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Bevacqua MT, Gaffoglio R, Bellizzi GG, Righero M, Giordanengo G, Crocco L, Vecchi G, Isernia T. Field and Temperature Shaping for Microwave Hyperthermia: Recent Treatment Planning Tools to Enhance SAR-Based Procedures. Cancers (Basel) 2023; 15:cancers15051560. [PMID: 36900351 PMCID: PMC10000666 DOI: 10.3390/cancers15051560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/13/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
The aim of the article is to provide a summary of the work carried out in the framework of a research project funded by the Italian Ministry of Research. The main goal of the activity was to introduce multiple tools for reliable, affordable, and high-performance microwave hyperthermia for cancer therapy. The proposed methodologies and approaches target microwave diagnostics, accurate in vivo electromagnetic parameters estimation, and improvement in treatment planning using a single device. This article provides an overview of the proposed and tested techniques and shows their complementarity and interconnection. To highlight the approach, we also present a novel combination of specific absorption rate optimization via convex programming with a temperature-based refinement method implemented to mitigate the effect of thermal boundary conditions on the final temperature map. To this purpose, numerical tests were carried out for both simple and anatomically detailed 3D scenarios for the head and neck region. These preliminary results show the potential of the combined technique and improvements in the temperature coverage of the tumor target with respect to the case wherein no refinement is adopted.
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Affiliation(s)
- Martina T. Bevacqua
- Department of Information Engineering, Infrastructures and Sustainable Energy, Università Mediterranea di Reggio Calabria, Via Graziella, 89124 Reggio di Calabria, Italy
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Consorzio Nazionale Interuniversitario per le Telecomunicazioni, Viale G.P. Usberti, 181/A Pal.3, 43124 Parma, Italy
| | - Rossella Gaffoglio
- Advanced Computing, Photonics & Electromagnetics (CPE), Fondazione LINKS, 10138 Turin, Italy
| | - Gennaro G. Bellizzi
- Department of Information Engineering, Infrastructures and Sustainable Energy, Università Mediterranea di Reggio Calabria, Via Graziella, 89124 Reggio di Calabria, Italy
- Correspondence: (G.G.B.); (T.I.)
| | - Marco Righero
- Advanced Computing, Photonics & Electromagnetics (CPE), Fondazione LINKS, 10138 Turin, Italy
| | - Giorgio Giordanengo
- Advanced Computing, Photonics & Electromagnetics (CPE), Fondazione LINKS, 10138 Turin, Italy
| | - Lorenzo Crocco
- National Research Council of Italy (CNR), Istituto per il Rilevamento Elettromagnetico dell’ Ambiente, CNR-IREA, Via Diocleziano 308, 80100 Napoli, Italy
| | - Giuseppe Vecchi
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
| | - Tommaso Isernia
- Department of Information Engineering, Infrastructures and Sustainable Energy, Università Mediterranea di Reggio Calabria, Via Graziella, 89124 Reggio di Calabria, Italy
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Consorzio Nazionale Interuniversitario per le Telecomunicazioni, Viale G.P. Usberti, 181/A Pal.3, 43124 Parma, Italy
- National Research Council of Italy (CNR), Istituto per il Rilevamento Elettromagnetico dell’ Ambiente, CNR-IREA, Via Diocleziano 308, 80100 Napoli, Italy
- Correspondence: (G.G.B.); (T.I.)
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Leijsen R, van den Berg C, Webb A, Remis R, Mandija S. Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography. NMR IN BIOMEDICINE 2022; 35:e4211. [PMID: 31840897 PMCID: PMC9285035 DOI: 10.1002/nbm.4211] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 10/09/2019] [Accepted: 10/09/2019] [Indexed: 05/28/2023]
Abstract
Magnetic resonance electrical properties tomography (MR-EPT) is a technique used to estimate the conductivity and permittivity of tissues from MR measurements of the transmit magnetic field. Different reconstruction methods are available; however, all these methods present several limitations, which hamper the clinical applicability. Standard Helmholtz-based MR-EPT methods are severely affected by noise. Iterative reconstruction methods such as contrast source inversion electrical properties tomography (CSI-EPT) are typically time-consuming and are dependent on their initialization. Deep learning (DL) based methods require a large amount of training data before sufficient generalization can be achieved. Here, we investigate the benefits achievable using a hybrid approach, that is, using MR-EPT or DL-EPT as initialization guesses for standard 3D CSI-EPT. Using realistic electromagnetic simulations at 3 and 7 T, the accuracy and precision of hybrid CSI reconstructions are compared with those of standard 3D CSI-EPT reconstructions. Our results indicate that a hybrid method consisting of an initial DL-EPT reconstruction followed by a 3D CSI-EPT reconstruction would be beneficial. DL-EPT combined with standard 3D CSI-EPT exploits the power of data-driven DL-based EPT reconstructions, while the subsequent CSI-EPT facilitates a better generalization by providing data consistency.
