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Gungor A, Askin B, Soydan DA, Saritas EU, Top CB, Cukur T. TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:3562-3574. [PMID: 35816533 DOI: 10.1109/tmi.2022.3189693] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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.
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Mohn F, Knopp T, Boberg M, Thieben F, Szwargulski P, Graeser M. System Matrix Based Reconstruction for Pulsed Sequences in Magnetic Particle Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1862-1873. [PMID: 35130154 DOI: 10.1109/tmi.2022.3149583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Improving resolution and sensitivity will widen possible medical applications of magnetic particle imaging. Pulsed excitation promises such benefits, at the cost of more complex hardware solutions and restrictions on drive field amplitude and frequency. State-of-the-art systems utilize a sinusoidal excitation to drive superparamagnetic nanoparticles into the non-linear part of their magnetization curve, which creates a spectrum with a clear separation of direct feed-through and higher harmonics caused by the particles response. One challenge for rectangular excitation is the discrimination of particle and excitation signals, both broad-band. Another is the drive-field sequence itself, as particles that are not placed at the same spatial position, may react simultaneously and are not separable by their signal phase or shape. To overcome this potential loss of information in spatial encoding for high amplitudes, a superposition of shifting fields and drive-field rotations is proposed in this work. Upon close view, a system matrix approach is capable to maintain resolution, independent of the sequence, if the response to pulsed sequences still encodes information within the phase. Data from an Arbitrary Waveform Magnetic Particle Spectrometer with offsets in two spatial dimensions is measured and calibrated to guarantee device independence. Multiple sequence types and waveforms are compared, based on frequency space image reconstruction from emulated signals, that are derived from measured particle responses. A resolution of 1.0 mT (0.8 mm for a gradient of (-1.25,-1.25,2.5) Tm-1) in x- and y-direction was achieved and a superior sensitivity for pulsed sequences was detected on the basis of reference phantoms.
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Pantke D, Mueller F, Reinartz S, Philipps J, Mohammadali Dadfar S, Peters M, Franke J, Schrank F, Kiessling F, Schulz V. Frequency-selective signal enhancement by a passive dual coil resonator for magnetic particle imaging. Phys Med Biol 2022; 67. [PMID: 35472698 DOI: 10.1088/1361-6560/ac6a9f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 04/26/2022] [Indexed: 11/12/2022]
Abstract
Objective.Magnetic particle imaging (MPI) visualizes the spatial distribution of magnetic nanoparticles. MPI already provides excellent temporal and good spatial resolution, however, to achieve translation into clinics, further advances in the fields of sensitivity, image reconstruction and tracer performance are needed. In this work, we propose a novel concept to enhance the MPI signal and image resolution by a purely passive receive coil insert for a preclinical MPI system.Approach.The passive dual coil resonator (pDCR) provides frequency-selective signal enhancement. This is enabled by the adaptable resonance frequency of the pDCR network, which is galvanically isolated from the MPI system and composed of two coaxial solenoids connected via a capacitor. The pDCR aims to enhance frequency components related to high mixing orders, which are crucial to achieve high spatial resolution.Main Results.In this study, system matrix measurements and image acquisitions of a resolution phantom are carried out to evaluate the performance of the pDCR compared to the integrated receive unit of the preclinical MPI and a dedicated rat-sized receive coil. Frequency-selective signal increase and spatial resolution enhancement are demonstrated.Significance.Common dedicated receive coils come along with noise-matched receive networks, which makes them costly and difficult to reproduce. The presented pDCR is a purely passive coil insert that gets along without any additional receive electronics. Therefore, it is cost-efficient, easy-to-handle and adaptable to other MPI scanners and potentially other applications providing the basis for a new breed of passive MPI receiver systems.
