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      TranSMS: transformers for super-resolution calibration in magnetic particle imaging

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      Author(s)
      Gungor, Alper
      Askin, Baris
      Soydan, D.A.
      Saritas, Emine Ulku
      Top, C. B.
      Çukur, Tolga
      Date
      2022-07-11
      Source Title
      IEEE Transactions on Medical Imaging
      Print ISSN
      0278-0062
      Electronic ISSN
      1558-254X
      Publisher
      Institute of Electrical and Electronics Engineers Inc.
      Volume
      41
      Issue
      12
      Pages
      3562 - 3574
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging
      Keywords
      Magnetic particle imaging
      Calibration
      System matrix
      Reconstruction
      Transformer
      Deep learning
      Permalink
      http://hdl.handle.net/11693/111402
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/TMI.2022.3189693
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