International Journal on Magnetic Particle Imaging IJMPI
Vol. 12 No. 1 Suppl 1 (2026): Int J Mag Part Imag
https://doi.org/10.18416/IJMPI.2026.2604004

Proceedings Articles, ID 1010

CVU-SM: Complex-Valued U-Shaped Network for System Matrix Fast Calibration

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Hangyu Zhong (School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China), Shijie Sun (1) School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China; 2) Hangzhou International Innovation Institute, Beihang University, Hangzhou, China), Lijun Xu (School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China), Jing Zhong (Beihang University)

Copyright (c) 2026 Hangyu Zhong, Shijie Sun, Lijun Xu, Jing Zhong

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Magnetic Particle Imaging (MPI) faces a major bottleneck in the lengthy calibration required for system matrix acquisition. To address this, a complex-valued neural network-based super-resolution framework, CVU-SM, is proposed. Built on a U-shaped encoder-decoder architecture with complex-valued residual-in-residual dense blocks, CVU-SM preserves both magnitude and phase information to accurately reconstruct high-resolution system matrices from low-resolution inputs. Evaluated on the open source dataset, it outperforms existing deep learning methods in system matrix quality and downstream image reconstruction, demonstrating strong generalization, thereby enabling fast, high-quality MPI calibration.

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