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Affiliation(s)
- Dennis Pantke
- Department of Physics of Molecular Imaging, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Florian Mueller
- Department of Physics of Molecular Imaging, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Sebastian Reinartz
- Department of Diagnostic and Interventional Radiology, Uniklinik RWTH Aachen, Aachen, Germany
| | - Jonas Philipps
- Department of Physics of Molecular Imaging, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Seyed Mohammadali Dadfar
- Institute for Experimental Molecular Imaging, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Maximilian Peters
- Department of Physics of Molecular Imaging, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Jochen Franke
- Department of Physics of Molecular Imaging, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Bruker BioSpin MRI GmbH, Preclinical Imaging Division, Ettlingen, Germany
| | - Franziska Schrank
- Department of Physics of Molecular Imaging, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Fabian Kiessling
- Institute for Experimental Molecular Imaging, Medical Faculty, RWTH Aachen University, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Volkmar Schulz
- Department of Physics of Molecular Imaging, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.,III. Physikalisches Institut B, RWTH Aachen University, Aachen, Germany
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Knopp T, Grosser M, Graeser M, Gerkmann T, Moddel M. Efficient Joint Estimation of Tracer Distribution and Background Signals in Magnetic Particle Imaging Using a Dictionary Approach. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3568-3579. [PMID: 34152980 DOI: 10.1109/tmi.2021.3090928] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Background signals are a primary source of artifacts in magnetic particle imaging and limit the sensitivity of the method since background signals are often not precisely known and vary over time. The state-of-the art method for handling background signals uses one or several background calibration measurements with an empty scanner bore and subtracts a linear combination of these background measurements from the actual particle measurement. This approach yields satisfying results in case that the background measurements are taken in close proximity to the particle measurement and when the background signal drifts linearly. In this work, we propose a joint estimation of particle distribution and background signal based on a dictionary that is capable of representing typical background signals. Reconstruction is performed frame-by-frame with minimal assumptions on the temporal evolution of background signals. Thus, even non-linear temporal evolution of the latter can be captured. Using a singular-value decomposition, the dictionary is derived from a large number of background calibration scans that do not need to be recorded in close proximity to the particle measurement. The dictionary is sufficiently expressive and represented by its principle components. The proposed joint estimation of particle distribution and background signal is expressed as a linear Tikhonov-regularized least squares problem, which can be efficiently solved. In phantom experiments it is shown that the method strongly suppresses background artifacts and even allows to estimate and remove the direct feed-through of the excitation field.
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Gungor A, Askin B, Soydan DA, Baris Top C, Cukur T. Deep Learned Super Resolution of System Matrices for Magnetic Particle Imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3749-3752. [PMID: 34892051 DOI: 10.1109/embc46164.2021.9630601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Magnetic Particle Imaging (MPI) is a new imaging technique that allows high resolution & high frame-rate imaging of magnetic nanoparticles (MNP). It relies on the nonlinear response of MNPs under a magnetic field. The imaging process can be modeled linearly, and then image reconstruction can be case as an inverse problem using a measured system matrix (SM). However, this calibration measurement is time consuming so it reduces practicality. In this study, we proposed a novel method for accelerating the SM calibration based on joint super-resolution (SR) and denoising of sensitivty maps (i.e., rows of SM). The proposed method is based on a deep convolutional neural network (CNN) architecture with residual-dense blocks. Model training was performed using noisy SM measurements simulated for varying MNP size and gradient strengths. Comparisons were performed against conventional low-resolution SM calibration, noisy high-resolution SM calibration, and bicubic upsampling of low-resolution SM. We show that the proposed method improves high-resolution SM recovery, and in turn leads to improved resolution and quality in subsequently reconstructed MPI images.
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Lieb F, Knopp T. A wavelet-based sparse row-action method for image reconstruction in magnetic particle imaging. Med Phys 2021; 48:3893-3903. [PMID: 33982810 DOI: 10.1002/mp.14938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 02/19/2021] [Accepted: 04/24/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Magnetic particle imaging (MPI) is a preclinical imaging technique capable of visualizing the spatio-temporal distribution of magnetic nanoparticles. The image reconstruction of this fast and dynamic process relies on efficiently solving an ill-posed inverse problem. Current approaches to reconstruct the tracer concentration from its measurements are either adapted to image characteristics of MPI but suffer from higher computational complexity and slower convergence or are fast but lack in the image quality of the reconstructed images. METHODS In this work we propose a novel MPI reconstruction method to combine the advantages of both approaches into a single algorithm. The underlying sparsity prior is based on an undecimated wavelet transform and is integrated into a fast row-action framework to solve the corresponding MPI minimization problem. RESULTS Its performance is numerically evaluated against a classical FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) approach on simulated and real MPI data. The experimental results show that the proposed method increases image quality with significantly reduced computation times. CONCLUSIONS In comparison to state-of-the-art MPI reconstruction methods, our approach shows better reconstruction results and at the same time accelerates the convergence rate of the underlying row-action algorithm.
