American Institute of Mathematical Sciences

Advanced
  • Previous Article
    Existence and stability of electromagnetic Stekloff eigenvalues with a trace class modification
  • IPI Home
  • This Issue
  • Next Article
    Unique continuation property and Poincaré inequality for higher order fractional Laplacians with applications in inverse problems
August  2021, 15(4): 683-721. doi: 10.3934/ipi.2021010

Bragg scattering tomography

James W. Webber , and  Eric L. Miller

161 College Avenue, Halligan Hall, Medford, MA 02155, USA

* Corresponding author: James W. Webber

Received  April 2020 Revised  October 2020 Published  January 2021

Fund Project: This material is supported by the U.S. Department of Homeland Security, Science and Technology Directorate, Office of University Programs, under Grant Award 2013-ST-061-ED0001

Here we introduce a new forward model and imaging modality for Bragg Scattering Tomography (BST). The model we propose is based on an X-ray portal scanner with linear detector collimation, currently being developed for use in airport baggage screening. The geometry under consideration leads us to a novel two-dimensional inverse problem, where we aim to reconstruct the Bragg scattering differential cross section function from its integrals over a set of symmetric $ C^2 $ curves in the plane. The integral transform which describes the forward problem in BST is a new type of Radon transform, which we introduce and denote as the Bragg transform. We provide new injectivity results for the Bragg transform here, and describe how the conditions of our theorems can be applied to assist in the machine design of the portal scanner. Further we provide an extension of our results to $ n $-dimensions, where a generalization of the Bragg transform is introduced. Here we aim to reconstruct a real valued function on $ \mathbb{R}^{n+1} $ from its integrals over $ n $-dimensional surfaces of revolution of $ C^2 $ curves embedded in $ \mathbb{R}^{n+1} $. Injectivity proofs are provided also for the generalized Bragg transform.

Keywords: Bragg scattering, tomography, Volterra integral equations, generalized Radon transforms.
Mathematics Subject Classification: Primary: 45Axx, 44A12.
Citation: James W. Webber, Eric L. Miller. Bragg scattering tomography. Inverse Problems & Imaging, 2021, 15 (4) : 683-721. doi: 10.3934/ipi.2021010
References:
[1]

G. Ambartsoumian, R. Gouia-Zarrad and M. A. Lewis, Inversion of the circular Radon transform on an annulus, Inverse Problems, 26 (2010), 105015, 11pp. doi: 10.1088/0266-5611/26/10/105015.  Google Scholar

[2]

H. Andrade-Loarca, G. Kutyniok and O. Öktem, Shearlets as feature extractor for semantic edge detection: The model-based and data-driven realm, Proc. A., 476 (2020), 841–866. arXiv: 1911.12159. doi: 10.1098/rspa.2019.0841.  Google Scholar

[3]

D. Ballantine Jr, R. M. White, S. J. Martin, A. J. Ricco, E. Zellers, G. Frye and H. Wohltjen, Acoustic Wave Sensors: Theory, Design and Physico-chemical Applications, Elsevier, 1996. Google Scholar

[4]

G. Beylkin, The inversion problem and applications of the generalized Radon transform, Communications on Pure and Applied Mathematics, 37 (1984), 579-599.  doi: 10.1002/cpa.3160370503.  Google Scholar

[5]

R. Bryan, International Tables for Crystallography, Vol. C. Mathematical, physical and chemical tables edited by A. J. C. Wilson, 1993. Google Scholar

[6]

A. M. Cormack, The Radon transform on a family of curves in the plane, Proc. Amer. Math. Soc., 83 (1981), 325-330.  doi: 10.1090/S0002-9939-1981-0624923-1.  Google Scholar

[7]

A. M. Cormack, Radon's problem for some surfaces in $\mathbb{R}^n$, Proc. Amer. Math. Soc., 99 (1987), 305-312.  doi: 10.2307/2046630.  Google Scholar

[8]

J. J. DeMarco and P. Suortti, Effect of scattering on the attenuation of X-rays, Physical Review B, 4 (1971), 1028. Google Scholar

[9]

A. Denisyuk, Inversion of the generalized Radon transform, Translations of the American Mathematical Society-Series 2, 162 (1994), 19-32.  doi: 10.1090/trans2/162/02.  Google Scholar

[10]

