2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) 2017
DOI: 10.1109/isbi.2017.7950469
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Image reconstruction for Magnetic Particle Imaging using an Augmented Lagrangian Method

Abstract: Magnetic particle imaging (MPI) is a relatively new imaging modality that images the spatial distribution of superparamagnetic iron oxide nanoparticles administered to the body. In this study, we use a new method based on Alternating Direction Method of Multipliers (a subset of Augmented Lagrangian Methods, ADMM) with total variation and l1 norm minimization, to reconstruct MPI images. We demonstrate this method on data simulated for a field free line MPI system, and compare its performance against the convent… Show more

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Cited by 13 publications
(12 citation statements)
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References 16 publications
(29 reference statements)
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“…In this study, we used the algebraic reconstruction technique (Kaczmarz method) for image reconstruction. Presumably, better results with reduced artifacts can be obtained by using non‐negative fused LASSO or augmented Lagrangian methods for the reconstruction, which minimize l 1 norm and total variation of the images subject to the data consistency. From the definition of full width at half maximum (FWHM) spatial resolution for the MPI ( Δr ≈ 4.16/ βG ), 4.4‐mm resolution is expected for the SPIO parameters with a monodisperse distribution and 1 T/m gradient used in this study.…”
Section: Discussionmentioning
confidence: 99%
“…In this study, we used the algebraic reconstruction technique (Kaczmarz method) for image reconstruction. Presumably, better results with reduced artifacts can be obtained by using non‐negative fused LASSO or augmented Lagrangian methods for the reconstruction, which minimize l 1 norm and total variation of the images subject to the data consistency. From the definition of full width at half maximum (FWHM) spatial resolution for the MPI ( Δr ≈ 4.16/ βG ), 4.4‐mm resolution is expected for the SPIO parameters with a monodisperse distribution and 1 T/m gradient used in this study.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, a nonnegativity constraint is added to the problem definition as negative pixels are not expected in MPI images. The convex optimization problem was formulated as follows [13]:…”
Section: Standard Methodsmentioning
confidence: 99%
“…The SFM is reconstructed with iterative convex optimization methods using the information that the system functions are sparse in the discrete cosine transform (DCT) domain [9]. In this work, Alternating Direction Method of Multipliers (ADMM) was used as a convex optimization method due to its fast convergence rate and accuracy [13]. In order to reconstruct the SFM, the following problem is solved:…”
Section: Standard Compressed Sensing Of the System Function Matrixmentioning
confidence: 99%
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“…Bu şekilde SKM oluşturulduktan sonra, görüntülenmek istenen objeden alınan sinyalin ayrık Fourier dönüşümü ile bir dogrusal denklemler sistemi elde edilmiş olur. SPDO'ların uzamsal dagılımı bu tersine problemin Kaczmarz (ART) [5], negatif olmayan kaynaşık LASSO [6], Yön Degiştiren Ç arpanlar Yöntemi (YDÇ Y) [7,8] vb. yöntemlerle çözülmesiyle geriçatılır.…”
Section: Introductionunclassified