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Title: Alignment Solution for CT Image Reconstruction using Fixed Point and Virtual Rotation Axis

Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 2045-2322
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Scientific Reports; Journal Volume: 7
Country of Publication:
United States

Citation Formats

Jun, Kyungtaek, and Yoon, Seokhwan. Alignment Solution for CT Image Reconstruction using Fixed Point and Virtual Rotation Axis. United States: N. p., 2017. Web. doi:10.1038/srep41218.
Jun, Kyungtaek, & Yoon, Seokhwan. Alignment Solution for CT Image Reconstruction using Fixed Point and Virtual Rotation Axis. United States. doi:10.1038/srep41218.
Jun, Kyungtaek, and Yoon, Seokhwan. Wed . "Alignment Solution for CT Image Reconstruction using Fixed Point and Virtual Rotation Axis". United States. doi:10.1038/srep41218.
title = {Alignment Solution for CT Image Reconstruction using Fixed Point and Virtual Rotation Axis},
author = {Jun, Kyungtaek and Yoon, Seokhwan},
abstractNote = {},
doi = {10.1038/srep41218},
journal = {Scientific Reports},
number = ,
volume = 7,
place = {United States},
year = {Wed Jan 25 00:00:00 EST 2017},
month = {Wed Jan 25 00:00:00 EST 2017}
  • Purpose: Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise (and thereby patient dose), to reduce artifacts, or to improve spatial resolution. Among vendors and researchers, however, there is no consensus of how to best achieve these aims. The general approach is to incorporatea priori knowledge into iterative image reconstruction, for example, by adding additional constraints to the cost function, which penalize variations between neighboring voxels. However, this approach to regularization in general poses a resolution noise trade-off because the stronger the regularization, and thus the noise reduction, the stronger themore » loss of spatial resolution and thus loss of anatomical detail. The authors propose a method which tries to improve this trade-off. The proposed reconstruction algorithm is called alpha image reconstruction (AIR). One starts with generating basis images, which emphasize certain desired image properties, like high resolution or low noise. The AIR algorithm reconstructs voxel-specific weighting coefficients that are applied to combine the basis images. By combining the desired properties of each basis image, one can generate an image with lower noise and maintained high contrast resolution thus improving the resolution noise trade-off. Methods: All simulations and reconstructions are performed in native fan-beam geometry. A water phantom with resolution bar patterns and low contrast disks is simulated. A filtered backprojection (FBP) reconstruction with a Ram-Lak kernel is used as a reference reconstruction. The results of AIR are compared against the FBP results and against a penalized weighted least squares reconstruction which uses total variation as regularization. The simulations are based on the geometry of the Siemens Somatom Definition Flash scanner. To quantitatively assess image quality, the authors analyze line profiles through resolution patterns to define a contrast factor for contrast-resolution plots. Furthermore, the authors calculate the contrast-to-noise ratio with the low contrast disks and the authors compare the agreement of the reconstructions with the ground truth by calculating the normalized cross-correlation and the root-mean-square deviation. To evaluate the clinical performance of the proposed method, the authors reconstruct patient data acquired with a Somatom Definition Flash dual source CT scanner (Siemens Healthcare, Forchheim, Germany). Results: The results of the simulation study show that among the compared algorithms AIR achieves the highest resolution and the highest agreement with the ground truth. Compared to the reference FBP reconstruction AIR is able to reduce the relative pixel noise by up to 50% and at the same time achieve a higher resolution by maintaining the edge information from the basis images. These results can be confirmed with the patient data. Conclusions: To evaluate the AIR algorithm simulated and measured patient data of a state-of-the-art clinical CT system were processed. It is shown, that generating CT images through the reconstruction of weighting coefficients has the potential to improve the resolution noise trade-off and thus to improve the dose usage in clinical CT.« less
  • Purpose: To describe a novel methodology of converting megavoltage x-ray projections into virtual proton projections that are otherwise missing due to the proton range limit. These converted virtual proton projections can be used in the reconstruction of proton computed tomography (pCT). Methods: Relations exist between proton projections and multispectral megavoltage x-ray projections for human tissue. Based on these relations, these tissues can be categorized into: (a) adipose tissue; (b) nonadipose soft tissues; and (c) bone. These three tissue categories can be visibly identified on a regular megavoltage x-ray computed tomography (MVCT) image. With an MVCT image and its projection datamore » available, the x-ray projections through heterogeneous anatomy can be converted to the corresponding proton projections using predetermined calibration curves for individual materials, aided by a coarse segmentation on the x-ray CT image. To show the feasibility of this approach, mathematical simulations were carried out. The converted proton projections, plotted on a proton sinogram, were compared to the simulated ground truth. Proton stopping power images were reconstructed using either the virtual proton projections only or a blend of physically available proton projections and virtual proton projections that make up for those missing due to the range limit. These images were compared to a reference image reconstructed from theoretically calculated proton projections. Results: The converted virtual projections had an uncertainty of {+-}0.8% compared to the calculated ground truth. Proton stopping power images reconstructed using a blend of converted virtual projections (48%) and physically available projections (52%) had an uncertainty of {+-}0.86% compared with that reconstructed from theoretically calculated projections. Reconstruction solely from converted virtual proton projections had an uncertainty of {+-}1.1% compared with that reconstructed from theoretical projections. If these images are used for treatment planning, the average proton range uncertainty is estimated to be less than 1.5% for an imaging dose in the milligray range. Conclusions: The proposed method can be used to convert x-ray projections into virtual proton projections. The converted proton projections can be blended with existing proton projections or can be used solely for pCT reconstruction, addressing the range limit problem of pCT using current therapeutic proton machines.« less