M. J. Ehrhardt, K. Thielemans, L. Pizarro, D. Atkinson, S. Ourselin, B. F. Hutton and S. R. Arridge, Joint reconstruction of PET-MRI by exploiting structural similarity, Inverse Problems, 31 (2014), 015001, 23pp. doi: 10.1088/0266-5611/31/1/015001.  Google Scholar

[11]

J. A. Greenberg, K. Krishnamurthy and D. Brady, Snapshot molecular imaging using coded energy-sensitive detection, Optics express, 21 (2013), 25480-25491.  doi: 10.1364/OE.21.025480.  Google Scholar

[12]

A. Greenleaf and G. Uhlmann, Non-local inversion formulas for the X-ray transform, Duke Math. J., 58 (1989), 205-240.  doi: 10.1215/S0012-7094-89-05811-0.  Google Scholar

[13]

M. Hassan, J. A. Greenberg, I. Odinaka and D. J. Brady, Snapshot fan beam coded aperture coherent scatter tomography, Optics Express, 24 (2016), 18277-18289.   Google Scholar

[14]

J. Hubbell, W. J. Veigele, E. Briggs, R. Brown, D. Cromer and d. R. Howerton, Atomic form factors, incoherent scattering functions, and photon scattering cross sections, Journal of Physical and Chemical Reference Data, 4 (1975), 471-538.  doi: 10.1063/1.555523.  Google Scholar

[15]

J. H. Hubbell and I. Overbo, Relativistic atomic form factors and photon coherent scattering cross sections, Journal of Physical and Chemical Reference Data, 8 (1979), 69-106.  doi: 10.1063/1.555593.  Google Scholar

[16]

C.-Y. Jung and S. Moon, Inversion formulas for cone transforms arising in application of Compton cameras, Inverse Problems, 31 (2015), 015006, 20pp. doi: 10.1088/0266-5611/31/1/015006.  Google Scholar

[17]

H. P. Klug and L. E. Alexander, X-ray Diffraction Procedures: For Polycrystalline and Amorphous Materials, X-Ray Diffraction Procedures: For Polycrystalline and Amorphous Materials, 2nd Edition, by Harold P. Klug, Leroy E. Alexander, pp. 992. Wiley-VCH, May 1974., 992. Google Scholar

[18]

K. MacCabe, K. Krishnamurthy, A. Chawla, D. Marks, E. Samei and D. Brady, Pencil beam coded aperture X-ray scatter imaging, Optics Express, 20 (2012), 16310-16320.  doi: 10.1364/OE.20.016310.  Google Scholar

[19]

F. Natterer, The Mathematics of Computerized Tomography, SIAM, 2001. doi: 10.1137/1.9780898719284.  Google Scholar

[20]

M. Nguyen and T. Truong, Inversion of a new circular-arc Radon transform for Compton scattering tomography, Inverse Problems, 26 (2010), 099802, 1 p. doi: 10.1088/0266-5611/26/9/099802.  Google Scholar

[21]

S. J. Norton, Compton scattering tomography, Journal of Applied Physics, 76 (1994), 2007-2015.  doi: 10.1063/1.357668.  Google Scholar

[22]

V. P. Palamodov, A uniform reconstruction formula in integral geometry, Inverse Problems, 28 (2012), 065014, 15pp. doi: 10.1088/0266-5611/28/6/065014.  Google Scholar

[23]

V. Palamodov, An analytic reconstruction for the Compton scattering tomography in a plane, Inverse Problems, 27 (2011), 125004, 8pp. doi: 10.1088/0266-5611/27/12/125004.  Google Scholar

[24]

M. Plancherel and M. Leffler, Contribution à l'étude de la représentation d'une fonction arbitraire par des intégrales définies, Rendiconti del Circolo Matematico di Palermo (1884-1940), 30 (1910), 289-335.   Google Scholar

[25]

E. T. Quinto, The dependence of the generalized Radon transform on defining measures, Transactions of the American Mathematical Society, 257 (1980), 331-346.  doi: 10.1090/S0002-9947-1980-0552261-8.  Google Scholar

[26]

G. Rigaud, 3D Compton scattering imaging: Study of the spectrum and contour reconstruction, arXiv preprint, arXiv: 1908.03066. Google Scholar

[27]

G. Rigaud and B. N. Hahn, 3D Compton scattering imaging and contour reconstruction for a class of Radon transforms, Inverse Problems, 34 (2018), 075004, 22pp. doi: 10.1088/1361-6420/aabf0b.  Google Scholar

[28]

