Bias-reduction for sparsity promoting regularization in magnetic particle imaging
Standard
Bias-reduction for sparsity promoting regularization in magnetic particle imaging. / Nawwas, L.; Möddel, M.; Knopp, T.; Brandt, C.
In: Int J Magn Part Imag, Vol. 6, No. 2, 2009041, 2020.Research output: SCORING: Contribution to journal › Other (editorial matter etc.) › Research
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Bias-reduction for sparsity promoting regularization in magnetic particle imaging
AU - Nawwas, L.
AU - Möddel, M.
AU - Knopp, T.
AU - Brandt, C.
N1 - Publisher Copyright: © 2020 Nawwas et al.; licensee Infinite Science Publishing GmbH.
PY - 2020
Y1 - 2020
N2 - Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.
AB - Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.
U2 - 10.18416/IJMPI.2020.2009041
DO - 10.18416/IJMPI.2020.2009041
M3 - Other (editorial matter etc.)
AN - SCOPUS:85090249309
VL - 6
JO - Int J Magn Part Imag
JF - Int J Magn Part Imag
SN - 2365-9033
IS - 2
M1 - 2009041
ER -