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On algorithmical methods facilitating clinical translation of magnetic particle imaging
Citation Link: https://doi.org/10.15480/882.16008
Publikationstyp
Doctoral Thesis
Date Issued
2025
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2025-10-13
Institute
TORE-DOI
Citation
Technische Universität Hamburg (2025)
Magnetic Particle Imaging (MPI) is an emerging tomographic imaging modality that has demonstrated significant potential in pre-clinical applications. As MPI moves towards clinical translation, the field faces a variety of interdisciplinary challenges that must be addressed through advancements in hardware, algorithmic methodologies, and application-driven refinements. This dissertation explores algorithmic methods that facilitate the transition of MPI from a research-oriented technology to a clinically viable imaging tool.
The work presented in this dissertation highlights the crucial interplay between applicational demands, hardware design and limitations, and methodological development. Hardware innovations define the fundamental capabilities of MPI, such as sensitivity, resolution, and acquisition speed. However, these capabilities alone are insufficient to meet the stringent requirements of clinical imaging. Algorithmic research plays a pivotal role in bridging this gap by optimizing imaging performance, improving reconstruction accuracy, and addressing practical constraints in system usability. From accelerating system calibration to refining signal processing techniques and optimizing field encoding strategies, computational advancements serve as a critical link between hardware constraints and the needs of real-world medical applications. By focusing on the synergies between these three domains, this dissertation demonstrates how methodological improvements can drive the clinical adoption of MPI.
Building on this foundation, the dissertation presents key methodological contributions that address specific challenges in MPI. The research contributions of the author are embedded within this broader framework, showcasing innovations that improve the efficiency and feasibility of MPI for clinical use. The core of the dissertation is composed of three peer-reviewed research articles, each addressing a distinct methodological challenge in MPI.
The first contribution introduces a novel approach to system calibration by leveraging extrapolation techniques for system matrices. Traditional MPI calibration is time-intensive and requires extensive measurements, limiting the scalability of the method for clinical applications. By employing an extrapolation-based methodology, this work significantly reduces the calibration burden while maintaining reconstruction accuracy, thereby improving efficiency and reducing experimental effort.
The second contribution focuses on regularization techniques in MPI reconstruction, presenting a method with automatically tuned parameters. Image reconstruction in MPI requires balancing noise suppression with the preservation of fine structural details, which is particularly crucial for medical imaging applications. This research introduces an adaptive regularization framework that dynamically adjusts parameters based on image characteristics, enhancing robustness, improving reconstruction quality, and reducing the need for manual parameter selection.
The third contribution explores the use of ellipsoidal harmonic expansions to efficiently represent magnetic fields, addressing a fundamental challenge in MPI system modeling. Accurate field representations are essential for precise spatial encoding and the development of tailored field-cameras for MPI scanners. Compared to spherical harmonic representations, ellipsoidal harmonics provide greater geometric flexibility, enabling optimized field encoding strategies that are better suited for specific scanner designs. This study develops a mathematically rigorous yet efficient method for representing complex field distributions, offering improvements in encoding efficiency and adaptability to application-specific requirements.
Additional research contributions contextualize these advancements within collaborative efforts involving multiple research institutions. These collaborations have furthered the development of MPI modeling techniques, investigated the MPI performance of the clinically approved tracer Resotran, and contributed to studies on the development of novel MPI tracers. The findings presented in this dissertation have been disseminated through various peer-reviewed journal articles and conference proceedings, reinforcing the significance of algorithmic research in driving the clinical translation of MPI.
In conclusion, this dissertation demonstrates that algorithmic innovations are fundamental to bridging the gap between pre-clinical research and clinical implementation in MPI. By enhancing calibration processes, improving reconstruction methodologies, and optimizing field representations, these advancements facilitate the practical deployment of MPI in medical imaging. The final chapter provides a summary of the key findings and an outlook on future algorithmic research directions that will further support the integration of MPI into clinical workflows.
The work presented in this dissertation highlights the crucial interplay between applicational demands, hardware design and limitations, and methodological development. Hardware innovations define the fundamental capabilities of MPI, such as sensitivity, resolution, and acquisition speed. However, these capabilities alone are insufficient to meet the stringent requirements of clinical imaging. Algorithmic research plays a pivotal role in bridging this gap by optimizing imaging performance, improving reconstruction accuracy, and addressing practical constraints in system usability. From accelerating system calibration to refining signal processing techniques and optimizing field encoding strategies, computational advancements serve as a critical link between hardware constraints and the needs of real-world medical applications. By focusing on the synergies between these three domains, this dissertation demonstrates how methodological improvements can drive the clinical adoption of MPI.
Building on this foundation, the dissertation presents key methodological contributions that address specific challenges in MPI. The research contributions of the author are embedded within this broader framework, showcasing innovations that improve the efficiency and feasibility of MPI for clinical use. The core of the dissertation is composed of three peer-reviewed research articles, each addressing a distinct methodological challenge in MPI.
The first contribution introduces a novel approach to system calibration by leveraging extrapolation techniques for system matrices. Traditional MPI calibration is time-intensive and requires extensive measurements, limiting the scalability of the method for clinical applications. By employing an extrapolation-based methodology, this work significantly reduces the calibration burden while maintaining reconstruction accuracy, thereby improving efficiency and reducing experimental effort.
The second contribution focuses on regularization techniques in MPI reconstruction, presenting a method with automatically tuned parameters. Image reconstruction in MPI requires balancing noise suppression with the preservation of fine structural details, which is particularly crucial for medical imaging applications. This research introduces an adaptive regularization framework that dynamically adjusts parameters based on image characteristics, enhancing robustness, improving reconstruction quality, and reducing the need for manual parameter selection.
The third contribution explores the use of ellipsoidal harmonic expansions to efficiently represent magnetic fields, addressing a fundamental challenge in MPI system modeling. Accurate field representations are essential for precise spatial encoding and the development of tailored field-cameras for MPI scanners. Compared to spherical harmonic representations, ellipsoidal harmonics provide greater geometric flexibility, enabling optimized field encoding strategies that are better suited for specific scanner designs. This study develops a mathematically rigorous yet efficient method for representing complex field distributions, offering improvements in encoding efficiency and adaptability to application-specific requirements.
Additional research contributions contextualize these advancements within collaborative efforts involving multiple research institutions. These collaborations have furthered the development of MPI modeling techniques, investigated the MPI performance of the clinically approved tracer Resotran, and contributed to studies on the development of novel MPI tracers. The findings presented in this dissertation have been disseminated through various peer-reviewed journal articles and conference proceedings, reinforcing the significance of algorithmic research in driving the clinical translation of MPI.
In conclusion, this dissertation demonstrates that algorithmic innovations are fundamental to bridging the gap between pre-clinical research and clinical implementation in MPI. By enhancing calibration processes, improving reconstruction methodologies, and optimizing field representations, these advancements facilitate the practical deployment of MPI in medical imaging. The final chapter provides a summary of the key findings and an outlook on future algorithmic research directions that will further support the integration of MPI into clinical workflows.
Subjects
Magnetic Particle Imaging
MPI Reconstruction
MPI Calibration
Regularization
Magnetic Fields
Ellipsoidal Harmonic Expansion
DDC Class
616.07: Pathology
006.3: Artificial Intelligence
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Konrad_Scheffler_On Algorithmical Methods Facilitating Clinical Translation of Magnetic Particle Imaging.pdf
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