Variational model-based reconstruction techniques for multi-patch data in Magnetic Particle Imaging
References
[1]
Gleich B., Weizenecker J., Tomographic imaging using the nonlinear response of magnetic particles, Nature 435 (2005) 1214–1217,.
[2]
Knopp T., Buzug T.M., Magnetic Particle Imaging: An Introduction to Imaging Principles and Scanner Instrumentation, Springer, 2012.
[3]
B. Zheng, T. Vazin, W. Yang, P. Goodwill, E. Saritas, L. Croft, D. Schaffer, S. Conolly, Quantitative Stem Cell Imaging with Magnetic Particle Imaging, in: IEEE International Workshop on Magnetic Particle Imaging, 2013, p. 1, https://doi.org/10.1109/IWMPI.2013.6528323.
[4]
Graeser M., Thieben F., Szwargulski P., Werner F., Gdaniec N., Boberg M., Griese F., Möddel M., Ludewig P., van de Ven D., Weber O.M., Woywode O., Gleich B., Knopp T., Human-sized magnetic particle imaging for brain applications, Nature Commun. 10 (1) (2019) 1936,.
[5]
Ter-Pogossian M., Phelps M., Hoffman E., Mullani N., A positron-emission transaxial tomograph for nuclear imaging (PETT), Radiology 114 (1975) 89–98,.
[6]
Kuhl D., Edwards R., Image separation radioisotope scanning, Radiology 80 (1963) 653–662,.
[7]
Knopp T., Biederer S., Sattel T., Erbe M., Buzug T., Prediction of the spatial resolution of magnetic particle imaging using the modulation transfer function of the imaging process, IEEE Trans. Med. Imaging 30 (6) (2011) 1284–1292,.
[8]
Weizenecker J., Borgert J., Gleich B., A simulation study on the resolution and sensitivity of magnetic particle imaging, Phys. Med. Biol. 52 (2007) 6363–6374,.
[9]
Rahmer J., Weizenecker J., Gleich B., Borgert J., Analysis of a 3-D system function measured for magnetic particle imaging, IEEE Trans. Med. Imaging 31 (6) (2012) 1289–1299,.
[10]
Lampe J., Bassoy C., Rahmer J., Weizenecker J., Voss H., Gleich B., Borgert J., Fast reconstruction in magnetic particle imaging, Phys. Med. Biol. 57 (2012) 1113–1134,.
[11]
Knopp T., Rahmer J., Sattel T., Biederer S., Weizenecker J., Gleich B., Borgert J., Buzug T., Weighted iterative reconstruction for magnetic particle imaging, Phys. Med. Biol. 55 (2010) 1577–1589,.
[12]
Storath M., Brandt C., Hofmann M., Knopp T., Salamon J., Weber A., Weinmann A., Edge preserving and noise reducing reconstruction for magnetic particle imaging, IEEE Trans. Med. Imaging 36 (1) (2016) 74–85,.
[13]
Askin B., Güngör A., Alptekin Soydan D., Saritas E.U., Top C.B., Cukur T., PP-MPI: A deep plug-and-play prior for magnetic particle imaging reconstruction, in: Machine Learning for Medical Image Reconstruction, Springer International Publishing, 2022, pp. 105–114.
[14]
Güngör A., Askin B., Soydan D.A., Top C.B., Saritas E.U., Çukur T., DEQ-MPI: A deep equilibrium reconstruction with learned consistency for magnetic particle imaging, IEEE Trans. Med. Imaging (2023) 1,.
[15]
Lempitsky V., Vedaldi A., Ulyanov D., Deep image prior, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 9446–9454,.
[16]
Dittmer S., Kluth T., Henriksen M.T.R., Maass P., Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on open MPI dataset, 2020, arXiv preprint arXiv:2007.01593.
[17]
Shang Y., Liu J., Zhang L., Wu X., Zhang P., Yin L., Hui H., Tian J., Deep learning for improving the spatial resolution of magnetic particle imaging, Phys. Med. Biol. 67 (12) (2022),.
[18]
Knopp T., Weber A., Sparse reconstruction of the magnetic particle imaging system matrix, IEEE Trans. Med. Imaging 32 (8) (2013) 1473–1480,.
[19]
Weber A., Knopp T., Symmetries of the 2D magnetic particle imaging system matrix, Phys. Med. Biol. 60 (10) (2015) 4033,.
[20]
Weber A., Knopp T., Reconstruction of the magnetic particle imaging system matrix using symmetries and compressed sensing, Adv. Math. Phys. 2015 (2015),.