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Affiliation(s)
- Florian Lieb
- Department of Computer Science, TH Aschaffenburg, Aschaffenburg, 63741, Germany
| | - Tobias Knopp
- Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf and Institute for Biomedical Imaging, Hamburg University of Technology, Germany
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Cao N, Miao X, Zhang J. Spatial intelligent decision system based on multidimensional network theory. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
With the development of information technology and the reduction of storage equipment costs, a large number of databases have been able to create and store massive amounts of data. How to use this data to provide guidance and advice for business decisions is a difficult problem that decision analysis systems need to solve. This paper designs a new multi-dimensional heterogeneous network information model, defines the binary relational meta-path in the heterogeneous network and the n-ary relational meta-path, and studies the relationship of these meta-paths as a new way to guide network aggregation. An intelligent emergency decision support system based on the GIS platform and the concept of the plan library was established. The system adopts the method of artificial intelligence and GIS technology to complete the management, analysis and processing of map space data, and realizes the rapid and automatic generation of emergency decision-making. Finally, through experiments on large-scale real and simulated data, it is verified that the system can effectively and efficiently analyze large-scale multi-dimensional heterogeneous networks.
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Affiliation(s)
- Ning Cao
- School of Management, Northwestern Polytechnical University, Xi’an, China
| | - Xiaoming Miao
- School of Management, Northwestern Polytechnical University, Xi’an, China
| | - Juan Zhang
- School of Management, Northwestern Polytechnical University, Xi’an, China
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Wegner F, von Gladiss A, Haegele J, Grzyska U, Sieren MM, Stahlberg E, Oechtering TH, Lüdtke-Buzug K, Barkhausen J, Buzug TM, Friedrich T. Magnetic Particle Imaging: In vitro Signal Analysis and Lumen Quantification of 21 Endovascular Stents. Int J Nanomedicine 2021; 16:213-221. [PMID: 33469281 PMCID: PMC7810673 DOI: 10.2147/ijn.s284694] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/11/2020] [Indexed: 11/23/2022] Open
Abstract
Purpose Endovascular stents are medical devices, which are implanted in stenosed blood vessels to ensure sufficient blood flow. Due to a high rate of in-stent re-stenoses, there is the need of a noninvasive imaging method for the early detection of stent occlusion. The evaluation of the stent lumen with computed tomography (CT) and magnetic resonance imaging (MRI) is limited by material-induced artifacts. The purpose of this work is to investigate the potential of the tracer-based modality magnetic particle imaging (MPI) for stent lumen visualization and quantification. Methods In this in vitro study, 21 endovascular stents were investigated in a preclinical MPI scanner. Therefore, the stents were implanted in vessel phantoms. For the signal analysis, the phantoms were scanned without tracer material, and the signal-to-noise-ratio was analyzed. For the evaluation of potential artifacts and the lumen quantification, the phantoms were filled with diluted tracer agent. To calculate the stent lumen diameter a calibrated threshold value was applied. Results We can show that it is possible to visualize the lumen of a variety of endovascular stents without material induced artifacts, as the stents do not generate sufficient signals in MPI. The stent lumen quantification showed a direct correlation between the calculated and nominal diameter (r = 0.98). Conclusion In contrast to MRI and CT, MPI is able to visualize and quantify stent lumina very accurately.