G. Rigaud, M. K. Nguyen and A. K. Louis, Novel numerical inversions of two circular-arc Radon transforms in Compton scattering tomography, Inverse Problems in Science and Engineering, 20 (2012), 809-839.  doi: 10.1080/17415977.2011.653008.  Google Scholar

[29]

A. Smakula and J. Kalnajs, Precision determination of lattice constants with a Geiger-counter X-ray diffractometer, Physical Review, 99 (1955), 1737. doi: 10.1103/PhysRev.99.1737.  Google Scholar

[30]

A. A. Taha and A. Hanbury, Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool, BMC Medical Imaging, 15 (2015), 29. doi: 10.1186/s12880-015-0068-x.  Google Scholar

[31]

A. Taylor and H. Sinclair, On the determination of lattice parameters by the Debye-Scherrer method, Proceedings of the Physical Society, 57 (1945), 126. doi: 10.1088/0959-5309/57/2/306.  Google Scholar

[32]

F. G. Tricomi, Integral Equations, Dover Publications, Inc., New York, 1985.  Google Scholar

[33]

T. Truong and M. Nguyen, Radon transforms on generalized Cormack's curves and a new Compton scatter tomography modality, Inverse Problems, 27 (2011), 125001, 23pp. doi: 10.1088/0266-5611/27/12/125001.  Google Scholar

[34]

T. T. Truong and M. K. Nguyen, New properties of the V-line Radon transform and their imaging applications, Journal of Physics A: Mathematical and Theoretical, 48 (2015), 405204, 28pp. doi: 10.1088/1751-8113/48/40/405204.  Google Scholar

[35]

T. T. Truong, M. K. Nguyen and H. Zaidi, The mathematical foundations of 3D Compton scatter emission imaging, International Journal of Biomedical Imaging, 2007 (2007), Article ID 092780. doi: 10.1155/2007/92780.  Google Scholar

[36]

N. Wadeson, Modelling and Correction of Scatter in a Switched Source Multi-Ring Detector X-ray CT Machine, PhD thesis, The University of Manchester (United Kingdom), 2011. Google Scholar

[37]

B. E. Warren, X-ray Diffraction, Courier Corporation, 1990. Google Scholar

[38]

J. Webber and E. L. Miller, Compton scattering tomography in translational geometries, Inverse Problems, 36 (2020), 025007, 20pp. doi: 10.1088/1361-6420/ab4a32.  Google Scholar

[39]

J. Webber and E. T. Quinto, Microlocal analysis of a Compton tomography problem, SIAM J. Imaging Sci., 13 (2020), 746–774, arXiv: 1902.09623. doi: 10.1137/19M1251035.  Google Scholar

[40]

J. Webber, E. T. Quinto and E. L. Miller, A joint reconstruction and lambda tomography regularization technique for energy-resolved X-ray imaging, Inverse Problems, 36 (2020), 074002, 32 pp. doi: 10.1088/1361-6420/ab8f82.  Google Scholar

[41]

J. W. Webber and E. T. Quinto, Microlocal analysis of generalized Radon transforms from scattering tomography, arXiv preprint, arXiv: 2007.00208. Google Scholar

show all references

References:
[1]

G. Ambartsoumian, R. Gouia-Zarrad and M. A. Lewis, Inversion of the circular Radon transform on an annulus, Inverse Problems, 26 (2010), 105015, 11pp. doi: 10.1088/0266-5611/26/10/105015.  Google Scholar

[2]

H. Andrade-Loarca, G. Kutyniok and O. Öktem, Shearlets as feature extractor for semantic edge detection: The model-based and data-driven realm, Proc. A., 476 (2020), 841–866. arXiv: 1911.12159. doi: 10.1098/rspa.2019.0841.  Google Scholar

[3]

D. Ballantine Jr, R. M. White, S. J. Martin, A. J. Ricco, E. Zellers, G. Frye and H. Wohltjen, Acoustic Wave Sensors: Theory, Design and Physico-chemical Applications, Elsevier, 1996. Google Scholar

[4]

G. Beylkin, The inversion problem and applications of the generalized Radon transform, Communications on Pure and Applied Mathematics, 37 (1984), 579-599.  doi: 10.1002/cpa.3160370503.  Google Scholar

[5]

R. Bryan, International Tables for Crystallography, Vol. C. Mathematical, physical and chemical tables edited by A. J. C. Wilson, 1993. Google Scholar

[6]