[21]
Güngör A., Askin B., Soydan D.A., Barış Top C., Cukur T., Deep learned super resolution of system matrices for magnetic particle imaging, in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC, 2021, pp. 3749–3752,.
[22]
Schrank F., Pantke D., Schulz V., Deep learning MPI super-resolution by implicit representation of the system matrix, 8, 2022,.
[23]
Güngör A., Askin B., Soydan D.A., Saritas E.U., Top C.B., Çukur T., TranSMS: Transformers for super-resolution calibration in magnetic particle imaging, IEEE Trans. Med. Imaging 41 (12) (2022) 3562–3574,.
[24]
Yin L., Guo H., Zhang P., Li Y., Hui H., Du Y., Tian J., System matrix recovery based on deep image prior in magnetic particle imaging, Phys. Med. Biol. 68 (3) (2023),.
[25]
Scheffler K., Boberg M., Knopp T., Boundary artifact reduction by extrapolating system matrices outside the field-of-view in joint multi-patch MPI, Int. J. Mag. Part Imag. 8 (1, Suppl 1) (2022),.
[26]
Scheffler K., Boberg M., Knopp T., Extrapolation of system matrices in magnetic particle imaging, IEEE Trans. Med. Imaging 42 (4) (2023) 1121–1132,.
[27]
Knopp T., Sattel T.F., Biederer S., Rahmer J., Weizenecker J., Gleich B., Borgert J., Buzug T.M., Model-based reconstruction for magnetic particle imaging, IEEE Trans. Med. Imaging 29 (2010) 12–18,.
[28]
Knopp T., Biederer S., Sattel T.F., Rahmer J., Weizenecker J., Gleich B., Borgert J., Buzug T.M., 2D model-based reconstruction for magnetic particle imaging, Med. Phys. 37 (2010) 485–491,.
[29]
Rahmer J., Weizenecker J., Gleich B., Borgert J., Signal encoding in magnetic particle imaging: Properties of the system function, BMC Med. Imag. 9 (2009) 4,.
[30]
H. Schomberg, Magnetic Particle Imaging: Model and Reconstruction, in: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010, pp. 992–995, https://doi.org/10.1109/ISBI.2010.5490155.
[31]
Goodwill P., Conolly S.M., The X-Space formulation of the magnetic particle imaging process: 1-D signal, resolution, bandwidth, SNR, SAR, and magnetostimulation, IEEE Trans. Med. Imaging 29 (2010) 1851–1859,.
[32]
Goodwill P., Conolly S.M., Multidimensional X-Space magnetic particle imaging, IEEE Trans. Med. Imaging 30 (2011) 1581–1590,.
[33]
Grüttner M., Knopp T., Franke J., Heidenreich M., Rahmer J., Halkola A., Kaethner C., Borgert J., Buzug T.M., On the formulation of the image reconstruction problem in magnetic particle imaging, Biomed. Eng. 58 (6) (2013) 583–591,.
[34]
Bringout G., Erb W., Frikel J., A new 3D model for magnetic particle imaging using realistic magnetic field topologies for algebraic reconstruction, Inverse Problems 36 (12) (2020),.
[35]
Schmale I., Gleich B., Rahmer J., Bontus C., Schmidt J., Borgert J., MPI safety in the view of MRI safety standards, IEEE Trans. Magn. 51 (2) (2015) 1–4,.
[36]
Saritas E.U., Goodwill P.W., Zhang G.Z., Conolly S.M., Magnetostimulation limits in magnetic particle imaging, IEEE Trans. Med. Imaging 32 (9) (2013) 1600–1610,.
[37]
Szwargulski P., Möddel M., Gdaniec N., Knopp T., Efficient joint image reconstruction of multi-patch data reusing a single system matrix in magnetic particle imaging, IEEE Trans. Med. Imaging 38 (4) (2018) 932–944,.
[38]
Gdaniec N., Boberg M., Möddel M., Szwargulski P., Knopp T., Suppression of motion artifacts caused by temporally recurring tracer distributions in multi-patch magnetic particle imaging, IEEE Trans. Med. Imaging 39 (11) (2020) 3548–3558,.
[39]
Weizenecker J., Bontus C., Rahmer J., Schmidt J., Kanzenbach J., Borgert J., Fast MPI demonstrator with enlarged field of view, 2010.
[40]
Szwargulski P., Gdaniec N., Graeser M., Möddel M., Griese F., Krishnan K.M., Buzug T.M., Knopp T., Moving table magnetic particle imaging: A stepwise approach preserving high spatio-temporal resolution, J. Med. Imaging 5 (4) (2018),.