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Affiliation(s)
- Franz Wegner
- Department of Radiology and Nuclear Medicine, University of Lübeck, Lübeck, Germany
| | | | - Julian Haegele
- Department of Radiology and Nuclear Medicine, University of Lübeck, Lübeck, Germany.,Zentrum für Radiologie und Nuklearmedizin Rheinland, Dormagen, Germany
| | - Ulrike Grzyska
- Department of Radiology and Nuclear Medicine, University of Lübeck, Lübeck, Germany
| | - Malte Maria Sieren
- Department of Radiology and Nuclear Medicine, University of Lübeck, Lübeck, Germany
| | - Erik Stahlberg
- Department of Radiology and Nuclear Medicine, University of Lübeck, Lübeck, Germany
| | | | | | - Joerg Barkhausen
- Department of Radiology and Nuclear Medicine, University of Lübeck, Lübeck, Germany
| | - Thorsten M Buzug
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany.,Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering, Lübeck, Germany
| | - Thomas Friedrich
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany.,Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering, Lübeck, Germany
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Graeser M, Ludewig P, Szwargulski P, Foerger F, Liebing T, Forkert ND, Thieben F, Magnus T, Knopp T. Design of a head coil for high resolution mouse brain perfusion imaging using magnetic particle imaging. Phys Med Biol 2020; 65:235007. [PMID: 33049723 DOI: 10.1088/1361-6560/abc09e] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Magnetic particle imaging (MPI) is a novel and versatile imaging modality developing toward human application. When up-scaling to human size, the sensitivity of the systems naturally drops as the coil sensitivity depends on the bore diameter. Thus, new methods to push the sensitivity limit further have to be investigated to cope for this loss. In this paper a dedicated surface coil for mice is developed, improving the sensitivity in cerebral imaging applications. Similar to magnetic resonance imaging the developed surface coil improves the sensitivity due to the closer vicinity to the region of interest. With the developed surface coil presented in this work, it is possible to image tracer samples containing only 896 pg[Formula: see text] and detect even small vessels and anatomical structures within a wild type mouse model. As current sensitivity measures require a tracer system a new method for determining a sensitivity measure without this requirement is presented and verified to enable comparison between MPI receiver systems.
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Affiliation(s)
- Matthias Graeser
- Section for Biomedical Imaging, Department of Diagnostic and Interventional Radiology and Nuclear Medicine at the University Medical Center Hamburg- Eppendorf, Hamburg, Germany. Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany
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Efficient hybrid 3D system calibration for magnetic particle imaging systems using a dedicated device. Sci Rep 2020; 10:18432. [PMID: 33116183 PMCID: PMC7595165 DOI: 10.1038/s41598-020-75122-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 10/06/2020] [Indexed: 11/09/2022] Open
Abstract
Image reconstruction in magnetic particle imaging is often performed using a system matrix based approach. The acquisition of a system matrix is a time-consuming calibration which may take several weeks and thus, is not feasible for a clinical device. Due to hardware characteristics of the receive chain, a system matrix may not even be used in similar devices but has to be acquired for each imager. In this work, a dedicated device is used for measuring a hybrid system matrix. It is shown that the measurement time of a 3D system matrix is reduced by 96%. The transfer function of the receive chains is measured, which allows the use of the same system matrix in multiple devices. Equivalent image reconstruction results are reached using the hybrid system matrix. Furthermore, the inhomogeneous sensitivity profile of receive coils is successfully applied to a hybrid system matrix. It is shown that each aspect of signal acquisition in magnetic particle imaging can be taken into account using hybrid system matrices. It is favourable to use a hybrid system matrix for image reconstruction in terms of measurement time, signal-to-noise ratio and discretisation.
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Paysen H, Kosch O, Wells J, Loewa N, Wiekhorst F. Characterization of noise and background signals in a magnetic particle imaging system. Phys Med Biol 2020; 65. [PMID: 33086200 DOI: 10.1088/1361-6560/abc364] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/21/2020] [Indexed: 11/11/2022]
Abstract
Magnetic Particle Imaging (MPI) is a novel technology, which opens new possibilities for promising biomedical applications. MPI uses magnetic fields to generate a specific response from magnetic nanoparticles (MNPs), to determine their spatial location non-invasively and without using ionizing radiation. One open challenge of MPI is to achieve further improvements in terms of sensitivity to translate the currently preclinical performed research into clinical applications. In this work, we study the noise and background signals of our preclinical MPI system, to identify and characterize disturbing signal contributions. The current limit of detection achieved with our device was determined previously to be 20 ng of iron. Based on the results presented in this work, we describe possible hardware and software improvements and estimate that the limit of detection could be lowered to about 200-400 pg. Additionally, a long-term analysis of the scanner performance over the last three years is presented, which proved to be an easy and effective way to monitor possible changes or damage of hardware components. All the presented results were obtained by analysing empty scanner measurements and the presented methodology can easily be adapted for different scanner types, to compare their performances.