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Affiliation(s)
- Reijer Leijsen
- Department of Radiology, C.J. Gorter Center for High Field MRILeiden University Medical CenterLeidenThe Netherlands
| | - Cornelis van den Berg
- Department of Radiotherapy, Division of Imaging & OncologyUniversity Medical Center UtrechtUtrechtThe Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image SciencesUtrecht UniversityUtrechtThe Netherlands
| | - Andrew Webb
- Department of Radiology, C.J. Gorter Center for High Field MRILeiden University Medical CenterLeidenThe Netherlands
| | - Rob Remis
- Circuits and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, Division of Imaging & OncologyUniversity Medical Center UtrechtUtrechtThe Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image SciencesUtrecht UniversityUtrechtThe Netherlands
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Stijnman PRS, Stefano Mandija, Fuchs PS, van den Berg CAT, Remis RF. Transceive phase corrected 2D contrast source inversion-electrical properties tomography. Magn Reson Med 2021; 85:2856-2868. [PMID: 33280166 PMCID: PMC7898605 DOI: 10.1002/mrm.28619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/08/2020] [Accepted: 11/05/2020] [Indexed: 11/11/2022]
Abstract
PURPOSE To remove the necessity of the tranceive phase assumption for CSI-EPT and show electrical properties maps reconstructed from measured data obtained using a standard 3T birdcage body coil setup. METHODS The existing CSI-EPT algorithm is reformulated to use the transceive phase rather than relying on the transceive phase assumption. Furthermore, the radio frequency (RF)-shield is numerically implemented to accurately model the RF fields inside the MRI scanner. We verify that the reformulated two-dimensional (2D) CSI-EPT algorithm can reconstruct electrical properties maps given 2D electromagnetic simulations. Afterward, the algorithm is tested with three-dimensional (3D) FDTD simulations to investigate if the 2D CSI-EPT can retrieve the electrical properties for 3D RF fields. Finally, an MR experiment at 3T with a phantom is performed. RESULTS From the results of the 2D simulations, it is seen that CSI-EPT can reconstruct the electrical properties using MRI accessible quantities. For 3D simulations, it is observed that the electrical properties are underestimated, nonetheless, CSI-EPT has a lower standard deviation than the standard Helmholtz based methods. Finally, the first CSI-EPT reconstructions based on measured data are presented showing comparable accuracy and precision to reconstructions based on simulated data, and demonstrating the feasibility of CSI-EPT. CONCLUSIONS The CSI-EPT algorithm was rewritten to use MRI accessible quantities. This allows for CSI-EPT to fully exploit the benefits of the higher static magnetic field strengths with a standard quadrature birdcage coil setup.
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Affiliation(s)
- Peter R. S. Stijnman
- Computational Imaging Group for MRI Diagnostics and TherapyCentre for Image Sciences UMC UtrechtUtrechtThe Netherlands
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | - Stefano Mandija
- Computational Imaging Group for MRI Diagnostics and TherapyCentre for Image Sciences UMC UtrechtUtrechtThe Netherlands
| | - Patrick S. Fuchs
- Circuit & Systems Group of the Electrical EngineeringDelft University of TechnologyDelftThe Netherlands
| | - Cornelis A. T. van den Berg
- Computational Imaging Group for MRI Diagnostics and TherapyCentre for Image Sciences UMC UtrechtUtrechtThe Netherlands
| | - Rob F. Remis
- Circuit & Systems Group of the Electrical EngineeringDelft University of TechnologyDelftThe Netherlands
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