A. M. Cormack, The Radon transform on a family of curves in the plane, Proc. Amer. Math. Soc., 83 (1981), 325-330.  doi: 10.1090/S0002-9939-1981-0624923-1.  Google Scholar

[7]

A. M. Cormack, Radon's problem for some surfaces in $\mathbb{R}^n$, Proc. Amer. Math. Soc., 99 (1987), 305-312.  doi: 10.2307/2046630.  Google Scholar

[8]

J. J. DeMarco and P. Suortti, Effect of scattering on the attenuation of X-rays, Physical Review B, 4 (1971), 1028. Google Scholar

[9]

A. Denisyuk, Inversion of the generalized Radon transform, Translations of the American Mathematical Society-Series 2, 162 (1994), 19-32.  doi: 10.1090/trans2/162/02.  Google Scholar

[10]

M. J. Ehrhardt, K. Thielemans, L. Pizarro, D. Atkinson, S. Ourselin, B. F. Hutton and S. R. Arridge, Joint reconstruction of PET-MRI by exploiting structural similarity, Inverse Problems, 31 (2014), 015001, 23pp. doi: 10.1088/0266-5611/31/1/015001.  Google Scholar

[11]

J. A. Greenberg, K. Krishnamurthy and D. Brady, Snapshot molecular imaging using coded energy-sensitive detection, Optics express, 21 (2013), 25480-25491.  doi: 10.1364/OE.21.025480.  Google Scholar

[12]

A. Greenleaf and G. Uhlmann, Non-local inversion formulas for the X-ray transform, Duke Math. J., 58 (1989), 205-240.  doi: 10.1215/S0012-7094-89-05811-0.  Google Scholar

[13]

M. Hassan, J. A. Greenberg, I. Odinaka and D. J. Brady, Snapshot fan beam coded aperture coherent scatter tomography, Optics Express, 24 (2016), 18277-18289.   Google Scholar

[14]

J. Hubbell, W. J. Veigele, E. Briggs, R. Brown, D. Cromer and d. R. Howerton, Atomic form factors, incoherent scattering functions, and photon scattering cross sections, Journal of Physical and Chemical Reference Data, 4 (1975), 471-538.  doi: 10.1063/1.555523.  Google Scholar

[15]

J. H. Hubbell and I. Overbo, Relativistic atomic form factors and photon coherent scattering cross sections, Journal of Physical and Chemical Reference Data, 8 (1979), 69-106.  doi: 10.1063/1.555593.  Google Scholar

[16]

C.-Y. Jung and S. Moon, Inversion formulas for cone transforms arising in application of Compton cameras, Inverse Problems, 31 (2015), 015006, 20pp. doi: 10.1088/0266-5611/31/1/015006.  Google Scholar

[17]

H. P. Klug and L. E. Alexander, X-ray Diffraction Procedures: For Polycrystalline and Amorphous Materials, X-Ray Diffraction Procedures: For Polycrystalline and Amorphous Materials, 2nd Edition, by Harold P. Klug, Leroy E. Alexander, pp. 992. Wiley-VCH, May 1974., 992. Google Scholar

[18]

K. MacCabe, K. Krishnamurthy, A. Chawla, D. Marks, E. Samei and D. Brady, Pencil beam coded aperture X-ray scatter imaging, Optics Express, 20 (2012), 16310-16320.  doi: 10.1364/OE.20.016310.  Google Scholar

[19]

F. Natterer, The Mathematics of Computerized Tomography, SIAM, 2001. doi: 10.1137/1.9780898719284.  Google Scholar

[20]

M. Nguyen and T. Truong, Inversion of a new circular-arc Radon transform for Compton scattering tomography, Inverse Problems, 26 (2010), 099802, 1 p. doi: 10.1088/0266-5611/26/9/099802.  Google Scholar

[21]

S. J. Norton, Compton scattering tomography, Journal of Applied Physics, 76 (1994), 2007-2015.  doi: 10.1063/1.357668.  Google Scholar

[22]

V. P. Palamodov, A uniform reconstruction formula in integral geometry, Inverse Problems, 28 (2012), 065014, 15pp. doi: 10.1088/0266-5611/28/6/065014.  Google Scholar

[23]

V. Palamodov, An analytic reconstruction for the Compton scattering tomography in a plane, Inverse Problems, 27 (2011), 125004, 8pp. doi: 10.1088/0266-5611/27/12/125004.  Google Scholar

[24]