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Affiliation(s)
- Hendrik Paysen
- 8.2 Biosignals, Physikalisch-Technische Bundesanstalt in Berlin, Berlin, GERMANY
| | - Olaf Kosch
- 8.2 Biosignals, Physikalisch-Technische Bundesanstalt in Berlin, Berlin, Berlin, GERMANY
| | - James Wells
- 8.2 Biosignals, Physikalisch-Technische Bundesanstalt, Berlin, Berlin, GERMANY
| | - Norbert Loewa
- 8.2 Biosignals, Physikalisch-Technische Bundesanstalt, Berlin, Berlin, GERMANY
| | - Frank Wiekhorst
- 8.2 Biosignals, Physikalisch - Technische Bundesanstalt, Berlin, GERMANY
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Magnetic Particle Imaging: Artifact-Free Metallic Stent Lumen Imaging in a Phantom Study. Cardiovasc Intervent Radiol 2019; 43:331-338. [PMID: 31578634 DOI: 10.1007/s00270-019-02347-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 09/24/2019] [Indexed: 12/30/2022]
Abstract
PURPOSE To illustrate the potential of magnetic particle imaging (MPI) for stent lumen imaging in comparison with clinical computed tomography (CT) and magnetic resonance imaging (MRI). MATERIALS AND METHODS Imaging of eight tracer-filled, stented vessel phantoms and a tracer-filled, non-stented reference phantom for each diameter was performed on a preclinical MPI scanner: eight commercially available coronary stents of different dimensions (diameter: 3-4 mm; length: 11-38 mm) and materials (stainless steel, platinum-chromium) were implanted into silicone vessel phantoms. For comparison, all vessel phantoms were also visualized by MRI and CT. Two radiologists assessed the images regarding stent-induced artifacts using a 5-point grading scale. RESULTS The visualization of all stented vessel phantoms was achieved without stent-induced artifacts with MPI. In contrast, MRI and CT images revealed multiform stent-induced artifacts. CONCLUSION Given its clinical introduction, MPI has the potential to overcome the disadvantages of MRI and CT concerning the visualization of the stent lumen.
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Ilbey S, Top CB, Gungor A, Cukur T, Saritas EU, Guven HE. Fast System Calibration With Coded Calibration Scenes for Magnetic Particle Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2070-2080. [PMID: 30714915 DOI: 10.1109/tmi.2019.2896289] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Magnetic particle imaging (MPI) is a relatively new medical imaging modality, which detects the nonlinear response of magnetic nanoparticles (MNPs) that are exposed to external magnetic fields. The system matrix (SM) method for MPI image reconstruction requires a time consuming system calibration scan prior to image acquisition, where a single MNP sample is measured at each voxel position in the field-of-view (FOV). The scanned sample has the maximum size of a voxel so that the calibration measurements have relatively poor signal-to-noise ratio (SNR). In this paper, we present the coded calibration scene (CCS) framework, where we place multiple MNP samples inside the FOV in a random or pseudo-random fashion. Taking advantage of the sparsity of the SM, we reconstruct the SM by solving a convex optimization problem with alternating direction method of multipliers using CCS measurements. We analyze the effects of filling rate, number of measurements, and SNR on the SM reconstruction using simulations and demonstrate different implementations of CCS for practical realization. We also compare the imaging performance of the proposed framework with that of a standard compressed sensing SM reconstruction that utilizes a subset of calibration measurements from a single MNP sample. The results show that CCS significantly reduces calibration time while increasing both the SM reconstruction and image reconstruction performances.
