IBI: Research

Actuation and Imaging of Magnetic Nanoparticles

Magnetic Particle Imaging

Actuation and Imaging of Magnetic Nanoparticles

In this project, we exploit the ability of magnetic particle imaging to manipulate magnetic particles with magnetic force and simultaneously image the particles by switching between both modes. In the imaging mode, we perform a normal imaging sequence, while in the force mode, we use the focus fields to move the FFP away from the particles to induce a magnetic force by increasing the magnetic field strength at the particle position. We are able to continuously switch between imaging and force modes and obtain a temporal imaging resolution of 4Hz.

Magnetic force mode

In order to be able to see this content, you must accept the integration of external content. Find out more in our privacy policy

Members

Florian Griese
Nadine Gdaniec
Tobias Knopp

Publications

[78995]

Title: Direct Image Reconstruction of Lissajous Type Magnetic Particle Imaging Data using Chebyshev-based Matrix Compression.

Written by: L. Schmiester, M. Möddel, W. Erb, and T. Knopp

in: IEEE Transactions on Computational Imaging. (2017).

Volume: Number:

on pages:

Chapter:

Editor:

Publisher:

Series:

Address:

Edition:

ISBN:

how published:

Organization:

School:

Institution:

Type:

DOI: 10.1109/TCI.2017.2706058

URL:

ARXIVID:

PMID:

Note: article, matrix compression, real-time

Abstract: mage reconstruction in magnetic particle imaging (MPI) is done using an algebraic approach for Lissajous-type measurement sequences. By solving a large linear system of equations, the spatial distribution of magnetic nanoparticles can be determined. Despite the use of iterative solvers that converge rapidly, the size of the MPI system matrix leads to reconstruction times that are typically much longer than the actual data acquisition time. For this reason, matrix compression techniques have been introduced that transform the MPI system matrix into a sparse domain and then utilize this sparsity for accelerated reconstruction. Within this work, we investigate the Chebyshev transformation for matrix compression and show that it can provide better reconstruction results for high compression rates than the commonly applied Cosine transformation. By reducing the number of coefficients per matrix row to one, it is even possible to derive a direct reconstruction method that obviates the usage of iterative solvers.

Bimodal Fiducial Markers

Magnetic Particle Imaging

Bimodal MRI/MPI Fiducial Markers

In this project we develop bimodal fiducial markers for magnetic resonance and magnetic particle imaging to perform positioning within MPI experiments and to register and fuse images of both modalities.

Compared to most other medical imaging techniques MPI only visualizes an applied tracer without additional morphological information. However, this information is crucial for the interpretation of magnetic particle images and the positioning of objects within the MPI scanner.

Our bimodal fiducial markers provide visual landmarks in MP and MR images. These landmarks can be used as points of reference to perform faithful positioning within the MPI scanner prior to MPI experiments. Furthermore, they can be used for an automated image registration and fusion.

Members

Martin Möddel
Franziska Werner
Caroline Jung, Department of Interventional and Diagnostic Radiology
Rene Werner, Department of Computational Neuroscience
Alexander Schlaefer
Tobias Knopp

Grants

This project was funded by the FMTHH (grant number 01fmthh15)

Publications

[78995]

Title: Direct Image Reconstruction of Lissajous Type Magnetic Particle Imaging Data using Chebyshev-based Matrix Compression.

Written by: L. Schmiester, M. Möddel, W. Erb, and T. Knopp

in: IEEE Transactions on Computational Imaging. (2017).

Volume: Number:

on pages:

Chapter:

Editor:

Publisher:

Series:

Address:

Edition:

ISBN:

how published:

Organization:

School:

Institution:

Type:

DOI: 10.1109/TCI.2017.2706058

URL:

ARXIVID:

PMID:

Note: article, matrix compression, real-time

Abstract: mage reconstruction in magnetic particle imaging (MPI) is done using an algebraic approach for Lissajous-type measurement sequences. By solving a large linear system of equations, the spatial distribution of magnetic nanoparticles can be determined. Despite the use of iterative solvers that converge rapidly, the size of the MPI system matrix leads to reconstruction times that are typically much longer than the actual data acquisition time. For this reason, matrix compression techniques have been introduced that transform the MPI system matrix into a sparse domain and then utilize this sparsity for accelerated reconstruction. Within this work, we investigate the Chebyshev transformation for matrix compression and show that it can provide better reconstruction results for high compression rates than the commonly applied Cosine transformation. By reducing the number of coefficients per matrix row to one, it is even possible to derive a direct reconstruction method that obviates the usage of iterative solvers.

