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When too few photons reach detector elements, strong streaks appear through paths of high X-ray attenuation and an image becomes completely useless. This photon starvation artifact phenomenon occurs frequently when a pelvis or sho...
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When too few photons reach detector elements, strong streaks appear through paths of high X-ray attenuation and an image becomes completely useless. This photon starvation artifact phenomenon occurs frequently when a pelvis or shoulder is scanned with thin slices. The common understanding regarding photon starvation streaks is that they are a manifestation of irregularities caused by noise in the raw data profile. Therefore, the common countermeasure is local raw-data filtering, which detects and smoothes out the highly noisy part of the raw data. However, the photon starvation artifact can be solved only partly with such a method and a more effective solution is necessary. Here, we examined the mean level shift of raw data attributable to the nonlinear nature of logarithmic conversion, which is the process required for generating raw data from detected X-ray data. We judge that the real culprit of the photon starvation artifact is this mean level shift. When the noise level is very high or the photon level is very low, this mean level shift can become prominent and can become manifest as thick streaks against which the conventional local raw data filtering has no power. To solve this problem, we propose a new scheme of local raw data filtering, which consists of reverting log-converted raw data to a form that is equivalent to pre-log detector data. With this method, not only fine streaks, but also thick streaks are removed effectively. A better image quality with lower X-ray doses is possible with this method.
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Magnetic resonance imaging (MRI) and continuous electroencephalogram (EEG) monitoring are essential in the clinical management of neonatal seizures. EEG electrodes, however, can significantly degrade the image quality of both MRI ...
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Magnetic resonance imaging (MRI) and continuous electroencephalogram (EEG) monitoring are essential in the clinical management of neonatal seizures. EEG electrodes, however, can significantly degrade the image quality of both MRI and CT due to substantial metallic artifacts and distortions. Thus, we developed a novel thin film trace EEG net ("NeoNet") for improved MRI and CT image quality without compromising the EEG signal quality. The aluminum thin film traces were fabricated with an ultra-high-aspect ratio (up to 17,000:1, with dimensions 30 nm × 50.8 cm × 100 μm), resulting in a low density for reducing CT artifacts and a low conductivity for reducing MRI artifacts. We also used numerical simulation to investigate the effects of EEG nets on the B1 transmit field distortion in 3 T MRI. Specifically, the simulations predicted a 65% and 138% B1 transmit field distortion higher for the commercially available copper-based EEG net ("CuNet", with and without current limiting resistors, respectively) than with NeoNet. Additionally, two board-certified neuroradiologists, blinded to the presence or absence of NeoNet, compared the image quality of MRI images obtained in an adult and two children with and without the NeoNet device and found no significant difference in the degree of artifact or image distortion. Additionally, the use of NeoNet did not cause either: (i) CT scan artifacts or (ii) impact the quality of EEG recording. Finally, MRI safety testing confirmed a maximum temperature rise associated with the NeoNet device in a child head-phantom to be 0.84 °C after 30 min of high-power scanning, which is within the acceptance criteria for the temperature for 1 h of normal operating mode scanning as per the FDA guidelines. Therefore, the proposed NeoNet device has the potential to allow for concurrent EEG acquisition and MRI or CT scanning without significant image artifacts, facilitating clinical care and EEG/fMRI pediatric research.
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In computed tomography, high attenuation occurs when x-rays pass through a dense region or a long path in the scanning object. In this case, only limited photons reach the detector, which causes photon starvation artifacts. The ar...
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In computed tomography, high attenuation occurs when x-rays pass through a dense region or a long path in the scanning object. In this case, only limited photons reach the detector, which causes photon starvation artifacts. The artifacts usually appear as streaks along the directions with high attenuation. It might lower the discrimination of minor structures and lead to misdiagnosis. Applying a local filter to the projection data adaptively is a common solution, however, if the parameters of projection-based filter are not well selected, new artifacts and noise might appear in the final image. In this paper, a post image processing technique was developed to suppress the photon starvation streak artifacts. Based on the directional characteristics of streaks, a semi-adaptive anisotropic diffusion filter was applied to the high frequency sub-bands after wavelet transformation (WASA). Qualitative and quantitative experiments were performed on phantom data and clinical data to prove the effectiveness of this method for photon starvation artifact suppression.
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In metal artifact reduction (MAR) in computed tomography (CT) based on projection data inpainting, X-ray photon noise
has not been considered in the inpainting process. This study aims to assess the effectiveness of a MAR techniq...