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Knopp T, Gdaniec N, Rehr R, Graeser M, Gerkmann T. Correction of linear system drifts in magnetic particle imaging. ACTA ACUST UNITED AC 2019; 64:125013. [DOI: 10.1088/1361-6560/ab2480] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Szwargulski P, Moddel M, Gdaniec N, Knopp T. Efficient Joint Image Reconstruction of Multi-Patch Data Reusing a Single System Matrix in Magnetic Particle Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:932-944. [PMID: 30334751 DOI: 10.1109/tmi.2018.2875829] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Due to peripheral nerve stimulation, the magnetic particle imaging (MPI) method is limited in the maximum applicable excitation-field amplitude. This in turn leads to a limitation of the size of the covered field of view (FoV) to few millimeters. In order to still capture a larger FoV, MPI is capable to rapidly acquire volumes in a multi-patch fashion. To this end, the small excitation volume is shifted through space using the magnetic focus fields. Recently, it has been shown that the individual patches are preferably reconstructed in a joint fashion by solving a single linear system of equations taking the coupling between individual patches into account. While this improves the image quality, it is computationally and memory demanding since the size of the linear system increases in the best case quadratically with the number of patches. In this paper, we will develop a reconstruction algorithm for MPI multi-patch data exploiting the sparsity of the joint system matrix. A highly efficient implicit matrix format allows for rapid on-the-fly calculations of linear algebra operations involving the system matrix. Using this approach, the computational effort can be reduced to a linear dependence on the number of used patches. The algorithm is validated on 3-D multi-patch phantom data sets and shown to reconstruct large data sets with 15 patches in less than 22 s.
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Top CB, Güngör A, Ilbey S, Güven HE. Trajectory analysis for field free line magnetic particle imaging. Med Phys 2019; 46:1592-1607. [PMID: 30695100 DOI: 10.1002/mp.13411] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/13/2019] [Accepted: 01/15/2019] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Magnetic particle imaging (MPI) is a relatively new method to image the spatial distribution of magnetic nanoparticle (MNP) tracers administered to the body with high spatial and temporal resolution using an inhomogeneous magnetic field. The spatial information of the MNP's is encoded using a field free point (FFP), or a field free line (FFL), in which the magnetic field vanishes at a point, or on a line, respectively. FFL scanning has the advantage of improved sensitivity compared to FFP scanning as a result of higher signal-to-noise ratio. The trajectory traversed by the FFL or FFP is an important parameter of the MPI system and should be selected to achieve the best imaging quality in minimum scan time, while considering hardware constraints and patient safety. In this study, we analyzed the image quality of different FFL trajectories for a large field of view (FOV) using simulations, to provide a baseline information for FFL scanning MPI system design. METHODS We simulated a human-sized FFL scanning MPI configuration to image a circular FOV with 160 mm diameter, and compared Radial, Spiral, Uniform Spiral, Flower, and Lissajous trajectories with different trajectory densities scanned by the FFL for constant scan time. We analyzed the system matrices of the trajectories in terms of mutual coherence and homogeneity of the spatial sensitivity. We calculated the maximum electric fields induced on a homogeneous conductive body by the selection field (SF) and the focus field (FF) to compare the trajectories based on the nerve stimulation threshold. The images were obtained using the system matrix reconstruction approach with two different image reconstruction methods. In the first one, we used the conventional image reconstruction method, algebraic reconstruction technique (ART), which gives a regularized least-squares solution. In the second one, we used the state-of-the-art alternating direction method of multipliers (ADMM), which minimizes a weighted sum of the l1 -norm and the total variation (TV) of the images. RESULTS The Radial and Spiral trajectories resulted in a poor imaging performance at low trajectory densities due to relatively high coherency and poor sensitivity of the measurements, respectively. For ART reconstruction, the highest image quality with the lowest trajectory density was achieved with the Uniform Spiral trajectory. Uniform Spiral, Flower, and Lissajous trajectories yielded comparable performance with ADMM reconstruction. The rotating SF induced higher electric field amplitude compared to the FF. Consequently, maximum allowable gradient at the same trajectory density was greater for the Radial trajectory compared to the other trajectories. CONCLUSIONS For a large FOV coverage, the Uniform Spiral trajectory offers a good compromise between image quality and imaging time, taking safety and hardware limitations into account. The Radial trajectory, especially using l1 -norm and TV priors in the reconstruction, may be favorable in case the SF induced electric field is higher than that of the FF at the same frequency (e.g., relatively small FOV coverage). In general, ADMM reconstruction resulted in higher contrast and resolution compared to ART, leading to lighter requirements on the density of the trajectory.