M. Plancherel and M. Leffler, Contribution à l'étude de la représentation d'une fonction arbitraire par des intégrales définies, Rendiconti del Circolo Matematico di Palermo (1884-1940), 30 (1910), 289-335.   Google Scholar

[25]

E. T. Quinto, The dependence of the generalized Radon transform on defining measures, Transactions of the American Mathematical Society, 257 (1980), 331-346.  doi: 10.1090/S0002-9947-1980-0552261-8.  Google Scholar

[26]

G. Rigaud, 3D Compton scattering imaging: Study of the spectrum and contour reconstruction, arXiv preprint, arXiv: 1908.03066. Google Scholar

[27]

G. Rigaud and B. N. Hahn, 3D Compton scattering imaging and contour reconstruction for a class of Radon transforms, Inverse Problems, 34 (2018), 075004, 22pp. doi: 10.1088/1361-6420/aabf0b.  Google Scholar

[28]

G. Rigaud, M. K. Nguyen and A. K. Louis, Novel numerical inversions of two circular-arc Radon transforms in Compton scattering tomography, Inverse Problems in Science and Engineering, 20 (2012), 809-839.  doi: 10.1080/17415977.2011.653008.  Google Scholar

[29]

A. Smakula and J. Kalnajs, Precision determination of lattice constants with a Geiger-counter X-ray diffractometer, Physical Review, 99 (1955), 1737. doi: 10.1103/PhysRev.99.1737.  Google Scholar

[30]

A. A. Taha and A. Hanbury, Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool, BMC Medical Imaging, 15 (2015), 29. doi: 10.1186/s12880-015-0068-x.  Google Scholar

[31]

A. Taylor and H. Sinclair, On the determination of lattice parameters by the Debye-Scherrer method, Proceedings of the Physical Society, 57 (1945), 126. doi: 10.1088/0959-5309/57/2/306.  Google Scholar

[32]

F. G. Tricomi, Integral Equations, Dover Publications, Inc., New York, 1985.  Google Scholar

[33]

T. Truong and M. Nguyen, Radon transforms on generalized Cormack's curves and a new Compton scatter tomography modality, Inverse Problems, 27 (2011), 125001, 23pp. doi: 10.1088/0266-5611/27/12/125001.  Google Scholar

[34]

T. T. Truong and M. K. Nguyen, New properties of the V-line Radon transform and their imaging applications, Journal of Physics A: Mathematical and Theoretical, 48 (2015), 405204, 28pp. doi: 10.1088/1751-8113/48/40/405204.  Google Scholar

[35]

T. T. Truong, M. K. Nguyen and H. Zaidi, The mathematical foundations of 3D Compton scatter emission imaging, International Journal of Biomedical Imaging, 2007 (2007), Article ID 092780. doi: 10.1155/2007/92780.  Google Scholar

[36]

N. Wadeson, Modelling and Correction of Scatter in a Switched Source Multi-Ring Detector X-ray CT Machine, PhD thesis, The University of Manchester (United Kingdom), 2011. Google Scholar

[37]

B. E. Warren, X-ray Diffraction, Courier Corporation, 1990. Google Scholar

[38]

J. Webber and E. L. Miller, Compton scattering tomography in translational geometries, Inverse Problems, 36 (2020), 025007, 20pp. doi: 10.1088/1361-6420/ab4a32.  Google Scholar

[39]

J. Webber and E. T. Quinto, Microlocal analysis of a Compton tomography problem, SIAM J. Imaging Sci., 13 (2020), 746–774, arXiv: 1902.09623. doi: 10.1137/19M1251035.  Google Scholar

[40]

J. Webber, E. T. Quinto and E. L. Miller, A joint reconstruction and lambda tomography regularization technique for energy-resolved X-ray imaging, Inverse Problems, 36 (2020), 074002, 32 pp. doi: 10.1088/1361-6420/ab8f82.  Google Scholar

[41]

J. W. Webber and E. T. Quinto, Microlocal analysis of generalized Radon transforms from scattering tomography, arXiv preprint, arXiv: 2007.00208. Google Scholar

