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Title: SU-G-IeP4-03: Cone Beam X-Ray Luminescence Computed Tomography Based On Generalized Gaussian Markov Random Field

Abstract

Purpose: Cone beam X-ray luminescence computed tomography (CB-XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. However, the inverse problem of CB-XLCT is seriously ill-conditioned, hindering us to achieve good image quality. In this work, a novel reconstruction method based on Bayesian theory is proposed to tackle this problem Methods: Bayesian theory provides a natural framework for utilizing various kinds of available prior information to improve the reconstruction image quality. A generalized Gaussian Markov random field (GGMRF) model is proposed here to construct the prior model of the Bayesian theory. The most important feature of GGMRF model is the adjustable shape parameter p, which can be continuously adjusted from 1 to 2. The reconstruction image tends to have more edge-preserving property when p is slide to 1, while having more noise tolerance property when p is slide to 2, just like the behavior of L1 and L2 regularization methods, respectively. The proposed method provides a flexible regularization framework to adapt to a wide range of applications. Results: Numerical simulations were implemented to test the performance of the proposed method. The Digimouse atlas were employed to construct a three-dimensional mousemore » model, and two small cylinders were placed inside to serve as the targets. Reconstruction results show that the proposed method tends to obtain better spatial resolution with a smaller shape parameter, while better signal-to-noise image with a larger shape parameter. Quantitative indexes, contrast-to-noise ratio (CNR) and full-width at half-maximum (FWHM), were used to assess the performance of the proposed method, and confirmed its effectiveness in CB-XLCT reconstruction. Conclusion: A novel reconstruction method for CB-XLCT is proposed based on GGMRF model, which enables an adjustable performance tradeoff between L1 and L2 regularization methods. Numerical simulations were conducted to demonstrate its performance.« less

Authors:
 [1];  [2]
  1. Beijing Jiaotong University, Beijing, Beijing (China)
  2. Stanford University School of Medicine, Stanford, CA (United States)
Publication Date:
OSTI Identifier:
22649438
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
61 RADIATION PROTECTION AND DOSIMETRY; 60 APPLIED LIFE SCIENCES; BEAMS; BIOMEDICAL RADIOGRAPHY; COMPUTERIZED SIMULATION; COMPUTERIZED TOMOGRAPHY; CONES; IMAGES; LUMINESCENCE; MARKOV PROCESS; PERFORMANCE; RANDOMNESS; SPATIAL RESOLUTION; THREE-DIMENSIONAL CALCULATIONS

