Dynamic linear inverse problems with moderate movements of the object: Ill-posedness and regularization

Bernadette N. Hahn

doi:10.3934/ipi.2015.9.395

Article Contents

2015, Volume 9, Issue 2: 395-413. Doi: 10.3934/ipi.2015.9.395

This issue Previous Article Some geometric inverse problems for the linear wave equation Next Article A new nonlocal variational setting for image processing

Dynamic linear inverse problems with moderate movements of the object: Ill-posedness and regularization

Bernadette N. Hahn^1,

1.
Tufts University, Department of Mathematics, Medford, MA 02155

Received: April 2014

Revised: October 2014

Published: May 2015

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Abstract

One challenge for modern imaging methods is the investigation of objects which change during the data acquisition. This occurs in non-destructive testing as well as in medical applications, e.g. on account of patient or organ movements. Due to the object's deformations, the respective imaging modality is described by a dynamic inverse problem. In this paper, a classification scheme for linear dynamic problems depending on the object's motion is provided. Based on this scheme, we study the class in detail where the dynamic problem is still related to the operator in the static case, and where we call the deformations moderate. We proof important properties of the dynamic operator, derive a singular value decomposition and develop suitable regularization methods. The application of these methods to specific problems is illustrated at two examples including dynamic computerized tomography.

Keywords:

Mathematics Subject Classification: 65R32, 45Q05, 45A05.

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References

[1]	D. Atkinson, D. L. Hill, P. N. Stoyle, P. E. Summers, S. Clare, R. Bowtell and S. F. Keevil, Automatic compensation of motion artefacts in MRI, Magn. Reson. Med., 41 (1999), 163-170.doi: 10.1002/(SICI)1522-2594(199901)41:1<163::AID-MRM23>3.3.CO;2-0.
[2]	L. Desbat, S. Roux and P. Grangeat, Compensation of some time dependent deformations in tomography, IEEE Trans. Med. Imag., 26 (2007), 261-269.doi: 10.1109/TMI.2006.889743.
[3]	H. W. Engl and C. W. Groetsch, Inverse and Ill-Posed Problems, Academic Press, New York, 1986.
[4]	J. Fitzgerald and P.G. Danias, Effect of motion on cardiac SPECT imaging: Recognition and motion correction, J. Nucl. Cardiol., 8 (2001), 701-706.doi: 10.1067/mnc.2001.118694.
[5]	F. Gigengack, L. Ruthotto, M. Burger, C. H. Wolters, X. Jiang and K. P. Schäfers, Motion correction in dual gated cardiac PET using mass-preserving image registration, IEEE Trans. Med. Imag., 31 (2012), 698-712.doi: 10.1109/TMI.2011.2175402.
[6]	G. H. Glover and J. M. Pauly, Projection Reconstruction Techniques for reduction of motion effects in MRI, Mag. Reson. Med., 28 (1992), 275-289.doi: 10.1002/mrm.1910280209.
[7]	B. Hahn, Reconstruction of dynamic objects with affine deformations in dynamic computerized tomography, J. Inverse Ill-Posed Probl., 22 (2014), 323-339.doi: 10.1515/jip-2012-0094.
[8]	B. N. Hahn, Efficient algorithms for linear dynamic inverse problems with known motion, Inverse Problems, 30 (2014), 035008, 20pp.doi: 10.1088/0266-5611/30/3/035008.
[9]	B. Hofmann, Regularization for Applied Inverse and Ill-posed Problems, Teubner, Leipzig, 1986.doi: 10.1007/978-3-322-93034-7.
[10]	A. Katsevich, An accurate approximate algorithm for motion compensation in two-dimensional tomography, Inverse Problems, 26 (2010), 065007, 16pp.doi: 10.1088/0266-5611/26/6/065007.
[11]	A. Katsevich, M. Silver and A. Zamayatin, Local tomography and the motion estimation problem, SIAM J. Imaging Sci., 4 (2011), 200-219.doi: 10.1137/100796728.
[12]	S. Kindermann and A. Leitão, On regularization methods for inverse problems of dynamic type, Numer. Func. Anal. Opt., 27 (2006), 139-160.doi: 10.1080/01630560600569973.
[13]	D. Le Bihan, C. Poupon, A. Amadon and F. Lethimonnier, Artifacts and pitfalls in diffusion MRI, JMRI - J. Magn. Reson. Im., 24 (2006), 478-488.
[14]	A. K. Louis, Inverse und Schlecht Gestellte Probleme, Teubner, Stuttgart, 1989.doi: 10.1007/978-3-322-84808-6.
[15]	A. K. Louis, Diffusion reconstruction from very noisy tomographic data, Inverse Probl. Imagine, 4 (2010), 675-683.doi: 10.3934/ipi.2010.4.675.
[16]	W. Lu and T. R. Mackie, Tomographic motion detection and correction directly in sinogram space, Phys. Med. Biol., 47 (2002), 1267-1284.doi: 10.1088/0031-9155/47/8/304.
[17]	S. J. McQuaid and B. F. Hutton, Sources of attenuation-correction artefacts in cardiac PET/CT and SPECT/CT, Eur. J. Nucl. Med. Mol. Imaging, 35 (2008), 1117-1123.doi: 10.1007/s00259-008-0718-0.
[18]	J. L. Müller and S. Siltanen, Linear and Nonlinear Inverse Problems with Practical Applications, SIAM, Philadelphia, 2012.
[19]	F. Natterer, The Mathematics of Computerized Tomography, John Wiley & Sons, Chichester, 1986.
[20]	F. Qiao, T. Pan, J. W. Clark, O. R. Mawlawi, A motion-incorporated reconstruction method for gated PET studies, Phys. Med. Biol., 51 (2006), 3769-3783.doi: 10.1088/0031-9155/51/15/012.
[21]	U. Schmitt and A. K. Louis, Efficient algorithms for the regularization of dynamic inverse problems: I. Theory, Inverse Problems, 18 (2002), 645-658.doi: 10.1088/0266-5611/18/3/308.
[22]	U. Schmitt, A. K. Louis, C. Wolters and M. Vauhkonen, Efficient algorithms for the regularization of dynamic inverse problems: II. Applications, Inverse Problems, 18 (2002), 659-676.doi: 10.1088/0266-5611/18/3/309.
[23]	A. Shankaranarayanan, M. Wendt, J. S. Lewin and J. L. Duerk, Two-step navigatorless correction algorithm for radial k-space MRI acquisitions, Magn. Reson. Med., 45 (2001), 277-288.
[24]	L. Shepp, S. Hilal and R. Schulz, The tuning fork artifact in computerized tomography, Comput. Graph. Image Process., 10 (1979), 246-255.doi: 10.1016/0146-664X(79)90004-2.