[41]
Gapyak V., März T., Weinmann A., Quality-enhancing techniques for model-based reconstruction in magnetic particle imaging, Mathematics 10 (18) (2022),.
[42]
Knopp T., Them K., Kaul M., Gdaniec N., Joint reconstruction of non-overlapping magnetic particle imaging focus-field data, Phys. Med. Biol. 60 (8) (2015) L15,.
[43]
Boberg M., Knopp T., Szwargulski P., Möddel M., Generalized MPI multi-patch reconstruction using clusters of similar system matrices, IEEE Trans. Med. Imaging 39 (5) (2020) 1347–1358,.
[44]
März T., Weinmann A., Model-based reconstruction for magnetic particle imaging in 2D and 3D, Inverse Prob. Imag. 10 (4) (2016) 1087–1110,.
[45]
März T., Gapyak V., Weinmann A., A two-stage model-based regularized reconstruction approach for magnetic particle imaging, 2939, 2023,.
[46]
Gapyak V., März T., Weinmann A., Quality-enhancing techniques for a two-stage model-based approach for magnetic particle imaging, 3094, 2024,.
[47]
Knopp T., Biederer S., Sattel T.F., Weizenecker J., Gleich B., Borgert J., Buzug T.M., Trajectory aanalysis for magnetic particle imaging, Phys. Med. Biol. 54 (2009) 385–397,.
[48]
Knopp T., Szwargulski P., Griese F., Gräser M., OpenMPIData: An initiative for freely accessible magnetic particle imaging data, Data Brief 28 (2020),.
[49]
Kalender W.A., Seissler W., Klotz E., Vock P., Spiral volumetric CT with single-breath-hold technique, continuous transport, and continuous scanner rotation, Radiology 176 1 (1990) 181–183,.
[50]
Kruger D.G., Riederer S.J., Grimm R.C., Rossman P.J., Continuously moving table data acquisition method for long FOV contrast-enhanced MRA and whole-body MRI, Magn. Reson. Med. 47 (2002),.
[51]
Goldstein H., Classical Mechanics, Addison-Wesley, 1980.
[52]
Chikazumi S., Charap S., Physics of Magnetism, Krieger Publishing, New York, 1978.
[53]
Jiles D., Introduction to Magnetism and Magnetic Materials, CRC Press, 1998.
[54]
Gleich B., Weizenecker J., Tomographic imaging using the nonlinear response of magnetic particles, Nature 435 (7046) (2005) 1214–1217,.
[55]
Shasha C., Teeman E., Krishnan K.M., Nanoparticle core size optimization for magnetic particle imaging, Biomed. Phys. Eng. Express 5 (5) (2019),.
[56]
Croft L.R., Goodwill P.W., Conolly S.M., Relaxation in X-Space magnetic particle imaging, IEEE Trans. Med. Imaging 31 (12) (2012) 2335–2342,.
[57]
März T., Gapyak V., Weinmann A., A flexible model-based regularized reconstruction approach for magnetic particle imaging, 3094, 2024,.
[58]
Bertero M., Boccacci P., De Mol C., Introduction to Inverse Problems in Imaging, CRC Press, 2021.
[59]
Hansen P.C., Discrete Inverse Problems: Insight and Algorithms, SIAM, 2010.
[60]
Ambrosio L., Fusco N., Pallara D., Functions of Bounded Variation and Free Discontinuity Problems, Oxford Mathematical Monographs, 2000.
[61]
Dobson D., Scherzer O., Analysis of regularized total variation penalty methods for denoising, Inverse Problems 12 (5) (1996) 601,.
[62]
Vogel C.R., Oman M.E., Iterative methods for total variation denoising, SIAM J. Sci. Comput. 17 (1) (1996) 227–238,.
[63]
Vogel C.R., Oman M.E., Fast, robust total variation-based reconstruction of noisy, blurred images, IEEE Trans. Image Process. 7 (6) (1998) 813–824,.
[64]
Weizenecker J., Gleich B., Rahmer J., Dahnke H., Borgert J., Three-dimensional real-time in vivo magnetic particle imaging, Phys. Med. Biol. 54 (2009) L1–L10,.
[65]
Storath M., Weinmann A., Frikel J., Unser M., Joint image reconstruction and segmentation using the potts model, Inverse Problems 31 (2) (2015),.
[66]
Storath M., Rickert D., Unser M., Weinmann A., Fast segmentation from blurred data in 3D fluorescence microscopy, IEEE Trans. Image Process. 26 (10) (2017) 4856–4870,.