Guidance of Vascular Interventions

Magnetic Particle Imaging

Interventional Magnetic Particle Imaging

Magnetic particle imaging is a new radiation-free tomographic imaging method providing fast, background-free, sensitive, directly quantifiable information about the spatial distribution of SPIOs at high temporal resolution. In this project we investigate its potential to offer an alternative to traditional Digital subtraction angiography in interventional procedures.

Multi-contrast MPI makes it possible to jointly image blood pool tracer and labeled cardiovascular devices.

Members

Publications

[78995]

Title: Direct Image Reconstruction of Lissajous Type Magnetic Particle Imaging Data using Chebyshev-based Matrix Compression.

Written by: L. Schmiester, M. Möddel, W. Erb, and T. Knopp

in: IEEE Transactions on Computational Imaging. (2017).

Volume: Number:

on pages:

Chapter:

Editor:

Publisher:

Series:

Address:

Edition:

ISBN:

how published:

Organization:

School:

Institution:

Type:

DOI: 10.1109/TCI.2017.2706058

URL:

ARXIVID:

PMID:

Note: article, matrix compression, real-time

Abstract: mage reconstruction in magnetic particle imaging (MPI) is done using an algebraic approach for Lissajous-type measurement sequences. By solving a large linear system of equations, the spatial distribution of magnetic nanoparticles can be determined. Despite the use of iterative solvers that converge rapidly, the size of the MPI system matrix leads to reconstruction times that are typically much longer than the actual data acquisition time. For this reason, matrix compression techniques have been introduced that transform the MPI system matrix into a sparse domain and then utilize this sparsity for accelerated reconstruction. Within this work, we investigate the Chebyshev transformation for matrix compression and show that it can provide better reconstruction results for high compression rates than the commonly applied Cosine transformation. By reducing the number of coefficients per matrix row to one, it is even possible to derive a direct reconstruction method that obviates the usage of iterative solvers.

Online Reconstruction

Magnetic Particle Imaging

Online Reconstruction for Magnetic Particle Imaging

MPI is an imaging modality that provides very high acquisition rates with up to 46 volumes per second. However, in practice in order to show images of the SPIO distribution directly on the screen it is equally important that the data reconstruction is fast enough to handle the incoming raw data from the receiver unit. Within this project we develop efficient algorithms that allow to reconstruct the SPIO distribution in near real-time such that the reconstructed images can be shown directly on the acquisition computer.

Members

Publications

[78995]

Title: Direct Image Reconstruction of Lissajous Type Magnetic Particle Imaging Data using Chebyshev-based Matrix Compression.

Written by: L. Schmiester, M. Möddel, W. Erb, and T. Knopp

in: IEEE Transactions on Computational Imaging. (2017).

Volume: Number:

on pages:

Chapter:

Editor:

Publisher:

Series:

Address:

Edition:

ISBN:

how published:

Organization:

School:

Institution:

Type:

DOI: 10.1109/TCI.2017.2706058

URL:

ARXIVID:

PMID:

Note: article, matrix compression, real-time

Abstract: mage reconstruction in magnetic particle imaging (MPI) is done using an algebraic approach for Lissajous-type measurement sequences. By solving a large linear system of equations, the spatial distribution of magnetic nanoparticles can be determined. Despite the use of iterative solvers that converge rapidly, the size of the MPI system matrix leads to reconstruction times that are typically much longer than the actual data acquisition time. For this reason, matrix compression techniques have been introduced that transform the MPI system matrix into a sparse domain and then utilize this sparsity for accelerated reconstruction. Within this work, we investigate the Chebyshev transformation for matrix compression and show that it can provide better reconstruction results for high compression rates than the commonly applied Cosine transformation. By reducing the number of coefficients per matrix row to one, it is even possible to derive a direct reconstruction method that obviates the usage of iterative solvers.

Research Projects

Hardware and Instrumentation

Clinical Surveillance MPI Scanner for Stroke Units

Magnetic Field Characterization

MPI Image Reconstruction

MRI Image Reconstruction

Software

Spectral CT & HR-pQCT

Quicknavigation for

Completed projects

Actuation and Imaging of Magnetic Nanoparticles

Actuation and Imaging of Magnetic Nanoparticles

Members

Publications