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In metal artifact reduction (MAR) in computed tomography (CT) based on projection data inpainting, X-ray photon noise
has not been considered in the inpainting process. This study aims to assess the effectiveness of a MAR technique incorporating
noise recovery in such projection data regions, compared with existing MAR techniques based on projection
data normalization (NMAR), including one with frequency splitting (FSNMAR). Phantoms simulating hip prostheses and
dental fillings were scanned using a 64-row multi slice CT scanner. The projection data was processed by NMAR and
NMAR with noise recovery (NRNMAR); the processed data was sent back to the CT system for reconstruction. For the
phantoms and clinical cases with hip prostheses and dental fillings, images were reconstructed without MAR, and with
NMAR, NRNMAR, and FSNMAR (incorporated in the CT system). To validate the efficacy of noise recovery, noise
power spectra (NPSs) were measured from the images of the hip prosthesis phantom with and without metals. The artifact
index (AI) was compared between NRNMAR and FSNMAR. The resultant NPSs of NRNMAR were very similar to those
of phantom images with no metals, endorsing the efficacy of noise recovery. The NMAR images had unnatural noise
textures and FSNMAR caused additional streaks. NRNMAR exhibited some significant improvements in these respects:
It reduced the AI by as much as 66.2−88.6% compared to FSNMAR, except for the case of a unilateral prosthesis. In
conclusion, NRNMAR, which simply adds white noise to the projection data, would be effective in improving the quality
of CT images with metal artifacts reduction.
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Purpose Polychromatic x‐rays are used in most computed tomography scanners. In this case, a beam‐hardening effect occurs, which degrades the image quality and distorts the shapes of objects in the reconstructed images. When the ...
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Purpose Polychromatic x‐rays are used in most computed tomography scanners. In this case, a beam‐hardening effect occurs, which degrades the image quality and distorts the shapes of objects in the reconstructed images. When the beam‐hardening artifact is not severe, conventional correction methods can reduce the artifact reasonably well. However, highly dense materials, such as iron and titanium, can produce more severe beam‐hardening artifacts, which often cannot be corrected by conventional methods. Moreover, when the size of the metal is large, severe darks bands due to photon starvation as well as beam‐hardening are generated. The purpose of our study was to develop a new method for correcting severe beam‐hardening artifacts and severe dark bands using a high‐order polynomial correction function and a prior‐image‐based linearization method. Methods The initial estimate of an image free of beam‐hardening (a prior image) was constructed from the initial reconstruction of the original projection data. Its corresponding beam‐hardening‐free projection data (a prior projection) were calculated by a projection operator onto the prior image. A new beam‐hardening correction function G ( p raw ) with many high‐order terms was effectively determined via a simple minimization process applied to the difference between the original projection data and the prior projection data. Using the determined correction function G ( p raw ), a corrected linearized sinogram p corr can be obtained, which became effectively linear for the line integrals of the object. Final beam‐hardening corrected images can be reconstructed from the linearized sinogram. The proposed method was evaluated in both simulation and real experimental studies. Results All investigated cases in both simulations and real experiments showed that the proposed method effectively removed not only streaks for moderate beam‐hardening artifacts but also dark bands for severe beam‐hardening artifacts without causing structural and contrast distortion. Conclusions The prior‐image‐based linearization method exhibited better correction performance than conventional methods. Because the proposed method did not require time‐consuming iterative reconstruction processes to obtain the optimal correction function, it can expedite the correction procedure and incorporate more high‐order terms in the linearization correction function in comparison to the conventional methods.
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Background The differential phase computed tomography (DP-CT) imaging is a kind of X-ray-phase-based CT methods, which is widely used in various X-ray imaging laboratories. At present, a collection of projections with the horizont...
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Background The differential phase computed tomography (DP-CT) imaging is a kind of X-ray-phase-based CT methods, which is widely used in various X-ray imaging laboratories. At present, a collection of projections with the horizontal direction (perpendicular to the sample rotation axis) DP information is often acquired in DP-CT. In an ideal experiment, if the background is zero, then we can accurately reconstruct the information of each direction of the sample with horizontal data only. Actually, background information will produce streaking artifacts to affect the resolution in directions other than the horizontal direction. Purpose To mitigate the streaking artifacts in the conventionally reconstructed DP-CT images. Method This study develops a novel analytical DP-CT reconstruction framework by jointly using the bilateral DP information obtained along two perpendicular directions. In addition, a new data acquisition scheme is also proposed to quickly acquire the bilateral DP signal without the need of changing the direction of refraction signal acquisition. Results Experimental results demonstrate that this new algorithm is able to greatly reduce the streaking artifacts on DP-CT images.
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Abstract Background The beam‐hardening effect due to the polychromatic nature of the X‐ray spectra results in two main artifacts in CT images: cupping in homogeneous areas and dark bands between dense parts in heterogeneous samp...
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Abstract Background The beam‐hardening effect due to the polychromatic nature of the X‐ray spectra results in two main artifacts in CT images: cupping in homogeneous areas and dark bands between dense parts in heterogeneous samples. Post‐processing methods have been proposed in the literature to compensate for these artifacts, but these methods may introduce additional noise in low‐dose acquisitions. Iterative methods are an alternative to compensate noise and beam‐hardening artifacts simultaneously. However, they usually rely on the knowledge of the spectrum or the selection of empirical parameters. Purpose We propose an iterative reconstruction method with beam hardening compensation for small animal scanners that is robust against low‐dose acquisitions and that does not require knowledge of the spectrum, overcoming the limitations of current beam‐hardening correction algorithms. Methods The proposed method includes an empirical characterization of the beam‐hardening function based on a simple phantom in a polychromatic statistical reconstruction method. Evaluation was carried out on simulated data with different noise levels and step angles and on limited‐view rodent data acquired with the ARGUS/CT system. Results Results in small animal studies showed a proper correction of the beam‐hardening artifacts in the whole sample, independently of the quantity of bone present on each slice. The proposed approach also reduced noise in the low‐dose acquisitions and reduced streaks in the limited‐view acquisitions. Conclusions Using an empirical model for the beam‐hardening effect, obtained through calibration, in an iterative reconstruction method enables a robust correction of beam‐hardening artifacts in low‐dose small animal studies independently of the bone distribution.
