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- Frankel, Jennifer, 1981-
(författare)
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Characterization of the MRI patient exposure environment and exposure assessment methods for magnetic fields in MRI scanners
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Magnetic resonance imaging (MRI) has become one of the most common imaging modalities available in modern medicine, and it is an indispensable diagnostic tool thanks to the unparalleled soft-tissue contrast and high image resolution. It is also a unique exposure environment consisting of a complex mix of magnetic fields. During an MRI scan, the patient is simultaneously exposed to a strong static magnetic field, a fast-switching gradient magnetic field, and a pulsed radiofrequency (RF) magnetic field. Transient acute effects, such as nerve excitation and tissue heating, are well known and limited by universal safety guidelines. Long-term health effects related to MRI exposure have, however, not been scientifically established, and no interaction mechanisms have been verified, despite a growing body of research on electromagnetic field exposure. Further epidemiological and experimental research on MRI exposure has been recommended but the lack of a common definition of dose or exposure metric makes evaluation of past research and the design of future experiments difficult.The objectives of this thesis were to characterize the MRI patient exposure environment in terms of the magnetic fields involved, suggest relevant exposure metrics, and introduce exposure assessment methods suitable for epidemiological and experimental research on MRI and long-term health effects.In Paper I, we discussed the MRI exposure environment and its complexity and gave an overview of the current scientific situation. In Paper II, we investigated the exposure variability between different MRI sequences and suggested patient-independent exposure metrics that describe different characteristics of the magnetic field exposure, including mean, peak, and threshold values. In Paper III, we presented three exposure assessment methods, specifically suited to the complex MRI exposure environment: a measurement-based method, a calculation-based method, and a proxy method.Papers I and II showed that MRI exams are not homogenous in terms of exposure, and exposure variability exists between the individual sequences that comprise an exam. Differences in mean exposure between sequences were several-fold, peak exposure differences up to 30-fold, and differences in threshold exposure were in some cases more than 100-fold. Furthermore, within-sequence exposure variability, related to the parameter adjustments that can be made at the scanner console before the start of a scan, gave rise to 5-to-8-fold exposure increases. Paper III showed that magnetic field models could be used to approximate the exposure at arbitrary locations inside the scanner, with slight underestimation of gradient field metrics and large variability in some RF field metrics. With improvements in accuracy and efficiency, the method could become a useful exposure assessment tool for in vitro and in vivo research as well as clinical work on medical implant safety. Our search for suitable exposure metric proxies resulted in a limited selection with low correlation between proxies and their counterpart metrics, but, with further development, the proxy method has the potential to allow for much needed exposure classification relevant to large-scale epidemiological research.The work in this thesis has contributed to increased awareness of the unique MRI exposure environment, the characteristics of the magnetic fields involved, and the inherent exposure variability in MRI exams. The metrics and methods presented are specifically suited to exposure assessment of the unique MRI environment, and may contribute to improved research quality by allowing for meaningful comparisons between study results and for experimental conditions to be easily replicated in future studies.
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| 2. |
- Simkó, Attila, 1995-
(författare)
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Contributions to deep learning for imaging in radiotherapy
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Purpose: The increasing importance of medical imaging in cancer treatment, combined with the growing popularity of deep learning gave relevance to the presented contributions to deep learning solutions with applications in medical imaging.Relevance: The projects aim to improve the efficiency of MRI for automated tasks related to radiotherapy, building on recent advancements in the field of deep learning.Approach: Our implementations are built on recently developed deep learning methodologies, while introducing novel approaches in the main aspects of deep learning, with regards to physics-informed augmentations and network architectures, and implicit loss functions. To make future comparisons easier, we often evaluated our methods on public datasets, and made all solutions publicly available.Results: The results of the collected projects include the development of robust models for MRI bias field correction, artefact removal, contrast transfer and sCT generation. Furthermore, the projects stress the importance of reproducibility in deep learning research and offer guidelines for creating transparent and usable code repositories.Conclusions: Our results collectively build the position of deep learning in the field of medical imaging. The projects offer solutions that are both novel and aim to be highly applicable, while emphasizing generalization towards a wide variety of data and the transparency of the results.
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| 3. |
- Brynolfsson, Patrik, 1981-
(författare)
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Applications of statistical methods in quantitative magnetic resonance imaging
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Magnetic resonance imaging, MRI, offers a vast range of imaging methods that can be employed in the characterization of tumors. MRI is generally used in a qualitative way, where radiologists interpret the images for e.g. diagnosis, follow ups, or assessment of treatment response. In the past decade, there has been an increasing interest for quantitative imaging, which give repeatable measurements of the anatomy. Quantitative imaging allows for objective analysis of the images, which are grounded in physical properties of the underlying tissues. The aim of this thesis was to improve quantitative measurements of Dynamic contrast enhanced MRI (DCE-MRI), and the texture analysis of diffusion weighted MRI (DW-MRI).DCE-MRI measures perfusion, which is the delivery of blood, oxygen and nutrients to the tissues. The exam involves continuously imaging the region of interest, e.g. a tumor, while injecting a contrast agent (CA) in the blood stream. By analyzing how fast and how much CA leaks out into the tissues, the cell density and the permeability of the capillaries can be estimated. Tumors often have an irregular and broken vasculature, and DCE-MRI can aid in tumor grading or treatment assessment. One step is crucial when performing DCE-MRI analysis, the quantification of CA in the tissue. The CA concentration is difficult to measure accurately due to uncertainties in the imaging, properties of the CA, and physiology of the patient. Paper I, the possibility of using two aspects of the MRI data, phase and magnitude, for improved CA quantification, is explored. We found that the combination of phase and magnitude information improved the CA quantification in regions with high CA concentration, and was more advantageous for high field strength scanners.DW-MRI measures the diffusion of water in and between cells, which reflects the cell density and structure of the tissue. The structure of a tumor can give insights into the prognosis of the disease. Tumors are heterogeneous, both genetically and in the distribution of cells, and tumors with high intratumoral heterogeneity have poorer prognosis. This heterogeneity can be measured using texture analysis. In 1973, Haralick et al. presented a texture analysis method using a gray level co-occurrence matrix, GLCM, to gauge the spatial distribution of gray levels in the image. This method of assessing texture in images has been successfully applied in many areas of research, from satellite images to medical applications. Texture analysis in treatment outcome assessment is studied in Paper II, where we showed that texture can distinguish between groups of patients with different survival times, in images acquired prior to treatment start.However, this type of texture analysis is not inherently quantitative in the way it is calculated today. This was studied in Paper III, where we investigated how texture features were affected by five parameters related to image acquisition and pre-processing. We found that the texture feature values were dependent on the choice of these imaging and preprocessing parameters. In Paper IV, a novel method for calculating Haralick texture features was presented, which makes the texture features asymptotically invariant to the size of the GLCM. This method allows for comparison of textures between images that have been analyzed in different ways.In conclusion, the work in this thesis has been aimed at improving quantitative analysis of tumors using MRI and texture analysis.
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