A CNN Based Super-Resolution Technique for Magnetic Particle Imaging System Matrix

Askin, Baris; Güngör, Alper; Soydan, Damla Alptekin; Top, Can Barış; Çukur, Tolga

doi:10.1109/siu53274.2021.9477772

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Notably, several deep learning algorithms have been also reported to improve the performance of MPI. A CNN-based super-resolution technique was proposed in Aşkın et al (2021) to obtain the HR system matrix from the LR system matrix of MPI. Baltruschat et al (2020) used deep learning to restore the small-size system matrix to large-size system matrix, which saved the time of calibration of the system matrix.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for improving the spatial resolution of magnetic particle imaging

Hui

Tian

2022

Phys. Med. Biol.

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Magnetic particle imaging (MPI) is a new medical non-destructive imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs). In MPI, spatial resolution is an important indicator of efficiency; traditional techniques for improving the spatial resolution may result in higher costs, lower sensitivity, or reduced contrast. Therefore, we propose a deep learning approach to improve the spatial resolution of MPI by fusing a dual-sampling convolutional neural network (FDS-MPI). An end-to-end model is established to generate high-spatial-resolution images from low-spatial-resolution images, avoiding the aforementioned shortcomings. Further, we evaluate the performance of the proposed FDS-MPI model through simulation and phantom experiments. The results demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two. This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.

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Section: Introductionmentioning

confidence: 99%