Weighted iterative reconstruction for magnetic particle imaging
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Weighted iterative reconstruction for magnetic particle imaging. / Knopp, T; Rahmer, J; Sattel, T F; Biederer, S; Weizenecker, J; Gleich, B; Borgert, J; Buzug, T M.
in: PHYS MED BIOL, Jahrgang 55, Nr. 6, 21.03.2010, S. 1577-89.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Weighted iterative reconstruction for magnetic particle imaging
AU - Knopp, T
AU - Rahmer, J
AU - Sattel, T F
AU - Biederer, S
AU - Weizenecker, J
AU - Gleich, B
AU - Borgert, J
AU - Buzug, T M
PY - 2010/3/21
Y1 - 2010/3/21
N2 - Magnetic particle imaging (MPI) is a new imaging technique capable of imaging the distribution of superparamagnetic particles at high spatial and temporal resolution. For the reconstruction of the particle distribution, a system of linear equations has to be solved. The mathematical solution to this linear system can be obtained using a least-squares approach. In this paper, it is shown that the quality of the least-squares solution can be improved by incorporating a weighting matrix using the reciprocal of the matrix-row energy as weights. A further benefit of this weighting is that iterative algorithms, such as the conjugate gradient method, converge rapidly yielding the same image quality as obtained by singular value decomposition in only a few iterations. Thus, the weighting strategy in combination with the conjugate gradient method improves the image quality and substantially shortens the reconstruction time. The performance of weighting strategy and reconstruction algorithms is assessed with experimental data of a 2D MPI scanner.
AB - Magnetic particle imaging (MPI) is a new imaging technique capable of imaging the distribution of superparamagnetic particles at high spatial and temporal resolution. For the reconstruction of the particle distribution, a system of linear equations has to be solved. The mathematical solution to this linear system can be obtained using a least-squares approach. In this paper, it is shown that the quality of the least-squares solution can be improved by incorporating a weighting matrix using the reciprocal of the matrix-row energy as weights. A further benefit of this weighting is that iterative algorithms, such as the conjugate gradient method, converge rapidly yielding the same image quality as obtained by singular value decomposition in only a few iterations. Thus, the weighting strategy in combination with the conjugate gradient method improves the image quality and substantially shortens the reconstruction time. The performance of weighting strategy and reconstruction algorithms is assessed with experimental data of a 2D MPI scanner.
KW - Algorithms
KW - Image Enhancement
KW - Least-Squares Analysis
KW - Magnetics
KW - Metal Nanoparticles
KW - Molecular Imaging
KW - Particle Size
KW - Sensitivity and Specificity
KW - Time Factors
KW - Journal Article
U2 - 10.1088/0031-9155/55/6/003
DO - 10.1088/0031-9155/55/6/003
M3 - SCORING: Journal article
C2 - 20164532
VL - 55
SP - 1577
EP - 1589
JO - PHYS MED BIOL
JF - PHYS MED BIOL
SN - 0031-9155
IS - 6
ER -