occurs in midsagittal speech real-time MRI. METHODS Simulations were performed to determine the likely origin of the Artifact. Two methods to mitigate the Artifact are proposed. The first approach, denoted as "large FOV" (LF), keeps an FOV that is large enough to include the Artifact signal source during reconstruction. The second approach, denoted as "estimation-subtraction" (ES), estimates the Artifact signal source before subtracting a synthetic signal representing that source in multicoil k-space raw data. Twenty-five midsagittal speech-production real-time MRI data sets were used to evaluate both of the proposed methods. Reconstructions without and with corrections were evaluated by two expert readers using a 5-level Likert scale assessing Artifact severity. Reconstruction time was also compared. RESULTS The origin of the Artifact was found to be a combination of gradient nonlinearity and imperfect anti-Aliasing in spiral sampling. The LF and ES methods were both able to substantially reduce the Artifact, with an averaged qualitative score improvement of 1.25 and 1.35 Likert levels for LF correction and ES correction, respectively. Average reconstruction time without correction, with LF correction, and with ES correction were 160.69 ± 1.56, 526.43 ± 5.17, and 171.47 ± 1.71 ms/frame. CONCLUSION Both proposed methods were able to reduce the spiral Aliasing Artifacts, with the ES-reduction method being more effective and more time efficient.
Jiang Hsieh - One of the best experts on this subject based on the ideXlab platform.
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Adaptive view synthesis for Aliasing Artifact reduction
Medical Imaging 2001: Physics of Medical Imaging, 2001Co-Authors: Jiang Hsieh, Christopher Carson Slack, Sandeep Dutta, Clarence L. Gordon, Edward ChaoAbstract:In recent years, the scan speed of computed tomography (CT) has increased significantly. Not long ago, the state-of-the- art CT was only capable of completing a single scan in 1.0 s per gantry rotation. Nowadays, 0.5 s per revolution is nearly an industry standard. Faster scan speeds demand faster sampling of the projections to combat Aliasing Artifacts, and higher x-ray tube output to ensure sufficient x-ray photon flux delivered to the scan. These demands often exceed the technological capability of these components. In this paper we performed a detailed analysis on the characteristics of the view Aliasing Artifact. Based on our analysis and clinical observations, we propose an adaptive view synthesis (AVS) scheme that effectively reduces the demands on the data acquisition system. Detailed performance comparison between the full view sampling and the adaptive view synthesis are performed through computer simulations and phantom experiments. Our analysis indicates that AVS is adequate for routine clinical applications.
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Aliasing Artifact suppression with adaptive segmentation based edge enhancement
International Conference on Image Processing, 1997Co-Authors: Jiang HsiehAbstract:One of the inherent limitations of the third generation CT scanner is the projection undersampling. Because of the fan-beam geometry, patient motion, and many other factors, the Nyquist sampling criteria are not always strictly observed. As a result, the fine structures of the anatomy and important pathologies are often marred by Aliasing streaks, which render the image unusable. We analyze the root cause of the Aliasing Artifact and present an adaptive algorithm that enhances the fine structures of the anatomy and suppresses Aliasing Artifacts and noise. The algorithm first reconstruct an image with a modified reconstruction kernel which preserves as much high frequency information as possible without introducing significant Aliasing Artifacts. The resulting image is then segmented into two classes. A fuzzy classification method is employed which uses not only the pixel intensity and texture information, but also the classifications of adjacent slices. Various phantom and clinical studies have demonstrated the robustness and effectiveness of our approach.
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Optimization of detector geometry for Aliasing Artifact reduction in the third-generation CT
Medical Imaging 1997: Physics of Medical Imaging, 1997Co-Authors: Jiang HsiehAbstract:Many clinical applications demand CT scanners to provide 15 to 20 line pairs per centimeter resolution. For a conventional third generation CT scanner, this demand present a special challenge because of the inherent sampling limitations. If special care is not taken, Aliasing Artifacts could result. These Artifacts typically appear as fine streaks irradiating from high density objects.One of the methods used to achieve Aliasing-free CT images is to increase the sampling density by x-ray focal spot wobbling. This is achieved by first acquiring a projection with focal spot at one position. The gantry is then rotated to a position so that the detector cells straddle the cell positions at the previous view.At the same time, the focal spot is deflected back so that it overlaps the previous focal spot position. The two set of projections form an interlaced projection with much higher sampling density. Since the detector cells form an arc concentric to the x-ray focal spot, the interlaced samples are not uniformly spaced. Detailed analysis indicates that the position displacement increases as a function of the detector angle, which results in less effective Aliasing Artifact reduction for objects located away from the iso-center. To overcome this shortcoming,w e propose a new detector geometry. Computer simulations have shown that further Aliasing Artifact reduction can be achieved for off-centered objects. In addition, an improvement in in-plane resolution can be realized, due to an increase in the magnification factor and a reduction in the effective detector cell cross-section. We further show that a closed form solution for the tomographic reconstruction is possible when certain constraints are met.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Adaptive Edge Enhancement Based on Image Segmentation
Medical Imaging 1997: Image Processing, 1997Co-Authors: Jiang HsiehAbstract:Many clinical applications, e.g., inner auditory cannel (IAC) studies, demand the CT scanner to provide high spatial in-plane resolution. Currently, these studies are performed by reconstructing the images with a high resolution reconstruction kernel. The cutoff frequency of the kernel is set to the limit of the Nyquist frequency, assuming perfect double sampling per detector cell can be achieved. Because of the fan-beam geometry, patient motion, and the inherent limitations of the third generation CT sampling, the Nyquist criteria are not always strictly observed. As a result, many clinical images are degraded by Aliasing Artifacts. In many cases, the fine structure of the anatomy and important pathologies are marred by Aliasing streaks, which render the image unusable. In this paper, we analyze the root cause of the Aliasing Artifact and present an adaptive edge enhancement algorithm that enhances the fine structures and suppress Aliasing Artifacts and noise in the IAC images. In the proposed scheme, a high resolution CT image is first reconstructed with a modified reconstruction kernel, H1(f), which has a frequency response and a cutoff frequency just below the point where significant Aliasing Artifact can be observed. The reconstructed image is then segmented into two classes (E: enhancement and S: suppression) based on CT numbers as well as texture. Adaptive edge enhancement is performed on the E class and adaptive noise suppression is performed on the S class. Various phantom and clinical studies were conducted. For each case, three images were generated: CT images reconstructed with the conventional high resolution kernel, images reconstructed with the modified H1 kernel, and images produced by the adaptive enhancement algorithm. The results were reviewed by the experts. The conclusion has been fairly consistent that the adaptive edge enhanced images are as sharp as the convectional high resolution CT images, with much reduced noise and Aliasing Artifacts. Since the segmentation relies on CT numbers as well as the texture in the image, the method is quite robust.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
