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Fast System Calibration With Coded Calibration Scenes for Magnetic Particle Imaging.

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Title: Fast System Calibration With Coded Calibration Scenes for Magnetic Particle Imaging.
Authors: Ilbey, Serhat1 (AUTHOR) serhatilbey@gmail.com, Top, Can Baris1 (AUTHOR), Gungor, Alper1 (AUTHOR), Cukur, Tolga2 (AUTHOR), Saritas, Emine Ulku2 (AUTHOR), Guven, H. Emre1 (AUTHOR)
Source: IEEE Transactions on Medical Imaging. Sep2019, Vol. 38 Issue 9, p2070-2080. 11p.
Subjects: Magnetic particle imaging, Calibration, Image reconstruction, Compressed sensing, Magnetic nanoparticles, Signal-to-noise ratio
Abstract: Magnetic particle imaging (MPI) is a relatively new medical imaging modality, which detects the nonlinear response of magnetic nanoparticles (MNPs) that are exposed to external magnetic fields. The system matrix (SM) method for MPI image reconstruction requires a time consuming system calibration scan prior to image acquisition, where a single MNP sample is measured at each voxel position in the field-of-view (FOV). The scanned sample has the maximum size of a voxel so that the calibration measurements have relatively poor signal-to-noise ratio (SNR). In this paper, we present the coded calibration scene (CCS) framework, where we place multiple MNP samples inside the FOV in a random or pseudo-random fashion. Taking advantage of the sparsity of the SM, we reconstruct the SM by solving a convex optimization problem with alternating direction method of multipliers using CCS measurements. We analyze the effects of filling rate, number of measurements, and SNR on the SM reconstruction using simulations and demonstrate different implementations of CCS for practical realization. We also compare the imaging performance of the proposed framework with that of a standard compressed sensing SM reconstruction that utilizes a subset of calibration measurements from a single MNP sample. The results show that CCS significantly reduces calibration time while increasing both the SM reconstruction and image reconstruction performances. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Medical Imaging is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: Fast System Calibration With Coded Calibration Scenes for Magnetic Particle Imaging.
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  Data: <searchLink fieldCode="DE" term="%22Magnetic+particle+imaging%22">Magnetic particle imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Compressed+sensing%22">Compressed sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+nanoparticles%22">Magnetic nanoparticles</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink>
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  Data: Magnetic particle imaging (MPI) is a relatively new medical imaging modality, which detects the nonlinear response of magnetic nanoparticles (MNPs) that are exposed to external magnetic fields. The system matrix (SM) method for MPI image reconstruction requires a time consuming system calibration scan prior to image acquisition, where a single MNP sample is measured at each voxel position in the field-of-view (FOV). The scanned sample has the maximum size of a voxel so that the calibration measurements have relatively poor signal-to-noise ratio (SNR). In this paper, we present the coded calibration scene (CCS) framework, where we place multiple MNP samples inside the FOV in a random or pseudo-random fashion. Taking advantage of the sparsity of the SM, we reconstruct the SM by solving a convex optimization problem with alternating direction method of multipliers using CCS measurements. We analyze the effects of filling rate, number of measurements, and SNR on the SM reconstruction using simulations and demonstrate different implementations of CCS for practical realization. We also compare the imaging performance of the proposed framework with that of a standard compressed sensing SM reconstruction that utilizes a subset of calibration measurements from a single MNP sample. The results show that CCS significantly reduces calibration time while increasing both the SM reconstruction and image reconstruction performances. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of IEEE Transactions on Medical Imaging is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1109/TMI.2019.2896289
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 2070
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      – SubjectFull: Magnetic particle imaging
        Type: general
      – SubjectFull: Calibration
        Type: general
      – SubjectFull: Image reconstruction
        Type: general
      – SubjectFull: Compressed sensing
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      – SubjectFull: Magnetic nanoparticles
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      – SubjectFull: Signal-to-noise ratio
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    Titles:
      – TitleFull: Fast System Calibration With Coded Calibration Scenes for Magnetic Particle Imaging.
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            NameFull: Ilbey, Serhat
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            NameFull: Top, Can Baris
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              M: 09
              Text: Sep2019
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              Y: 2019
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              Value: 38
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