Deep Learned Super Resolution of System Matrices for Magnetic Particle Imaging

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

doi:10.1109/embc46164.2021.9630601

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Super-resolving HR SM in MPI is closely related to the single-image super-resolution (SISR) problem in computer vision [13], [22], [25]. DL methods have recently become the gold standard in this domain.…”

Section: Related Workmentioning

confidence: 99%

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TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging

Güngör

Askin

Soydan

et al. 2022

IEEE Trans. Med. Imaging

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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.

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Section: Related Workmentioning

confidence: 99%

“…Thus, pixel intensities in each SM intrinsically show long-range correlations. Prior SR methods are predominantly based on convolutional neural networks (CNNs) that improve performance over traditional methods [22], [25]. However, due to their local kernels, CNNs cannot capture the long-range contextual features in SM rows.…”

mentioning

confidence: 99%

TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging

Güngör

Askin

Soydan

et al. 2022

IEEE Trans. Med. Imaging

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MPIGAN: An end‐to‐end deep based generative framework for high‐resolution magnetic particle imaging reconstruction

Zhao,

Shen,

Liu

et al. 2024

Medical Physics

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BackgroundMagnetic particle imaging (MPI) is a recently developed, non‐invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X‐space are two commonly used MPI reconstruction methods, where the former is extremely time‐consuming and the latter usually produces blurry images.PurposeCurrently, we proposed an end‐to‐end machine learning framework to reconstruct high‐resolution MPI images from 1‐D voltage signals directly and efficiently.MethodsThe proposed framework, which we termed “MPIGAN”, was trained on a large MPI simulation dataset containing 291 597 pairs of high‐resolution 2‐D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high‐resolution MPI image reconstruction.ResultsExperiment results showed that, MPIGAN exhibited remarkable abilities in high‐resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X‐space in recovering the fine‐scale structure of magnetic nanoparticles’ spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high‐quality MPI images.ConclusionOur study provides a promising AI solution for end‐to‐end, efficient, and high‐resolution magnetic particle imaging reconstruction.

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Cross-domain knowledge transfer based parallel-cascaded multi-scale attention network for limited view reconstruction in projection magnetic particle imaging

Gao

Zhang

et al. 2023

Computers in Biology and Medicine

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Deep Learned Super Resolution of System Matrices for Magnetic Particle Imaging

Cited by 6 publications

References 14 publications

TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging

TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging

MPIGAN: An end‐to‐end deep based generative framework for high‐resolution magnetic particle imaging reconstruction

Cross-domain knowledge transfer based parallel-cascaded multi-scale attention network for limited view reconstruction in projection magnetic particle imaging

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