Citation Formats

Zhang, G, and Xing, L. SU-G-IeP4-03: Cone Beam X-Ray Luminescence Computed Tomography Based On Generalized Gaussian Markov Random Field. United States: N. p., 2016. Web. doi:10.1118/1.4957098.
Zhang, G, & Xing, L. SU-G-IeP4-03: Cone Beam X-Ray Luminescence Computed Tomography Based On Generalized Gaussian Markov Random Field. United States. doi:10.1118/1.4957098.
Zhang, G, and Xing, L. 2016. "SU-G-IeP4-03: Cone Beam X-Ray Luminescence Computed Tomography Based On Generalized Gaussian Markov Random Field". United States. doi:10.1118/1.4957098.
@article{osti_22649438,
title = {SU-G-IeP4-03: Cone Beam X-Ray Luminescence Computed Tomography Based On Generalized Gaussian Markov Random Field},
author = {Zhang, G and Xing, L},
abstractNote = {Purpose: Cone beam X-ray luminescence computed tomography (CB-XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. However, the inverse problem of CB-XLCT is seriously ill-conditioned, hindering us to achieve good image quality. In this work, a novel reconstruction method based on Bayesian theory is proposed to tackle this problem Methods: Bayesian theory provides a natural framework for utilizing various kinds of available prior information to improve the reconstruction image quality. A generalized Gaussian Markov random field (GGMRF) model is proposed here to construct the prior model of the Bayesian theory. The most important feature of GGMRF model is the adjustable shape parameter p, which can be continuously adjusted from 1 to 2. The reconstruction image tends to have more edge-preserving property when p is slide to 1, while having more noise tolerance property when p is slide to 2, just like the behavior of L1 and L2 regularization methods, respectively. The proposed method provides a flexible regularization framework to adapt to a wide range of applications. Results: Numerical simulations were implemented to test the performance of the proposed method. The Digimouse atlas were employed to construct a three-dimensional mouse model, and two small cylinders were placed inside to serve as the targets. Reconstruction results show that the proposed method tends to obtain better spatial resolution with a smaller shape parameter, while better signal-to-noise image with a larger shape parameter. Quantitative indexes, contrast-to-noise ratio (CNR) and full-width at half-maximum (FWHM), were used to assess the performance of the proposed method, and confirmed its effectiveness in CB-XLCT reconstruction. Conclusion: A novel reconstruction method for CB-XLCT is proposed based on GGMRF model, which enables an adjustable performance tradeoff between L1 and L2 regularization methods. Numerical simulations were conducted to demonstrate its performance.},
doi = {10.1118/1.4957098},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • X-ray luminescence tomography (XLT) is an imaging technology based on X-ray-excitable materials. The main purpose of this paper is to obtain quantitative luminescence concentration using the structural information of the X-ray computed tomography (XCT) in the hybrid cone beam XLT/XCT system. A multi-wavelength luminescence cone beam XLT method with the structural a priori information is presented to relieve the severe ill-posedness problem in the cone beam XLT. The nanophosphors and phantom experiments were undertaken to access the linear relationship of the system response. Then, an in vivo mouse experiment was conducted. The in vivo experimental results show that the recoveredmore » concentration error as low as 6.67% with the location error of 0.85 mm can be achieved. The results demonstrate that the proposed method can accurately recover the nanophosphor inclusion and realize the quantitative imaging.« less
  • Purpose: The appearance of x-ray luminescence computed tomography (XLCT) opens new possibilities to perform molecular imaging by x ray. In the previous XLCT system, the sample was irradiated by a sequence of narrow x-ray beams and the x-ray luminescence was measured by a highly sensitive charge coupled device (CCD) camera. This resulted in a relatively long sampling time and relatively low utilization of the x-ray beam. In this paper, a novel cone beam x-ray luminescence computed tomography strategy is proposed, which can fully utilize the x-ray dose and shorten the scanning time. The imaging model and reconstruction method are described.more » The validity of the imaging strategy has been studied in this paper. Methods: In the cone beam XLCT system, the cone beam x ray was adopted to illuminate the sample and a highly sensitive CCD camera was utilized to acquire luminescent photons emitted from the sample. Photons scattering in biological tissues makes it an ill-posed problem to reconstruct the 3D distribution of the x-ray luminescent sample in the cone beam XLCT. In order to overcome this issue, the authors used the diffusion approximation model to describe the photon propagation in tissues, and employed the sparse regularization method for reconstruction. An incomplete variables truncated conjugate gradient method and permissible region strategy were used for reconstruction. Meanwhile, traditional x-ray CT imaging could also be performed in this system. The x-ray attenuation effect has been considered in their imaging model, which is helpful in improving the reconstruction accuracy. Results: First, simulation experiments with cylinder phantoms were carried out to illustrate the validity of the proposed compensated method. The experimental results showed that the location error of the compensated algorithm was smaller than that of