[67]
Bauschke H.H., Combettes P.L., Convex Analysis and Monotone Operator Theory in Hilbert Spaces, first ed., Springer Publishing Company, 2011, Incorporated.
[68]
Ekeland I., Téman R., Convex Analysis and Variational Problems, Society for Industrial and Applied Mathematics, USA, 1999.
[69]
Parikh N., Boyd S., Proximal algorithms, Found. Trends Optim. 1 (3) (2014) 127–239,.
[70]
Raguet H., Fadili J., Peyré G., A generalized forward-backward splitting, SIAM J. Imaging Sci. 6 (3) (2013) 1199–1226,.
[71]
Golub G.H., Van Loan C.F., Matrix Computations, JHU Press, 2013.
[72]
Hosseini A., Bredies K., A second-order TGV discretization with 9 0 ∘ rotational invariance property, 2023, arXiv:2209.11450.
[73]
Rudin L.I., Osher S., Fatemi E., Nonlinear total variation based noise removal algorithms, Phys. D: Nonlinear Phenomena 60 (1–4) (1992) 259–268,.
[74]
Edwards J., Advanced Calculus of Several Variables, Academic Press, 1973.
[75]
Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P., Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process. 13 (2004) 600–612,.
[76]
Ahlborg M., Kaethner C., Knopp T., Szwargulski P., Buzug T.M., Using data redundancy gained by patch overlaps to reduce truncation artifacts in magnetic particle imaging, Phys. Med. Biol. 61 (12) (2016) 4583–4598,.
[77]
Zdun L., Boberg M., Brandt C., Joint multi-patch reconstruction: Fast and improved results by stochastic optimization, Int. J. Mag. Part Imag. 8 (2) (2022),.
[78]
T. Knopp, N. Gdaniec, M. Möddel, Magnetic Particle Imaging: from Proof of Principle to Preclinical Applications, Phys. Med. Bio https://doi.org/10.1088/1361-6560/aa6c99.
[79]
Gdaniec N., Boberg M., Moddel M., Szwargulski P., Knopp T., Suppression of motion artifacts caused by temporally recurring tracer distributions in multi-patch magnetic particle imaging, IEEE Trans. Med. Imaging 39 (11) (2020) 3548–3558,.
[80]
Soydan D.A., Güngör A., Top C.B., A simulation study for three dimensional tomographic field free line magnetic particle imaging, in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC, 2021, pp. 3701–3704,.
[81]
Kluth T., Szwargulski P., Knopp T., Towards accurate modeling of the multidimensional magnetic particle imaging physics, New J. Phys. 21 (10) (2019),.
[82]
Yagiz E., Cagil A.R., Saritas E.U., Non-ideal selection field induced artifacts in X-Space MPI, Int. J. Mag. Part Imag. 6 (2) (2020),.
[83]
Brandt C., Schmidt C., Modeling magnetic particle imaging for dynamic tracer distributions, Sens. Imag. 22 (1) (2021) 45,.
[84]
Kiefer L., Storath M., Weinmann A., Iterative potts minimization for the recovery of signals with discontinuities from indirect measurements: the multivariate case, Found. Comput. Math. 21 (3) (2021) 649–694,.
[85]
Weinmann A., Storath M., Demaret L., The L 1-potts functional for robust jump-sparse reconstruction, SIAM J. Numer. Anal. 53 (1) (2015) 644–673,.
Recommendations
PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction
Machine Learning for Medical Image ReconstructionAbstractMagnetic particle imaging (MPI) is a recent modality that enables high contrast and frame-rate imaging of the magnetic nanoparticle (MNP) distribution. Based on a measured system matrix, MPI reconstruction can be cast as an inverse problem that is ...
Increased volume coverage in 3D magnetic particle imaging
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication TechnologiesMagnetic particle imaging (MPI) is a new tomographic imaging approach that detects and localizes magnetic nano-particles by their non-linear magnetization response to externally applied fields. MPI allows quantitative, sensitive, and rapid volumetric ...
A Deep Prior Approach to Magnetic Particle Imaging
Machine Learning for Medical Image ReconstructionAbstractMagnetic particle imaging (MPI) is a tracer-based imaging modality with an increasing number of potential medical applications exploiting the nonlinear magnetization behavior of magnetic nanoparticles. The image reconstruction is obtained by ...
Comments
Information & Contributors
Information
Published In
The Authors.
Publisher
Elsevier Science Publishers B. V.
Netherlands
Publication History
Published: 08 August 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Aug 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in