Figure 1.  The portal scanner geometry. The scanned object is labelled as $ f $. The detectors are collimated to planes, and the scattering events occur along lines $ L = \{x_2 = a, x_3 = 0\} $, for some $ -1<a<1 $. The scatter from $ L $ is measured by detectors $ \mathbf{d}\in\{x_2 = 1, x_3 = \epsilon\} $, for some $ \epsilon>0 $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  A scattering event occurs at a scattering site $ \mathbf{x} $, for photons emitted from a source $ \mathbf{s} $ and recorded at a detector $ \mathbf{d} $. The initial photon energy is $ E $ and the scattered energy is $ E_s $. Here $ \mathbf{v} $ is the direction normal to the detector surface, displayed as a square in the $ x_1x_3 $ plane. The scattering angle is $ \omega = 2\theta $. where $ \theta $ is the Bragg angle
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  Plot of the curves of integration for the Bragg transform for varying $ E $ and $ x_2 $. $ s_1 = 0 $ is fixed
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  A circle with center $ \mathbf{c} = (-\sqrt{r^2-1}, 0) $, radius $ r $ is pictured. The circle intersects the dashed line at two points, whose $ x_1 $ coordinates are the solutions to (4.6)
Figure Options

Download full-size image

Download as PowerPoint slide

figure 1a), and the source width is $ w = |\textbf{b}-\hat{ \mathbf{s}}|\tan\beta $. The momentum transfer is $ q = Eq_1(| \mathbf{x}- \mathbf{s}|) = E\sin\frac{\omega}{2} $">Figure 5.  Bragg scanning modality in the $ n = 2 $ case. A square detector $ \hat{ \mathbf{d}} = ( \mathbf{s}, 1) $ is shown opposite a source $ \hat{ \mathbf{s}} = ( \mathbf{s}, -1) $, and collects photons (shown as wavy lines) scattered from points $ \hat{ \mathbf{x}} = ( \mathbf{x}, 1-|\textbf{b}-\hat{ \mathbf{s}}|) $ on the crystal plane. The crystal sample (the red plane) is placed between, and is parallel to the $ \hat{ \mathbf{s}} $-plane and $ \hat{ \mathbf{d}} $-plane. The center of the base of the cone is $ \textbf{b} $, the source opening angle is $ \beta $ (as in figure 1a), and the source width is $ w = |\textbf{b}-\hat{ \mathbf{s}}|\tan\beta $. The momentum transfer is $ q = Eq_1(| \mathbf{x}- \mathbf{s}|) = E\sin\frac{\omega}{2} $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  $ q_1 $ curve examples. For the decreasing curves displayed we would have to choose $ w>1 $ for the injectivity of $ \mathfrak{B}_1 $ to hold
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 7.  Plot of the curves of integration for the offset Bragg transform for varying $ E $ and $ \frac{E_M}{q_1(w)} $. The line $ \{q = E_m\} $ is displayed in black
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 8.  Invertible design regions $ S $ (the blue regions on the top row) and possible linear $ \Phi $ (the red lines on the bottom row) for varying $ \beta $. Note that the $ \epsilon $ scales of the figures are different for each $ \beta $
Figure Options

Download full-size image

Download as PowerPoint slide

figure 8. The blue lines represent the collimation planes which intersect the tunnel at position $ T = 420(-x_2+1) $, where $ x_2 = \Phi^{-1}(\epsilon) $">Figure 9.  Venetian blind detector configurations for varying source opening angles $ \beta $. We show 21 detector arrays at $ \epsilon = \Phi(x_2) $ for $ x_2\in\left\{-1+\frac{j-1}{10} : 1\leq j\leq 21\right\} $, where for each $ \beta $, $ \Phi $ is the corresponding straight line relationship of figure 8. The blue lines represent the collimation planes which intersect the tunnel at position $ T = 420(-x_2+1) $, where $ x_2 = \Phi^{-1}(\epsilon) $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 10.  $ F(\cdot, Z) $ plots for varying $ Z $. The plots are normalized in $ L^{\infty} $ (by max value)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 11.  Top row – 2-spherical phantom. Bottom row – 4-spherical phantom. The image values in the left column are included for visualization (e.g. to distinguish between different materials) and have no physical meaning
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 12.  2-spherical phantom reconstructions for all $ (x_2, c_{\text{avg}})\in\{205,410,615\}\times\{1, 10\} $, and the Ground Truth (GT) on the top row. To clarify, the GT does not vary with $ c_{\text{avg}} $ and is included for comparison
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 13.  4-spherical phantom reconstructions for all $ (x_2, c_{\text{avg}})\in\{205,410,615\}\times\{1, 10\} $, and the Ground Truth (GT) on the top row. To clarify, the GT does not vary with $ c_{\text{avg}} $ and is included for comparison
Figure Options