the uncompensated method. The permissible region strategy was applied and reduced the reconstruction error to less than 2 mm. The robustness and stability were then evaluated from different view numbers, different regularization parameters, different measurement noise levels, and optical parameters mismatch. The reconstruction results showed that the settings had a small effect on the reconstruction. The nonhomogeneous phantom simulation was also carried out to simulate a more complex experimental situation and evaluated their proposed method. Second, the physical cylinder phantom experiments further showed similar results in their prototype XLCT system. With the discussion of the above experiments, it was shown that the proposed method is feasible to the general case and actual experiments. Conclusions: Utilizing numerical simulation and physical experiments, the authors demonstrated the validity of the new cone beam XLCT method. Furthermore, compared with the previous narrow beam XLCT, the cone beam XLCT could more fully utilize the x-ray dose and the scanning time would be shortened greatly. The study of both simulation experiments and physical phantom experiments indicated that the proposed method was feasible to the general case and actual experiments.« less
  • We present a method for automatic full precision alignmentof the images in a tomographic tilt series. Full-precision automaticalignment of cryo electron microscopy images has remained a difficultchallenge to date, due to the limited electron dose and low imagecontrast. These facts lead to poor signal to noise ratio (SNR) in theimages, which causes automatic feature trackers to generate errors, evenwith high contrast gold particles as fiducial features. To enable fullyautomatic alignment for full-precision reconstructions, we frame theproblem probabilistically as finding the most likely particle tracksgiven a set of noisy images, using contextual information to make thesolution more robust to the noisemore » in each image. To solve this maximumlikelihood problem, we use Markov Random Fields (MRF) to establish thecorrespondence of features in alignment and robust optimization forprojection model estimation. The resultingalgorithm, called RobustAlignment and Projection Estimation for Tomographic Reconstruction, orRAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as goodas the manual approach by an expert user. We are able to automaticallymap complete and partial marker trajectories and thus obtain highlyaccurate image alignment. Our method has been applied to challenging cryoelectron tomographic datasets with low SNR from intact bacterial cells,as well as several plastic section and x-ray datasets.« less
  • Purpose: To assess the accuracy of ultrasound-based repositioning (BAT) before prostate radiation with fiducial-based three-dimensional matching with cone-beam computed tomography (CBCT). Patients and Methods: Fifty-four positionings in 8 patients with {sup 125}I seeds/intraprostatic calcifications as fiducials were evaluated. Patients were initially positioned according to skin marks and after this according to bony structures based on CBCT. Prostate position correction was then performed with BAT. Residual error after repositioning based on skin marks, bony anatomy, and BAT was estimated by a second CBCT based on user-independent automatic fiducial registration. Results: Overall mean value (MV {+-} SD) residual error after BAT basedmore » on fiducial registration by CBCT was 0.7 {+-} 1.7 mm in x (group systematic error [M] = 0.5 mm; SD of systematic error [{sigma}] = 0.8 mm; SD of random error [{sigma}] = 1.4 mm), 0.9 {+-} 3.3 mm in y (M = 0.5 mm, {sigma} = 2.2 mm, {sigma} = 2.8 mm), and -1.7 {+-} 3.4 mm in z (M = -1.7 mm, {sigma} = 2.3 mm, {sigma} = 3.0 mm) directions, whereas residual error relative to positioning based on skin marks was 2.1 {+-} 4.6 mm in x (M = 2.6 mm, {sigma} = 3.3 mm, {sigma} = 3.9 mm), -4.8 {+-} 8.5 mm in y (M = -4.4 mm, {sigma} = 3.7 mm, {sigma} = 6.7 mm), and -5.2 {+-} 3.6 mm in z (M = -4.8 mm, {sigma} = 1.7 mm, {sigma} = 3.5mm) directions and relative to positioning based on bony anatomy was 0 {+-} 1.8 mm in x (M = 0.2 mm, {sigma} = 0.9 mm, {sigma} = 1.1 mm), -3.5 {+-} 6.8 mm in y (M = -3.0 mm, {sigma} = 1.8 mm, {sigma} = 3.7 mm), and -1.9 {+-} 5.2 mm in z (M = -2.0 mm, {sigma} = 1.3 mm, {sigma} = 4.0 mm) directions. Conclusions: BAT improved the daily repositioning accuracy over skin marks or even bony anatomy. The results obtained with BAT are within the precision of extracranial stereotactic procedures and represent values that can be achieved with several users with different education levels. If sonographic visibility is insufficient, CBCT or kV/MV portal imaging with implanted fiducials are recommended.« less
  • Image reconstruction from cone-beam projections collected along a single circular source trajectory is commonly done using the Feldkamp algorithm, which performs well only with a small cone angle. In this report, we propose an error-reduction-based algorithm to increase the cone angle by several folds to achieve satisfactory image quality at the same radiation dose. In our scheme, we first reconstruct the object using the Feldkamp algorithm. Then, we synthesize cone-beam projection data from the reconstructed volume in the same geometry, and reconstruct the volume again from the synthesized projections. Finally, these two reconstruction results are combined to reduce the reconstructionmore » error and produce a superior image volume. The merit of this algorithm is demonstrated in numerical simulation.« less