On the degree of ill-posedness of multi-dimensional magnetic particle imaging

Magnetic particle imaging is an imaging modality of relatively recent origin, and it exploits the nonlinear magnetization response for reconstructing the concentration of nanoparticles. Since first invented in 2005, it has received much interest in the literature. In this work, we study one prototypical mathematical model in multi-dimension, i.e., the equilibrium model, which formulates the problem as a linear Fredholm integral equation of the first kind. We analyze the degree of ill-posedness of the associated linear integral operator by means of the singular value decay estimate for Sobolev smooth bivariate functions, and discuss the influence of various experimental parameters. In particular, applied magnetic fields with a field free point and a field free line are distinguished. The study is complemented with extensive numerical experiments.

[1]  Tobias Kluth,et al.  Mathematical models for magnetic particle imaging , 2018, Inverse Problems.

[2]  Yong Wu,et al.  Dependence of Brownian and Néel relaxation times on magnetic field strength. , 2013, Medical physics.

[3]  B Gleich,et al.  A simulation study on the resolution and sensitivity of magnetic particle imaging , 2007, Physics in medicine and biology.

[4]  Zhimin Zhang,et al.  How Many Numerical Eigenvalues Can We Trust? , 2013, J. Sci. Comput..

[5]  Stefan Kindermann,et al.  On the Degree of Ill-posedness for Linear Problems with Noncompact Operators , 2010 .

[6]  Michael Griebel,et al.  Approximation of bi-variate functions: singular value decomposition versus sparse grids , 2014 .

[7]  Thorsten M. Buzug,et al.  Mathematical analysis of the 1D model and reconstruction schemes for magnetic particle imaging , 2017, 1711.08074.

[8]  M. Kreĭn,et al.  Introduction to the theory of linear nonselfadjoint operators , 1969 .

[9]  Thorsten M. Buzug,et al.  Model-Based Reconstruction for Magnetic Particle Imaging , 2010, IEEE Transactions on Medical Imaging.

[10]  Volker Behr und Peter Jakob Magnetic particle imaging. , 2015, Zeitschrift fur medizinische Physik.

[11]  B Gleich,et al.  Three-dimensional real-time in vivo magnetic particle imaging , 2009, Physics in medicine and biology.

[12]  Tobias Knopp,et al.  Magnetic Particle / Magnetic Resonance Imaging: In-Vitro MPI-Guided Real Time Catheter Tracking and 4D Angioplasty Using a Road Map and Blood Pool Tracer Approach , 2016, PloS one.

[13]  A. Pietsch Eigenvalues and S-Numbers , 1987 .

[14]  G. Burton Sobolev Spaces , 2013 .

[15]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[16]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[17]  A. Louis Inverse und schlecht gestellte Probleme , 1989 .

[18]  A. Weinmann,et al.  Model-Based Reconstruction for Magnetic Particle Imaging in 2D and 3D , 2016, 1605.08095.

[19]  K. M. Krishnan,et al.  Evaluation of PEG-coated iron oxide nanoparticles as blood pool tracers for preclinical magnetic particle imaging. , 2017, Nanoscale.

[20]  Anselm von Gladiss,et al.  MDF: Magnetic Particle Imaging Data Format , 2016, 1602.06072.

[21]  B. Hofmann,et al.  On Ill-Posedness Measures and Space Change in Sobolev Scales , 1997 .

[22]  Anna Bakenecker,et al.  Experimental Validation of the Selection Field of a Rabbit-Sized FFL Scanner , 2017 .

[23]  Patrick W. Goodwill,et al.  Magnetic Particle Imaging: A Novel in Vivo Imaging Platform for Cancer Detection. , 2017, Nano letters.

[24]  Bernhard Gleich,et al.  Magnetic particle imaging using a field free line , 2008 .

[25]  Tobias Knopp,et al.  Sensitivity Enhancement in Magnetic Particle Imaging by Background Subtraction , 2016, IEEE Transactions on Medical Imaging.

[26]  Bangti Jin,et al.  Inverse Problems , 2014, Series on Applied Mathematics.

[27]  H. König Eigenvalue Distribution of Compact Operators , 1986 .

[28]  P. Maass,et al.  Wavelet-Galerkin methods for ill-posed problems , 1996 .

[29]  G. Wahba Ill Posed Problems: Numerical and Statistical Methods for Mildly, Moderately and Severely Ill Posed Problems with Noisy Data. , 1980 .

[30]  R. Caflisch,et al.  Quasi-Monte Carlo integration , 1995 .

[31]  Jan van Neerven,et al.  Analysis in Banach Spaces , 2023, Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge / A Series of Modern Surveys in Mathematics.

[32]  D. Owen Handbook of Mathematical Functions with Formulas , 1965 .

[33]  Thorsten M. Buzug,et al.  Singular value analysis for Magnetic Particle Imaging , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[34]  Bernhard Gleich,et al.  Tomographic imaging using the nonlinear response of magnetic particles , 2005, Nature.

[35]  Olaf Kosch,et al.  Concentration Dependent MPI Tracer Performance , 2016 .

[36]  Thorsten M. Buzug,et al.  A Fourier slice theorem for magnetic particle imaging using a field-free line , 2011 .

[37]  Tobias Knopp,et al.  Magnetic particle imaging: from proof of principle to preclinical applications , 2017, Physics in medicine and biology.

[38]  M. Krasnosel’skiǐ,et al.  Integral operators in spaces of summable functions , 1975 .

[39]  Bernd Hofmann,et al.  Direct and inverse results in variable Hilbert scales , 2008, J. Approx. Theory.

[40]  Mathematical Analysis of the 1 D Model and Reconstruction Schemes for Magnetic Particle Imaging , 2022 .

[41]  A. Owen Quasi-Monte Carlo for integrands with point singularities at unknown locations , 2004 .

[42]  E. Stein,et al.  Introduction to Fourier Analysis on Euclidean Spaces. , 1971 .

[43]  Hyunjoong Kim,et al.  Functional Analysis I , 2017 .

[44]  Kenya Murase,et al.  Usefulness of Magnetic Particle Imaging for Predicting the Therapeutic Effect of Magnetic Hyperthermia , 2015 .

[45]  Gerald B. Folland,et al.  Real Analysis: Modern Techniques and Their Applications , 1984 .

[46]  Bernhard Gleich,et al.  Magnetic Particle imaging : Visualization of Instruments for Cardiovascular Intervention 1 , 2012 .

[47]  Guanglian Li,et al.  On the Decay Rate of the Singular Values of Bivariate Functions , 2018, SIAM J. Numer. Anal..

[48]  H. Engl,et al.  Regularization of Inverse Problems , 1996 .

[49]  Tobias Kluth,et al.  Model uncertainty in magnetic particle imaging: Nonlinear problem formulation and model-based sparse reconstruction , 2017 .

[50]  Otmar Scherzer,et al.  Factors influencing the ill-posedness of nonlinear problems , 1994 .

[51]  Thorsten M. Buzug,et al.  Magnetic Particle Imaging: An Introduction to Imaging Principles and Scanner Instrumentation , 2012 .

[52]  H. Triebel Interpolation Theory, Function Spaces, Differential Operators , 1978 .

[53]  Tsuyoshi Murata,et al.  {m , 1934, ACML.