  • Purpose: Parallel-scanning tomosynthesis (PS-TS) is a novel technique that fuses the slot scanning technique and the conventional tomosynthesis (TS) technique. This approach allows one to obtain long-view tomosynthesis images in addition to normally sized tomosynthesis images, even when using a system that has no linear tomographic scanning function. The reconstruction technique and an evaluation of the resulting image quality for PS-TS are described in this paper. Methods: The PS-TS image-reconstruction technique consists of several steps (1) the projection images are divided into strips, (2) the strips are stitched together to construct images corresponding to the reconstruction plane, (3) the stitchedmore » images are filtered, and (4) the filtered stitched images are back-projected. In the case of PS-TS using the fixed-focus reconstruction method (PS-TS-F), one set of stitched images is used for the reconstruction planes at all heights, thus avoiding the necessity of repeating steps (1)–(3). A physical evaluation of the image quality of PS-TS-F compared with that of the conventional linear TS was performed using a R/F table (Sonialvision safire, Shimadzu Corp., Kyoto, Japan). The tomographic plane with the best theoretical spatial resolution (the in-focus plane, IFP) was set at a height of 100 mm from the table top by adjusting the reconstruction program. First, the spatial frequency response was evaluated at heights of −100, −50, 0, 50, 100, and 150 mm from the IFP using the edge of a 0.3-mm-thick copper plate. Second, the spatial resolution at each height was visually evaluated using an x-ray test pattern (Model No. 38, PTW Freiburg, Germany). Third, the slice sensitivity at each height was evaluated via the wire method using a 0.1-mm-diameter tungsten wire. Phantom studies using a knee phantom and a whole-body phantom were also performed. Results: The spatial frequency response of PS-TS-F yielded the best results at the IFP and degraded slightly as the distance from the IFP increased. A visual evaluation of the spatial resolution using the x-ray test pattern indicated that the resolution was 1.8 lp/mm at the IFP and 1.2 lp/mm at heights of −100 and 100 mm from the IFP. The authors demonstrated that a spatial resolution of 1.2–1.8 lp/mm could be obtained within heights of 200 mm of the IFP. The slice sensitivity varied between 11.1 and 13.8 mm for heights between −50 and 100 mm, and there was no critical change in the slice sensitivity within a height range of 150 mm around the IFP. The phantom results demonstrated that tomosynthesis and long-view images could be reconstructed. Conclusions: PS-TS-F provides tomosynthesis images while using low-cost systems that have no tomographic scanning function, such as tableside-controlled universal R/F systems or universal radiographic systems.« less
  • We studied a recently proposed aggregated CT reconstruction technique which combines the complementary advantages of kilovoltage (kV) and megavoltage (MV) x-ray imaging. Various phantoms were imaged to study the effects of beam orientations and geometry of the imaging object on image quality of reconstructed CT. It was shown that the quality of aggregated CT was correlated with both kV and MV beam orientations and the degree of this correlation depended upon the geometry of the imaging object. The results indicated that the optimal orientations were those when kV beams pass through the thinner portion and MV beams pass through themore » thicker portion of the imaging object. A special preprocessing procedure was also developed to perform contrast conversions between kV and MV information prior to image reconstruction. The performance of two reconstruction methods, one filtered backprojection method and one iterative method, were compared. The effects of projection number, beam truncation, and contrast conversion on the CT image quality were investigated.« less
  • Purpose: Dual-energy CT (DECT) is being increasingly used for its capability of material decomposition and energy-selective imaging. A generic problem of DECT, however, is that the decomposition process is unstable in the sense that the relative magnitude of decomposed signals is reduced due to signal cancellation while the image noise is accumulating from the two CT images of independent scans. Direct image decomposition, therefore, leads to severe degradation of signal-to-noise ratio on the resultant images. Existing noise suppression techniques are typically implemented in DECT with the procedures of reconstruction and decomposition performed independently, which do not explore the statistical propertiesmore » of decomposed images during the reconstruction for noise reduction. In this work, the authors propose an iterative approach that combines the reconstruction and the signal decomposition procedures to minimize the DECT image noise without noticeable loss of resolution. Methods: The proposed algorithm is formulated as an optimization problem, which balances the data fidelity and total variation of decomposed images in one framework, and the decomposition step is carried out iteratively together with reconstruction. The noise in the CT images from the proposed algorithm becomes well correlated even though the noise of the raw projections is independent on the two CT scans. Due to this feature, the proposed algorithm avoids noise accumulation during the decomposition process. The authors evaluate the method performance on noise suppression and spatial resolution using phantom studies and compare the algorithm with conventional denoising approaches as well as combined iterative reconstruction methods with different forms of regularization. Results: On the Catphan©600 phantom, the proposed method outperforms the existing denoising methods on preserving spatial resolution at the same level of noise suppression, i.e., a reduction of noise standard deviation by one order of magnitude. This improvement is mainly attributed to the high noise correlation in the CT images reconstructed by the proposed algorithm. Iterative reconstruction using different regularization, including quadratic orq-generalized Gaussian Markov random field regularization, achieves similar noise suppression from high noise correlation. However, the proposed TV regularization obtains a better edge preserving performance. Studies of electron density measurement also show that our method reduces the average estimation error from 9.5% to 7.1%. On the anthropomorphic head phantom, the proposed method suppresses the noise standard deviation of the decomposed images by a factor of ∼14 without blurring the fine structures in the sinus area. Conclusions: The authors propose a practical method for DECT imaging reconstruction, which combines the image reconstruction and material decomposition into one optimization framework. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to decomposition accuracy, noise reduction, and spatial resolution.« less