Fast reconstruction in magnetic particle imaging

Lampe, Jörg; Bassoy, C; Rahmer, Jürgen; Weizenecker, J.; Voß, Heinrich; Gleich, Bernhard; Borgert, J.

doi:10.1088/0031-9155/57/4/1113

Cited by 71 publications

(54 citation statements)

References 24 publications

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“…Tools for an objective evaluation do not exist yet and have to be developed. The reconstruction process itself can be accelerated by applying compression techniques [16]. We conclude that with this first experiment a combined workflow process between a preclinical MRI system and the first commercially available preclinical MPI scanner has been demonstrated to be feasible.…”

mentioning

confidence: 82%

Combined Preclinical Magnetic Particle Imaging and Magnetic Resonance Imaging: Initial Results in Mice

Kaul

Weber

Heinen

et al. 2015

Fortschr Röntgenstr

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Purpose: Magnetic particle imaging (MPI) is a new radiologic imaging modality. For the first time, a commercial preclinical scanner is installed. The goal of this study was to establish a workflow between MPI and magnetic resonance imaging (MRI) scanners for a complete in vivo examination of a mouse and to generate the first co-registered in vivo MR-MP images. Materials and Methods: The in vivo examination of five mice were performed on a preclinical MPI scanner and a 7 Tesla preclinical MRI system. MRI measurements were used for anatomical referencing and validation of the injection of superparamagnetic iron oxide (SPIO) particles during a dynamic MPI scan. We extracted MPI data of the injection phase and co-registered it with MRI data. Results: A workflow process for a combined in vivo MRI and MPI examination was established. A successful injection of ferucarbotran was proven in MPI and MRI. MR-MPI co-registration allocated the SPIOs in the inferior vena cava and the heart during and shortly after the injection. Conclusion: The acquisition of preclinical MPI and MRI data is feasible and allows the combined analysis of MR-MPI information. Key Points: ??The first commercial preclinical MPI system was installed and has been tested successfully in an in vivo experiment. ??This MPI system can detect SPIO in vivo in a mouse model. ??First time performance of a combined in vivo MPI-MRI examination. ??Co-registered MR-MPI allows high temporal resolution imaging of the vascular SPIO distribution. Citation Format: ??Kaul MG., Weber O, Heinen U et?al. Combined Preclinical Magnetic Particle Imaging and Magnetic Resonance Imaging: Initial Results in a Mouse. Fortschr R?ntgenstr 2015; 187: 347???352

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mentioning

confidence: 82%

Combined Preclinical Magnetic Particle Imaging and Magnetic Resonance Imaging: Initial Results in Mice

Kaul

Weber

Heinen

et al. 2015

Fortschr Röntgenstr

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“…Therefore, the first condition is obtained with the sparsification of the MPI system matrix using a basis transformation, as the discrete Fourier transform (DFT) or the discrete cosine transform (DCT), while the second one is assured by an appropriate distribution of the sampling positions (Lampe et al 2012). Practically, there exist different types of random sensing matrices such as Bernoulli, Gaussian or Subgaussian: they all guarantee the low coherence of the system function matrix.…”

Section: Measurement-based Methodsmentioning

confidence: 99%

Hybrid x-space: a new approach for MPI reconstruction

et al. 2016

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Magnetic particle imaging (MPI) is a new medical imaging technique capable of recovering the distribution of superparamagnetic particles from their measured induced signals. In literature there are two main MPI reconstruction techniques: measurement-based (MB) and x-space (XS). The MB method is expensive because it requires a long calibration procedure as well as a reconstruction phase that can be numerically costly. On the other side, the XS method is simpler than MB but the exact knowledge of the field free point (FFP) motion is essential for its implementation. Our simulation work focuses on the implementation of a new approach for MPI reconstruction: it is called hybrid x-space (HXS), representing a combination of the previous methods. Specifically, our approach is based on XS reconstruction because it requires the knowledge of the FFP position and velocity at each time instant. The difference with respect to the original XS formulation is how the FFP velocity is computed: we estimate it from the experimental measurements of the calibration scans, typical of the MB approach. Moreover, a compressive sensing technique is applied in order to reduce the calibration time, setting a fewer number of sampling positions. Simulations highlight that HXS and XS methods give similar results. Furthermore, an appropriate use of compressive sensing is crucial for obtaining a good balance between time reduction and reconstructed image quality. Our proposal is suitable for open geometry configurations of human size devices, where incidental factors could make the currents, the fields and the FFP trajectory irregular.

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“…Due to the reasons that white noise can be assumed [2] and system function components are well-compressible with the DFT and DCT [5], frequency domain filters are chosen. Such filters exploit the fact that white noise, in contrast to the true signal, is not compressible by these basis transformations.…”

Section: Denoisingmentioning

confidence: 99%

“…In this paper, a method to denoise the system matrix is proposed in order to improve the image reconstruction. As the system function components are wellcompressible with certain basis transformations, such as the discrete Fourier transformation (DFT) or discrete Cosine transformation (DCT) [5], frequency domain filters are used for denoising the system function components. Hereby, the system function components are transformed in another basis representation in order to locate the signal information and separate it from noise.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction Enhancement by Denoising the Magnetic Particle Imaging System Matrix Using Frequency Domain Filter

et al. 2015

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Magnetic particle imaging is a new modality, which allows the determination of the spatial distribution of magnetic nanoparticles in-vivo. A standard approach for magnetic particle image reconstruction employs a so-called system matrix. The system matrix is typically acquired by a calibration measurement, whereby the system response at numerous positions in the field-of-view is measured. Due to the measurement process, the system matrix contains noise, which affects the reconstruction of the particle distribution. In this paper, the special structure of the system matrix is exploited for noise reduction by applying frequency domain filters. It is shown that image reconstruction with the denoised system matrix yields an improved resolution and a better signal-to-noise ratio.Index Terms-Denoising, frequency domain filter, image reconstruction, magnetic particle imaging (MPI).

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Fast reconstruction in magnetic particle imaging

Cited by 71 publications

References 24 publications

Combined Preclinical Magnetic Particle Imaging and Magnetic Resonance Imaging: Initial Results in Mice

Combined Preclinical Magnetic Particle Imaging and Magnetic Resonance Imaging: Initial Results in Mice

Hybrid x-space: a new approach for MPI reconstruction

Reconstruction Enhancement by Denoising the Magnetic Particle Imaging System Matrix Using Frequency Domain Filter

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