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Bakenecker AC, Ahlborg M, Debbeler C, Kaethner C, Buzug TM, Lüdtke-Buzug K. Magnetic particle imaging in vascular medicine. Innov Surg Sci 2018; 3:179-192. [PMID: 31579782 PMCID: PMC6604583 DOI: 10.1515/iss-2018-2026] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 09/14/2018] [Indexed: 01/31/2023] Open
Abstract
Magnetic particle imaging (MPI) is a new medical imaging technique that enables three-dimensional real-time imaging of a magnetic tracer material. Although it is not yet in clinical use, it is highly promising, especially for vascular and interventional imaging. The advantages of MPI are that no ionizing radiation is necessary, its high sensitivity enables the detection of very small amounts of the tracer material, and its high temporal resolution enables real-time imaging, which makes MPI suitable as an interventional imaging technique. As MPI is a tracer-based imaging technique, functional imaging is possible by attaching specific molecules to the tracer material. In the first part of this article, the basic principle of MPI will be explained and a short overview of the principles of the generation and spatial encoding of the tracer signal will be given. After this, the used tracer materials as well as their behavior in MPI will be introduced. A subsequent presentation of selected scanner topologies will show the current state of research and the limitations researchers are facing on the way from preclinical toward human-sized scanners. Furthermore, it will be briefly shown how to reconstruct an image from the tracer materials' signal. In the last part, a variety of possible future clinical applications will be presented with an emphasis on vascular imaging, such as the use of MPI during cardiovascular interventions by visualizing the instruments. Investigations will be discussed, which show the feasibility to quantify the degree of stenosis and diagnose strokes and traumatic brain injuries as well as cerebral or gastrointestinal bleeding with MPI. As MPI is not only suitable for vascular medicine but also offers a broad range of other possible applications, a selection of those will be briefly presented at the end of the article.
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Affiliation(s)
- Anna C. Bakenecker
- Institute of Medical Engineering, University of Luebeck, Luebeck, Germany
| | - Mandy Ahlborg
- Institute of Medical Engineering, University of Luebeck, Luebeck, Germany
| | - Christina Debbeler
- Institute of Medical Engineering, University of Luebeck, Luebeck, Germany
| | - Christian Kaethner
- Institute of Medical Engineering, University of Luebeck, Luebeck, Germany
| | - Thorsten M. Buzug
- Institute of Medical Engineering, University of Luebeck, Luebeck, Germany
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Straub M, Schulz V. Joint Reconstruction of Tracer Distribution and Background in Magnetic Particle Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1192-1203. [PMID: 29727282 DOI: 10.1109/tmi.2017.2777878] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Magnetic particle imaging (MPI) is a novel tomographic imaging technique, which visualizes the distribution of a magnetic nanoparticle-based tracer material. However, reconstructed MPI images often suffer from an insufficiently compensated image background caused by rapid non-deterministic changes in the background signal of the imaging device. In particular, the signal-to-background ratio (SBR) of the images is reduced for lower tracer concentrations or longer acquisitions. The state-of-the-art procedure in MPI is to frequently measure the background signal during the sample measurement. Unfortunately, this requires a removal of the entire object from the scanner's field of view (FOV), which introduces dead time and repositioning artifacts. To overcome these considerable restrictions, we propose a novel method that uses two consecutive image acquisitions as input parameters for a simultaneous reconstruction of the tracer distribution, as well as the background signal. The two acquisitions differ by just a small spatial shift, while keeping the object always within the focus of a slightly reduced FOV. A linearly interpolated background between the initial and final background measurement is used to seed the iterative reconstruction. The method has been tested with simulations and phantom measurements. Overall, a substantial reduction of the image background was observed, and the image SBR is increased by a factor of 2(7) for the measurement (simulation) data.
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Graeser M, von Gladiss A, Weber M, Buzug TM. Two dimensional magnetic particle spectrometry. Phys Med Biol 2017; 62:3378-3391. [DOI: 10.1088/1361-6560/aa5bcd] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Knopp T, Conolly SM, Buzug TM. Recent progress in magnetic particle imaging: from hardware to preclinical applications. Phys Med Biol 2017; 62:E4-E7. [DOI: 10.1088/1361-6560/aa62c7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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