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In this article, we study the limited angle problem for the weighted X-ray transform. We consider two approximate reconstructions by applying filtered back-projection formulas to the limited data. We prove that each resulted opera...
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In this article, we study the limited angle problem for the weighted X-ray transform. We consider two approximate reconstructions by applying filtered back-projection formulas to the limited data. We prove that each resulted operator can be decomposed into the sum of three Fourier integral operators whose symbols are of types The first operator, being a pseudo-differential operator, is responsible for the reconstruction of visible singularities. The other two are responsible for the generation of the artifacts. The theory of Fourier integral operators then implies, in particular, the continuity of the reconstruction operator and geometry of the artifacts. We then extend the technique developed by the author in [Inverse Problems 31 (2015) 055003] to obtain more refined microlocal estimates for the strength of the artifacts.
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Abstract Background Cone‐beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application in adaptive radiotherapy (ART) is lim...
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Abstract Background Cone‐beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application in adaptive radiotherapy (ART) is limited by many imaging artifacts and inaccurate Hounsfield units (HUs). The correction of CBCT image is necessary and of great value for CBCT‐based ART. Purpose To explore the synthetic CT (sCT) generation from CBCT images of thorax and abdomen patients, which usually surfer from serious artifacts duo to organ state changes. In this study, a streaking artifact reduction network (SARN) is proposed to reduce artifacts and combine with cycleGAN to generate high‐quality sCT images from CBCT and achieve an accurate dose calculation. Methods The proposed SARN was trained in a self‐supervised manner. Artifact‐CT images were generated from planning CT by random deformation and projection replacement, and SARN was trained based on paired artifact—CT and CT images. The planning CT and CBCT images of 260 patients with cancer, including 120 thoracic and 140 abdominal CT scans, were used to train and evaluate neural networks. The CBCT images of another 12 patients in late treatment fractions, which contained large anatomy changes, were also tested by trained models. The trained models include commonly used U‐Net, cycleGAN, attention‐gated cycleGAN (cycAT), and cascade models combined SARN with cycleGAN or cycAT. The generated sCT images were compared in terms of image quality and dose calculation accuracy. Results The sCT images generated by SARN combined with cycleGAN and cycAT showed the best image quality, removed the most artifacts, and retained the normal anatomical structure. The SARN+cycleGAN performed best in streaking artifacts removal with the maximum percent integrity uniformity (PIUm) of 91.0% and minimum standard deviation (SD) of 35.4 HU for delineated artifact regions among all models. The mean absolute error (MAE) of CBCT images in the thorax and abdomen were 71.6 and 55.2 HU, respectively, using planning CT images after deformable registration as ground truth. Compared with CBCT, the thoracic and abdominal sCT images generated by each model had significantly improved image quality with smaller MAE (p < 0.05). The SARN+cycAT obtained the minimum MAEs of 42.5 HU in the thorax while SARN+cycleGAN got the minimum MAEs of 32.0 HU in the abdomen. The sCT generated by U‐Net had a remarkably lower anatomical structure accuracy compared with the other models. The thoracic and abdominal sCT images generated by SARN+cycleGAN showed optimal dose calculation accuracy with gamma passing rates (2 mm/2%) of 98.2% and 96.9%, respectively. Conclusions The proposed SARN can reduce serious streaking artifacts in CBCT images. The SARN combined with cycleGAN can generate high‐quality sCT images with fewer artifacts, high‐accuracy HU values, and accurate anatomical structures, thus providing reliable dose calculation in ART.
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This study aims to address and test a new residual learning algorithm in neural network applied to the projection data to generate high qualified imaging by reducing the streaking artifacts in cone-beam computed tomography (CBCT)....
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This study aims to address and test a new residual learning algorithm in neural network applied to the projection data to generate high qualified imaging by reducing the streaking artifacts in cone-beam computed tomography (CBCT). Since the streaking artifacts have a large relationship with the noise on the projection, a residual objective upon Poisson noise corresponding to the image was proposed. As the prior, the convolution neural network (CNN) was constructed to residual learning based on the simulated label and exploited to eliminate the artifacts in the slice. To illustrate the robustness and applicability of CNN, the proposed method is evaluated using CBCT images. For the simulated projection, the PSNR and SSIM of the proposed method were dramatically increased by 15.4% and 85.9% of that with raw projection; for the true projection, the PSNR and SSIM were increased by 14.9% and 56.2%, respectively. Study results show effective results, and the proposed method is practical and attractive as a preferred solution to CT streaking artifacts suppression. (C) 2019 Elsevier B.V. All rights reserved.
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