Fully automated gridding reconstruction for non-Cartesian x-space magnetic particle imaging

Ozaslan, Ali Alper; Alacaoğlu, Ahmet; Demirel, Ömer Burak; Çukur, Tolga; Sarıtaş, Emine Ülkü

doi:10.1088/1361-6560/ab3525

Cited by 25 publications

(22 citation statements)

References 64 publications

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“…MPI is a new biomedical imaging modality that images the distribution of SPIONs. Some researchers adopt mathematical models to describe the physical process, like the equilibrium model [ 25 ] or its variations, the x-space approach [ 3 , 26 , 27 , 28 ], Chebyshev polynomials [ 29 ], and analytic inversion formulas [ 30 , 31 ]. The state-of-the-art reconstruction techniques adopt the experimentally calibrated forward operators, called the SM-based image reconstruction [ 32 , 33 ].…”

Section: The Theory Of Sm-based Mpimentioning

confidence: 99%

The Reconstruction of Magnetic Particle Imaging: Current Approaches Based on the System Matrix

et al. 2021

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Magnetic particle imaging (MPI) is a novel non-invasive molecular imaging technology that images the distribution of superparamagnetic iron oxide nanoparticles (SPIONs). It is not affected by imaging depth, with high sensitivity, high resolution, and no radiation. The MPI reconstruction with high precision and high quality is of enormous practical importance, and many studies have been conducted to improve the reconstruction accuracy and quality. MPI reconstruction based on the system matrix (SM) is an important part of MPI reconstruction. In this review, the principle of MPI, current construction methods of SM and the theory of SM-based MPI are discussed. For SM-based approaches, MPI reconstruction mainly has the following problems: the reconstruction problem is an inverse and ill-posed problem, the complex background signals seriously affect the reconstruction results, the field of view cannot cover the entire object, and the available 3D datasets are of relatively large volume. In this review, we compared and grouped different studies on the above issues, including SM-based MPI reconstruction based on the state-of-the-art Tikhonov regularization, SM-based MPI reconstruction based on the improved methods, SM-based MPI reconstruction methods to subtract the background signal, SM-based MPI reconstruction approaches to expand the spatial coverage, and matrix transformations to accelerate SM-based MPI reconstruction. In addition, the current phantoms and performance indicators used for SM-based reconstruction are listed. Finally, certain research suggestions for MPI reconstruction are proposed, expecting that this review will provide a certain reference for researchers in MPI reconstruction and will promote the future applications of MPI in clinical medicine.

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Section: The Theory Of Sm-based Mpimentioning

confidence: 99%

The Reconstruction of Magnetic Particle Imaging: Current Approaches Based on the System Matrix

et al. 2021

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“…erefore, the collected data need to be gridded before. e Jackson gridding algorithm is widely recognized [11] because of its simple calculation and less computing time, expressed as follows:…”

Section: Regriddingmentioning

confidence: 99%

Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

Xie

Bai

et al. 2021

Scientific Programming

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This study aimed to explore the application value of magnetic resonance imaging (MRI) images based on deep learning algorithms in the diagnosis of tibial plateau fractures combined with meniscus injuries. The original MRI image was input into the deep learning convolutional neural network (CNN), and the knee joint undersampled and fully sampled MRI image data were used for training to obtain a neural network model that can effectively remove the noise and blur of the undersampled image. Then, the image was reconstructed by the Regridding model to obtain an image with less noise and clearer structure. At the same time, all subjects underwent knee MRI examinations, and algorithms were used to analyze the sensitivity, specificity, and accuracy of their images. It was found that of 160 menisci from 80 cases of tibial plateau fractures, 64 were normal meniscus and 88 were injured menisci. The sensitivity, specificity, and accuracy of optimized MRI in diagnosing fracture of tibial plateau combined with meniscus injury were 96.9%, 93.2%, and 95.3%, respectively. In conclusion, the restored MRI images have high sensitivity in the diagnosis of meniscus injury and high consistency with the intraoperative results. It suggests that the optimized MRI image is effective in the diagnosis of meniscus injury.

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“…Taking advantage of the continuous signal acquisition of this trajectory, Harmonic Dispersion X-space (HD-X) reconstruction was utilized [34]. Since the data points in 2DTT were on a non-Cartesian grid, an automated gridding algorithm for non-Cartesian MPI reconstruction was utilized on the HD-X data [35]. This gridding algorithm automatically tuned all reconstruction parameters, including the grid size.…”

Section: Mpi Image Reconstructionmentioning

confidence: 99%

“…The estimated τ values were placed on the corresponding pFOV center locations to form a τ map. For 2DTT, the above-mentioned gridding algorithm was utilized to grid the τ values to a Cartesian grid [35]. In the background regions, τ can have unexpectedly high or low values due to low SNR.…”

Section: Mpi Image Reconstructionmentioning

confidence: 99%

Rapid TAURUS for Relaxation-Based Color Magnetic Particle Imaging

Arslan¹,

Ozaslan²,

Kurt³

et al. 2021

Preprint

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Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality that exploits the non-linear response of magnetic nanoparticles (MNPs). Color MPI widens the functionality of MPI, empowering it with the capability to distinguish different MNPs and/or MNP environments. The system function approach for color MPI relies on extensive calibrations that capture the differences in the harmonic responses of the MNPs. An alternative calibration-free x-space-based method called TAURUS estimates a map of the relaxation time constant, τ , by recovering the underlying mirror symmetry in the MPI signal. However, TAURUS requires a back and forth scanning of a given region, restricting its usage to slow trajectories with constant or piecewise constant focus fields (FFs). In this work, we propose a novel technique to increase the performance of TAURUS and enable τ map estimation for rapid and multi-dimensional trajectories. The proposed technique is based on correcting the distortions on mirror symmetry induced by time-varying FFs. We demonstrate via simulations and experiments in our in-house MPI scanner that the proposed method successfully estimates high-fidelity τ maps for rapid trajectories that provide orders of magnitude reduction in scanning time, while preserving the calibration-free property of TAURUS.

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Fully automated gridding reconstruction for non-Cartesian x-space magnetic particle imaging

Cited by 25 publications

References 64 publications

The Reconstruction of Magnetic Particle Imaging: Current Approaches Based on the System Matrix

The Reconstruction of Magnetic Particle Imaging: Current Approaches Based on the System Matrix

Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

Rapid TAURUS for Relaxation-Based Color Magnetic Particle Imaging

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