Download full-size image

Download as PowerPoint slide

Table 1.  Relative least square error values $ \epsilon_{\text{ls}} $ for all experiments conducted. The values in the left-hand columns give the $ x_2 $ coordinates (in mm) of the sphere centers and the equations of the scanning line profiles (as illustrated in figure 11)
$ \epsilon_{\text{ls}} $ $ c_{\text{avg}}=10 $ $ c_{\text{avg}}=1 $ $ \epsilon_{\text{ls}} $ $ c_{\text{avg}} = 10 $ $ c_{\text{avg}} = 1 $
$ x_2=0 $ $ 0.18 $ $ 0.58 $ $ x_2 = 0 $ $ 0.23 $ $ 0.73 $
$ x_2=205 $ $ 0.18 $ $ 0.56 $ $ x_2 = 205 $ $ 0.22 $ $ 0.71 $
$ x_2=615 $ $ 0.18 $ $ 0.57 $ $ x_2 = 615 $ $ 0.22 $ $ 0.71 $
(A) 2-spherical phantom (B)4-spherical phantom
Table Options

Download as excel

$ \epsilon_{\text{ls}} $ $ c_{\text{avg}}=10 $ $ c_{\text{avg}}=1 $ $ \epsilon_{\text{ls}} $ $ c_{\text{avg}} = 10 $ $ c_{\text{avg}} = 1 $
$ x_2=0 $ $ 0.18 $ $ 0.58 $ $ x_2 = 0 $ $ 0.23 $ $ 0.73 $
$ x_2=205 $ $ 0.18 $ $ 0.56 $ $ x_2 = 205 $ $ 0.22 $ $ 0.71 $
$ x_2=615 $ $ 0.18 $ $ 0.57 $ $ x_2 = 615 $ $ 0.22 $ $ 0.71 $
(A) 2-spherical phantom (B)4-spherical phantom
Table 2.  Mean $ F_1 $ score results. The values in the left-hand columns give the $ x_2 $ coordinates (in mm) of the sphere centers and the equations of the scanning line profiles (as illustrated in figure 11)
Mean $ F_1 $ score $ c_{\text{avg}}=10 $ $ c_{\text{avg}}=1 $ Mean $ F_1 $ score $ c_{\text{avg}} = 10 $ $ c_{\text{avg}} = 1 $
$ x_2=0 $ $ 0.90 $ $ 0.84 $ $ x_2 = 0 $ $ 0.86 $ $ 0.67 $
$ x_2=205 $ $ 0.90 $ $ 0.90 $ $ x_2 = 205 $ $ 0.89 $ $ 0.70 $
$ x_2=615 $ $ 0.91 $ $ 0.90 $ $ x_2 = 615 $ $ 0.87 $ $ 0.73 $
(A) 2-spherical phantom (B) 4-spherical phantom
Table Options

Download as excel

Mean $ F_1 $ score $ c_{\text{avg}}=10 $ $ c_{\text{avg}}=1 $ Mean $ F_1 $ score $ c_{\text{avg}} = 10 $ $ c_{\text{avg}} = 1 $
$ x_2=0 $ $ 0.90 $ $ 0.84 $ $ x_2 = 0 $ $ 0.86 $ $ 0.67 $
$ x_2=205 $ $ 0.90 $ $ 0.90 $ $ x_2 = 205 $ $ 0.89 $ $ 0.70 $
$ x_2=615 $ $ 0.91 $ $ 0.90 $ $ x_2 = 615 $ $ 0.87 $ $ 0.73 $
(A) 2-spherical phantom (B) 4-spherical phantom
[1]

Jan Boman. A local uniqueness theorem for weighted Radon transforms. Inverse Problems & Imaging, 2010, 4 (4) : 631-637. doi: 10.3934/ipi.2010.4.631
[2]

Alberto Ibort, Alberto López-Yela. Quantum tomography and the quantum Radon transform. Inverse Problems & Imaging, , () : -. doi: 10.3934/ipi.2021021
[3]

M. R. Arias, R. Benítez. Properties of solutions for nonlinear Volterra integral equations. Conference Publications, 2003, 2003 (Special) : 42-47. doi: 10.3934/proc.2003.2003.42
[4]

Yufeng Shi, Tianxiao Wang, Jiongmin Yong. Mean-field backward stochastic Volterra integral equations. Discrete & Continuous Dynamical Systems - B, 2013, 18 (7) : 1929-1967. doi: 10.3934/dcdsb.2013.18.1929
[5]

