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He J, Li Y, Zhang P, Hui H, Tian J. A fused LASSO operator for fast 3D magnetic particle imaging reconstruction. Phys Med Biol 2024; 69:135002. [PMID: 38815602 DOI: 10.1088/1361-6560/ad524b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 05/30/2024] [Indexed: 06/01/2024]
Abstract
Objective.Magnetic particle imaging (MPI) is a promising imaging modality that leverages the nonlinear magnetization behavior of superparamagnetic iron oxide nanoparticles to determine their concentration distribution. Previous optimization models with multiple regularization terms have been proposed to achieve high-quality MPI reconstruction, but these models often result in increased computational burden, particularly for dense gridding 3D fields of view. In order to achieve faster reconstruction speeds without compromising reconstruction quality, we have developed a novel fused LASSO operator, total sum-difference (TSD), which effectively captures the sparse and smooth priors of MPI images.Methods.Through an analysis-synthesis equivalence strategy and a constraint smoothing strategy, the TSD regularized model was solved using the fast iterative soft-thresholding algorithm (FISTA). The resulting reconstruction method, TSD-FISTA, boasts low computational complexity and quadratic convergence rate over iterations.Results.Experimental results demonstrated that TSD-FISTA required only 10% and 37% of the time to achieve comparable or superior reconstruction quality compared to commonly used fused LASSO-based alternating direction method of multipliers and Tikhonov-based algebraic reconstruction techniques, respectively.Significance.TSD-FISTA shows promise for enabling real-time 3D MPI reconstruction at high frame rates for large fields of view.
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Affiliation(s)
- Jie He
- School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing 100191, People's Republic of China
| | - Yimeng Li
- School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing 100191, People's Republic of China
| | - Peng Zhang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, People's Republic of China
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
- National Key Laboratory of Kidney Diseases, Beijing 100853, People's Republic of China
| | - Jie Tian
- School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing 100191, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- National Key Laboratory of Kidney Diseases, Beijing 100853, People's Republic of China
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Carpenter HJ, Ghayesh MH, Zander AC, Li J, Di Giovanni G, Psaltis PJ. Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction. Tomography 2022; 8:1307-1349. [PMID: 35645394 PMCID: PMC9149962 DOI: 10.3390/tomography8030108] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 11/16/2022] Open
Abstract
Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients' arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.
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Affiliation(s)
- Harry J. Carpenter
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
| | - Mergen H. Ghayesh
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
| | - Anthony C. Zander
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
| | - Jiawen Li
- School of Electrical Electronic Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, The University of Adelaide, Adelaide, SA 5005, Australia
- Institute for Photonics and Advanced Sensing, University of Adelaide, Adelaide, SA 5005, Australia
| | - Giuseppe Di Giovanni
- Vascular Research Centre, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia; (G.D.G.); (P.J.P.)
| | - Peter J. Psaltis
- Vascular Research Centre, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia; (G.D.G.); (P.J.P.)
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia
- Department of Cardiology, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia
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Sun Z, Du J. Suppression of motion artifacts in intravascular photoacoustic image sequences. BIOMEDICAL OPTICS EXPRESS 2021; 12:6909-6927. [PMID: 34858688 PMCID: PMC8606127 DOI: 10.1364/boe.440975] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/10/2021] [Accepted: 10/10/2021] [Indexed: 05/25/2023]
Abstract
Intravascular photoacoustic (IVPA) imaging is an image-based imaging modality for the assessment of atherosclerotic plaques. Successful application of IVPA for in vivo coronary arterial imaging requires one overcomes the challenge of motion artifacts associated with the cardiac cycle. We propose a method for correcting artifacts owing to cardiac motion, which are observed in sequential IVPA images acquired by the continuous pullback of the imaging catheter. This method groups raw photoacoustic signals into subsets corresponding to similar phases in the cardiac cycles. Thereafter, the sequential images are reconstructed, by representing the initial pressure distribution on the vascular cross-sections based on the clustered frames of signals by time reversal. Results of simulation data demonstrate the efficacy of this method in suppressing motion artifacts. Qualitative and quantitative evaluations of the method indicate an enhancement of the image quality. Comparison results reveal that this method is computationally efficient in motion correction compared with the image-based gating.
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Affiliation(s)
- Zheng Sun
- Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, China
- Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, Hebei, China
| | - Jiejie Du
- Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, China
- Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, Hebei, China
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Ludewig P, Graeser M, Forkert ND, Thieben F, Rández-Garbayo J, Rieckhoff J, Lessmann K, Förger F, Szwargulski P, Magnus T, Knopp T. Magnetic particle imaging for assessment of cerebral perfusion and ischemia. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2021; 14:e1757. [PMID: 34617413 DOI: 10.1002/wnan.1757] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 08/30/2021] [Accepted: 09/03/2021] [Indexed: 02/04/2023]
Abstract
Stroke is one of the leading worldwide causes of death and sustained disability. Rapid and accurate assessment of cerebral perfusion is essential to diagnose and successfully treat stroke patients. Magnetic particle imaging (MPI) is a new technology with the potential to overcome some limitations of established imaging modalities. It is an innovative and radiation-free imaging technique with high sensitivity, specificity, and superior temporal resolution. MPI enables imaging and diagnosis of stroke and other neurological pathologies such as hemorrhage, tumors, and inflammatory processes. MPI scanners also offer the potential for targeted therapies of these diseases. Due to lower field requirements, MPI scanners can be designed as resistive magnets and employed as mobile devices for bedside imaging. With these advantages, MPI could accelerate and improve the diagnosis and treatment of neurological disorders. This review provides a basic introduction to MPI, discusses its current use for stroke imaging, and addresses future applications, including the potential for clinical implementation. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.
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Affiliation(s)
- Peter Ludewig
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthias Graeser
- Section for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany.,Fraunhofer Research Institute for Individualized and Cell-based Medicine, Lübeck, Germany.,Institute for Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Nils D Forkert
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Florian Thieben
- Section for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany
| | - Javier Rández-Garbayo
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Johanna Rieckhoff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katrin Lessmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fynn Förger
- Section for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany
| | - Patryk Szwargulski
- Section for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany
| | - Tim Magnus
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias Knopp
- Section for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany
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