Onur Alp İlhan. Solvability of some volterra type integral equations in hilbert space. Conference Publications, 2007, 2007 (Special) : 28-34. doi: 10.3934/proc.2007.2007.28
[6]

Tianxiao Wang, Yufeng Shi. Symmetrical solutions of backward stochastic Volterra integral equations and their applications. Discrete & Continuous Dynamical Systems - B, 2010, 14 (1) : 251-274. doi: 10.3934/dcdsb.2010.14.251
[7]

Yufeng Shi, Tianxiao Wang, Jiongmin Yong. Optimal control problems of forward-backward stochastic Volterra integral equations. Mathematical Control & Related Fields, 2015, 5 (3) : 613-649. doi: 10.3934/mcrf.2015.5.613
[8]

Z. K. Eshkuvatov, M. Kammuji, Bachok M. Taib, N. M. A. Nik Long. Effective approximation method for solving linear Fredholm-Volterra integral equations. Numerical Algebra, Control & Optimization, 2017, 7 (1) : 77-88. doi: 10.3934/naco.2017004
[9]

François Monard. Efficient tensor tomography in fan-beam coordinates. Ⅱ: Attenuated transforms. Inverse Problems & Imaging, 2018, 12 (2) : 433-460. doi: 10.3934/ipi.2018019
[10]

Anudeep Kumar Arora. Scattering of radial data in the focusing NLS and generalized Hartree equations. Discrete & Continuous Dynamical Systems, 2019, 39 (11) : 6643-6668. doi: 10.3934/dcds.2019289
[11]

Seung Jun Chang, Jae Gil Choi. Generalized transforms and generalized convolution products associated with Gaussian paths on function space. Communications on Pure & Applied Analysis, 2020, 19 (1) : 371-389. doi: 10.3934/cpaa.2020019
[12]

Aki Pulkkinen, Ville Kolehmainen, Jari P. Kaipio, Benjamin T. Cox, Simon R. Arridge, Tanja Tarvainen. Approximate marginalization of unknown scattering in quantitative photoacoustic tomography. Inverse Problems & Imaging, 2014, 8 (3) : 811-829. doi: 10.3934/ipi.2014.8.811
[13]

Yushi Hamaguchi. Extended backward stochastic Volterra integral equations and their applications to time-Inconsistent stochastic recursive control problems. Mathematical Control & Related Fields, 2021, 11 (2) : 433-478. doi: 10.3934/mcrf.2020043
[14]

Dajana Conte, Raffaele D'Ambrosio, Beatrice Paternoster. On the stability of $\vartheta$-methods for stochastic Volterra integral equations. Discrete & Continuous Dynamical Systems - B, 2018, 23 (7) : 2695-2708. doi: 10.3934/dcdsb.2018087
[15]

Ludger Overbeck, Jasmin A. L. Röder. Path-dependent backward stochastic Volterra integral equations with jumps, differentiability and duality principle. Probability, Uncertainty and Quantitative Risk, 2018, 3 (0) : 4-. doi: 10.1186/s41546-018-0030-2
[16]

Ali Gholami, Mauricio D. Sacchi. Time-invariant radon transform by generalized Fourier slice theorem. Inverse Problems & Imaging, 2017, 11 (3) : 501-519. doi: 10.3934/ipi.2017023
[17]

Alexander Balandin. The localized basis functions for scalar and vector 3D tomography and their ray transforms. Inverse Problems & Imaging, 2016, 10 (4) : 899-914. doi: 10.3934/ipi.2016026
[18]

Dorota Bors, Andrzej Skowron, Stanisław Walczak. Systems described by Volterra type integral operators. Discrete & Continuous Dynamical Systems - B, 2014, 19 (8) : 2401-2416. doi: 10.3934/dcdsb.2014.19.2401
[19]

Hermann Brunner. On Volterra integral operators with highly oscillatory kernels. Discrete & Continuous Dynamical Systems, 2014, 34 (3) : 915-929. doi: 10.3934/dcds.2014.34.915
[20]

Sergiu Aizicovici, Yimin Ding, N. S. Papageorgiou. Time dependent Volterra integral inclusions in Banach spaces. Discrete & Continuous Dynamical Systems, 1996, 2 (1) : 53-63. doi: 10.3934/dcds.1996.2.53

2019 Impact Factor: 1.373

Tools

Metrics

  • PDF downloads (85)
  • HTML views (188)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2021 American Institute of Mathematical Sciences